Release Notes
Listing of all the techy updates
1. Job Description Optimizer Oracle Agent | Fields-Wise Update
Overview:
The Job Description Optimizer Agent has been enhanced to update job descriptions more comprehensively across Oracle. Previously, updates were limited to the External tab; now, the agent updates both External and Internal tabs and ensures heading updates.
Key Highlights:
- Expanded Field Updates: Fields such as Job Type and Education Required are now updated under Job Details in Oracle.
- Heading Updates: Job description headings are updated for better clarity and structure.
- Oracle Integration: Fields generated in the JD but not present in Oracle will not be displayed, maintaining system consistency.
Benefits:
- Ensures more complete and accurate job descriptions in Oracle.
- Improves consistency across External and Internal views.
- Enhances data quality and visibility for recruiters.
2. Skill Gap Analysis | Oracle Agent – New Matching Agent API
Overview:
The Skill Gap Analysis functionality in Oracle has been updated to use the Matching Agent API instead of the previous Job Application Analyzer API. This change standardizes responses and leverages the enhanced capabilities of the Matching Agent API.
Key Highlights:
- API Update: Skill Gap Analysis is now served via the Matching Agent API.
- Consistent Responses: Responses are now structured according to the Matching Agent API specifications.
- Improved Integration: Ensures seamless alignment with other matching and candidate evaluation processes in Oracle.
Benefits:
- Provides standardized and more consistent skill gap insights.
- Enhances the reliability of skill-based candidate evaluation.
- Improves overall efficiency in candidate assessment workflows.
HR Meeting Intelligence Agent by RChilli
Overview:
The HR Meeting Intelligence Agent by RChilli has been enhanced to automatically analyze HR-candidate interview transcripts and generate structured reports. This improvement provides recruitment teams with actionable insights, improving hiring efficiency and decision-making.
Key Highlights:
- Meeting Overview: Captures meeting type, participants, role/topic, objectives, and duration.
- Executive Summary: Summarizes main discussion topics, key outcomes, and overall tone.
- Key Discussion Highlights: Provides detailed candidate assessment, project examples, tools/practices used, prioritization approach, queries addressed, and HR responses.
- Action Items: Lists next steps with owners, priority, and timelines for follow-up.
- Additional Sections: Includes HR/recruiter performance feedback, DISC communication analysis, risk or compliance notes, recommendations for improvement, and overall assessment.
Benefits:
- Provides a comprehensive, structured view of HR interviews without manual summarization.
- Enables objective and data-driven candidate evaluation.
- Supports faster decision-making and alignment on next steps
Oracle Browser Assistant | Job Application Matching Scores
Overview:
The Oracle Browser Assistant now displays matching scores for candidates directly on the job application page. After entering a Job ID, recruiters can view all candidates who have applied for that job along with their individual matching scores.
Key Highlights:
- Job ID Lookup: Enter a job ID to retrieve all candidates for the role.
- Candidate Matching Scores: Each candidate is displayed with a score reflecting their relevance for the job.
- Quick Assessment: Enables recruiters to quickly identify top candidates without manual evaluation.
- Integrated View: Combines candidate details and scoring in a single, easy-to-read interface.
Benefits:
- Speeds up candidate shortlisting.
- Supports data-driven hiring decisions.
- Enhances recruiter efficiency by providing instant insights into candidate-job fit.
1. Job Description Optimizer Oracle Agent | Fields-Wise Update
Overview:
The Job Description Optimizer Agent has been enhanced to update job descriptions more comprehensively across Oracle. Previously, updates were limited to the External tab; now, the agent updates both External and Internal tabs and ensures heading updates.
Key Highlights:
- Expanded Field Updates: Fields such as Job Type and Education Required are now updated under Job Details in Oracle.
- Heading Updates: Job description headings are updated for better clarity and structure.
- Oracle Integration: Fields generated in the JD but not present in Oracle will not be displayed, maintaining system consistency.
Benefits:
- Ensures more complete and accurate job descriptions in Oracle.
- Improves consistency across External and Internal views.
- Enhances data quality and visibility for recruiters.
2. Skill Gap Analysis | Oracle Agent – New Matching Agent API
Overview:
The Skill Gap Analysis functionality in Oracle has been updated to use the Matching Agent API instead of the previous Job Application Analyzer API. This change standardizes responses and leverages the enhanced capabilities of the Matching Agent API.
Key Highlights:
- API Update: Skill Gap Analysis is now served via the Matching Agent API.
- Consistent Responses: Responses are now structured according to the Matching Agent API specifications.
- Improved Integration: Ensures seamless alignment with other matching and candidate evaluation processes in Oracle.
Benefits:
- Provides standardized and more consistent skill gap insights.
- Enhances the reliability of skill-based candidate evaluation.
- Improves overall efficiency in candidate assessment workflows.
MultiLingual API | Extended Language Support
Overview:The MultiLingual API now supports all non-English languages in the taxonomy for semantic skill and job profile matching. This functionality has been integrated with the Taxonomy to improve results while maintaining existing support for Arabic, skill, and job profile entities, ensuring consistent and accurate responses across languages.
Enhanced Country Identification for Multilingual Addresses
Overview:
The Resume Parser has been enhanced to improve country detection in multilingual addresses. The parser now uses city and state mapping as primary signals, ensuring accurate and consistent identification of country, city, and state information across resumes.
Key Highlights:
- Country selection is guided by geographic signals such as city and state.
- Supports accurate parsing of multilingual addresses, including Spanish (es).
- Ensures precise mapping of city, state, and country in the parsed output.
Benefits:
- Improves the accuracy and consistency of location data.
- Enhances the reliability of parsed resumes for matching and analytics.
- Supports better downstream processing in recruitment workflows.
HR Meeting Intelligence Agent by RChilli
Overview:
The HR Meeting Intelligence Agent by RChilli has been enhanced to automatically analyze HR-candidate interview transcripts and generate structured reports. This improvement provides recruitment teams with actionable insights, improving hiring efficiency and decision-making.
Key Highlights:
- Meeting Overview: Captures meeting type, participants, role/topic, objectives, and duration.
- Executive Summary: Summarizes main discussion topics, key outcomes, and overall tone.
- Key Discussion Highlights: Provides detailed candidate assessment, project examples, tools/practices used, prioritization approach, queries addressed, and HR responses.
- Action Items: Lists next steps with owners, priority, and timelines for follow-up.
- Additional Sections: Includes HR/recruiter performance feedback, DISC communication analysis, risk or compliance notes, recommendations for improvement, and overall assessment.
Benefits:
- Provides a comprehensive, structured view of HR interviews without manual summarization.
- Enables objective and data-driven candidate evaluation.
- Supports faster decision-making and alignment on next steps
Resume Skill Matching Enhancement
The Search & Match API has been enhanced to improve skill matching by enriching the Resume SkillAlias field during resume indexing. The system now adds JobProfile-related skills from the Taxonomy API and related skill group information into SkillAlias.
This enhancement helps the API match JD skills not only with skills directly mentioned in the resume, but also with skills related to the candidate’s JobProfile. It improves JD-to-Resume skill matching, JobProfile-based indirect skill matching, skill group matching, candidate recall, and overall match relevance.
The enhancement is supported in both v3.0 and v4.0 resume indexing flows.
Geographical Search Enhancement
The Geographical Search feature has been enhanced to improve location-based matching by expanding the set of GeoLocation entities used in the matching process. In addition to AddressGeoLocation and CurrentExperienceGeoLocation, the API now also considers CurrentGeoLocation, PreferredGeoLocation, ExperienceGeoLocation, and EducationGeoLocation.
This enhancement provides a more complete view of candidate location data, improves matching accuracy and relevance, and supports better candidate-job alignment through stronger geographical intelligence. The update is designed to remain compatible with existing GeoLocation matching behavior.
Address Extraction Improvement
Overview:
The Resume Parser has been enhanced to improve the extraction of address information from resumes. Address extraction is now focused on relevant fields, ensuring that location data is accurately captured from dedicated address sections.
Key Highlights:
- Extracts addresses from proper address fields only.
- Ignore unrelated sections such as summary, bio, or conversational text blocks.
- Provides more precise and structured location data for resumes.
Benefits:
- Improved accuracy of candidate location information.
- Enhanced quality of parsed resume data for matching and analytics.
Default User Settings for Version 3.2
Overview:
The JD Parser API now automatically applies default user settings for requests with version 3.2, ensuring consistent and predictable parsing behavior regardless of user-provided configurations.
Key Highlights:
- Version-Based Default Settings: For requests with "version": "3.2", the following settings are enforced:
- experienceWithSkill
- pickSubjectWithoutDegree
- parseMultipleSubjectDegree
- jdpartialdegree
Latest Updates
Role-Wise Experience in Additional Match Types
The Role-Wise Experience feature has been extended to additional match types in the Search & Match API to improve matching accuracy and consistency. In addition to JD to Resume, the feature is now supported in Resume to Resume and Resume to JD matching.
This enhancement evaluates experience specific to the relevant job profile instead of using the candidate’s total overall experience, resulting in more precise and role-relevant match scores. It also ensures compatibility with existing configurations in v3.0 and v4.0.
In Search & Match v4.0, this feature is enabled by default, and the roleWiseExperience parameter is not required in the API request.
Latest Updates
Extended Update Functionality in UpdateIndex API
The Search and Match UpdateIndex API has been enhanced to support extended insert and remove operations for both Resume and Job Description (JD) documents. This update enables partial document updates without requiring full re-indexing.
Key Highlights:
- Insert Support: Add new entity values to existing indexed Resume and JD documents using insertEntityValue.
- Remove Support: Remove specific fields, array elements, or complete sections using removeEntityValue.
- Resume Coverage: Supports removal of individual fields, array values, and complete sections such as PersonalDetails, SalaryDetails, and LocationDetails.
- JD Coverage: Supports insertion and removal of job-related fields, grouped sections such as JobDetails and SalaryDetails, and specific values from arrays such as Languages and Domains.
- Flexible Partial Updates: Improves efficiency and data management by eliminating the need to re-index complete documents for small changes.
Benefits:
- Faster and more efficient document updates
- Greater flexibility in managing dynamic indexed data
- Reduced overhead compared to full re-indexing
Latest Updates
Employer JobPeriod Parsing ImprovementThe parser has been enhanced to improve JobPeriod extraction under the Employer category when date ranges are written in different formats or orders. This update ensures more accurate parsing of employment duration values such as 01/01/2024-05/09/2026, Dec 2019 :- Aug 2020, Aug 2020 == Aug 2024
This enhancement improves the accuracy and consistency of employer experience data extracted from resumes.
Latest Updates
Improvement in Name Extraction Accuracy
The Name category has been enhanced to prevent city and state values from being incorrectly extracted as part of a person’s name. This improvement increases the accuracy of name parsing and ensures cleaner separation between personal and location data.
Latest Updates
1. Master Agent
A new Master Agent has been introduced to unify the capabilities of the Candidate Prescreening Agent, Job Application Analyzer, Interview Question Generator, and Job Description Optimizer into a single intelligent system. This enhancement enables end-to-end HR and recruitment automation, improving efficiency, scalability, maintainability, and overall operational performance.
2. Bias Detection Agent
A new Bias Detection Agent has been introduced to identify hidden bias, discriminatory language, and unfair screening criteria in job descriptions and resumes. This agent supports bias detection, compliance analysis, and fairness validation, helping organizations promote equitable hiring practices and align with diversity and inclusion standards.
3. Candidate Data Normalization Agent
The Candidate Data Normalization Agent has been introduced to standardize candidate information across education, skills, certifications, languages, and locations. It improves data quality through normalization, deduplication, and taxonomy alignment, enabling more consistent ATS integration, better matching, and more reliable analytics.
4. Learning Path Recommendation Agent
A new Learning Path Recommendation Agent has been introduced to identify skill gaps and generate personalized upskilling recommendations. By analyzing candidate resumes, the agent creates role-aligned learning pathways that help support targeted development and more effective workforce planning.
Latest Updates
1. Oracle Agent Authentication – OAuth ImplementationAuthentication has been enhanced by implementing OAuth-based authentication for all Oracle agents. This ensures secure and standardized access when agents interact with Oracle services.
Agents Covered
- Candidate Prescreening Agent
- Interview Question Generator
- Job Description Optimizer
- Job Application Analyzer
Key Features
- Implemented OAuth authentication across all Oracle agents.
- Enables secure token-based authentication.
- Provides a consistent authentication mechanism for all agent services.
Benefits
- Improves security and access control.
- Ensures secure communication between agents and Oracle services.
- Standardizes authentication across all agent integrations.
2. Oracle Agent Workflow Automation
Two automated Agent Workflows have been implemented in Oracle to streamline candidate processing and job application handling. These workflows automatically trigger agents based on system activity.
Workflows Implemented
I. New Candidate Workflow
- Triggered when a new candidate is added in Oracle.
- If the workflow is active, the system automatically updates the candidate profile.
- The workflow runs every 30 minutes and processes newly added candidates.
II. New Job Application Workflow
When a new job application is created and the workflow is active, the following agents run in sequence:
- Profile Augmentation – Talent Data Refresh – Updates the candidate profile.
- Job Application Analyzer – Generates a candidate matching score.
- Interview Question Generator – Generates interview questions for the candidate.
- Candidate Prescreening Agent – Sends a prescreening link to the candidate.
Benefits
- Automates candidate processing and evaluation.
- Reduces manual effort for recruiters.
- Ensures a structured workflow from profile update to prescreening.
III. Candidate Screening Transcript Reporting API | Transcript-Based Report Generation
Added functionality to generate structured reports directly from candidate screening transcripts, enabling easier review, better readability, and improved analysis of interview conversations.
IV. Candidate Pre-Screening Agent | Configuration-Based Enhancements
Enhanced the Candidate Pre-Screening Agent with additional configuration-based controls, allowing more flexible screening workflows and improved adaptability based on specific business requirements.
V. Role-Based Interview Question Builder by RChilli in Agent.ai
RChilli introduces the Role-Based Interview Question Builder to help recruiters and hiring managers create structured, role-specific interview questions faster.
The agent analyzes job descriptions and candidate resumes to generate relevant interview questions tailored to the role, required skills, and candidate profile.
Key Benefits
- Role-based interview questions
- Personalized using JD + resume
- Better alignment with skills and experience
- Consistent interview quality across teams
- Reduced preparation time
VI. Talent Skill Intelligence by RChilli in Agent.ai
A new Talent Skill Intelligence capability has been introduced to analyze candidate resumes and convert unstructured skill data into a clear, structured, and standardized format. This enhancement gives recruitment and talent teams better visibility into candidate skills without manual review.
Key Highlights:
- Skill Extraction and Organization: Identifies and organizes skills from resumes into a structured format.
- Skill Normalization: Standardizes skills for consistent understanding and interpretation across teams.
- Improved Talent Visibility: Provides clearer insight into a candidate’s actual capabilities.
- Reduced Manual Effort: Minimizes the need for manual skill identification and assessment.
Benefits:
- Supports smarter hiring decisions.
- Enables fairer and more consistent candidate evaluation.
- Improves workforce planning with better skill intelligence.
VII. Role Skill Intelligence by RChilli in Agent.ai
A new Role Skill Intelligence capability has been introduced to analyze job titles and job descriptions and identify the skills required for success in each role. This enhancement helps organizations define role requirements more clearly and build a stronger foundation for skill-based hiring.
Key Highlights:
- Role Analysis: Analyzes job titles and role descriptions to identify required skills.
- Skill Categorization: Organizes skills into meaningful groups for better understanding and evaluation.
- Supports Skill-Based Hiring: Helps teams align hiring decisions with actual role requirements.
- Improved Role Clarity: Brings more consistency and clarity to role expectations and hiring criteria.
Benefits: - Strengthens skill-based hiring and evaluation.
- Improves job design and role definition.
- Creates better alignment between roles and candidates.
Latest Updates
Taxonomy – Multi-Lingual Semantic Search Model Implementation
The Taxonomy APIs have been enhanced to support semantic search for non-English keywords (e.g., Arabic) by integrating the Semantic Search model in Skill and JobProfile. This allows better search results using meaning-based matching rather than exact keyword matches.
Key Features
- Semantic Search Integration:
- New optional request parameter: "semanticSearch" (default: true).
- When true, the Semantic Match API is invoked and results are merged into the taxonomy response.
- When false, the API uses existing taxonomy search logic without semantic enrichment.
- Enhanced Non-English Keyword Support:
- Supports languages like Arabic for better search accuracy.
- Handles both single and multi-keyword search scenarios.
Benefits
- Provides improved search results for non-English keywords.
- Enhances the relevance of skill and job profile matching.
- Backward compatible: Existing functionality works when semanticSearch=false.
Latest Updates
Accuracy Improvement: Employer extraction when mentioned with the Department and Branch
Improved the parsing accuracy to correctly identify and extract Employer names when they appear along with Department or Branch information in Resume documents. This enhancement ensures better employer detection and reduces incorrect splitting or misclassification of organization-related details.
Latest Updates
1. Candidate Data Standardizer by RChilli in Agent.ai
A new Candidate Data Standardizer capability has been introduced to help recruiters and HR teams maintain clean, consistent, and structured candidate data across resumes and profiles. This enhancement standardizes key candidate information to ensure every profile follows a common format, regardless of source or resume style.
Key Highlights:
Data Standardization: Normalizes skills, job titles, experience, and education into a consistent structure.Format and Terminology Cleanup:
Reduces variations caused by inconsistent naming, formatting differences, and unstructured resume content.
Improved Search and Comparison: Makes candidate profiles easier to search, compare, and analyze.
Reduced Manual Effort: Minimizes manual data cleanup and lowers the risk of errors in hiring workflows.
Workflow Ready: Supports both high-volume hiring and long-term talent management processes.
Benefits:
-Faster candidate screening.-More accurate talent matching.
-More reliable reporting and analysis.
-Better support for data-driven hiring decisions.
2. Unbiased Hiring Intelligence by RChilli in Agent.ai
Key Highlights:
Bias-Aware Evaluation: Reviews candidate information to identify details that may introduce unconscious bias.
Skill-Focused Assessment: Encourages evaluation based on relevant skills, experience, and role fit.
Consistent Hiring Decisions: Supports more objective and standardized candidate assessment across the hiring process.
Inclusive Hiring Support: Helps organizations strengthen fair hiring practices and diversity initiatives.
Seamless Workflow Fit: Works alongside existing recruitment processes without adding complexity.
Benefits:
-Promotes fairer candidate evaluation.-Reduces the impact of subjective judgment.
-Improves transparency and consistency in hiring.
-Supports inclusive and skill-based recruitment decisions.
Latest Updates
Optimization in Oracle AgentsPerformance and operational optimizations have been implemented across Oracle Agents to improve overall efficiency, system stability, and processing speed. These enhancements ensure smoother agent operations and better performance during high-volume processing.
Agent KPI Tracking and Dashboard
Introduced Agent KPI Tracking and Dashboard to monitor key performance metrics, enabling better visibility into agent performance and operational insights.
ScreenX Video Agent Improvements
Enhanced the ScreenX Video Agent with improvements to video processing, reliability, and overall user experience.
Latest Updates
Oracle Browser Assistant – Job Page Candidate Matching
Enhancement to the Oracle Browser Assistant to help recruiters quickly find suitable candidates for a job. When a recruiter opens a Job page in Oracle, the assistant detects the JobId and automatically displays a list of matching candidates within the assistant interface.
Key Features
- -Detects when the user opens an Oracle Job page.
- -Automatically extracts the JobId from the page.
- -Calls the backend API to fetch matching candidates for that job.
- -Displays the matching candidates list inside the Browser Assistant UI.
- -Supports loading, no results, and error handling states.
- -Works in both Dev and Prod environments based on the assistant configuration.
Benefits
- -Reduces manual effort for recruiters.
- -Helps recruiters quickly identify relevant candidates for a job.
- -Improves recruiter productivity and workflow efficiency.
Latest Updates
Degree Level + Specialization Matching Enhancement in Search and Match API
Enhancement to the Search and Match API to improve degree matching accuracy. The new feature enables matching based on both degree level and specialization, allowing more precise comparison during matching operations.
Key Features
- Introduced DegreeLevelSpecialization feature for improved degree matching.
- Added two new entities:
- degreeLevelSpecialization
- highestDegreeLevelSpecialization
- Enables matching based on degree level (e.g., Bachelor, Master) and specialization (e.g., Computer Science, Mechanical Engineering).
Matching Coverage
The enhancement supports matching across all flows:
- Resume → Job Description
- Job Description → Resume
- Resume → Resume
Benefits
- -Improves degree matching accuracy and scoring.
- -Supports more precise education comparison using level and specialization.
- -Maintains compatibility with existing degree-only matching logic without affecting current workflows.
Latest Updates
I. Oracle Agent Authentication – OAuth ImplementationAuthentication has been enhanced by implementing OAuth-based authentication for all Oracle agents. This ensures secure and standardized access when agents interact with Oracle services.
Agents Covered
- -Candidate Prescreening Agent
- -Interview Question Generator
- -Job Description Optimizer
- -Job Application Analyzer
Key Features
- Implemented OAuth authentication across all Oracle agents.
- Enables secure token-based authentication.
- Provides a consistent authentication mechanism for all agent services.
Benefits
- Improves security and access control.
- Ensures secure communication between agents and Oracle services.
- Standardizes authentication across all agent integrations.
II. Oracle Agent Workflow Automation
Two automated Agent Workflows have been implemented in Oracle to streamline candidate processing and job application handling. These workflows automatically trigger agents based on system activity.
Workflows Implemented
1. New Candidate Workflow
- Triggered when a new candidate is added in Oracle.
- If the workflow is active, the system automatically updates the candidate profile.
- The workflow runs every 30 minutes and processes newly added candidates.
2. New Job Application Workflow
When a new job application is created and the workflow is active, the following agents run in sequence:
- Profile Augmentation – Talent Data Refresh – Updates the candidate profile.
- Job Application Analyzer – Generates a candidate matching score.
- Interview Question Generator – Generates interview questions for the candidate.
- Candidate Prescreening Agent – Sends a prescreening link to the candidate.
Benefits
- Automates candidate processing and evaluation.
- Reduces manual effort for recruiters.
- Ensures a structured workflow from profile update to prescreening.
III. Candidate Screening Transcript Reporting API | Transcript-Based Report Generation
Added functionality to generate structured reports directly from candidate screening transcripts, enabling easier review, better readability, and improved analysis of interview conversations.
IV. Candidate Pre-Screening Agent | Configuration-Based Enhancements
Enhanced the Candidate Pre-Screening Agent with additional configuration-based controls, allowing more flexible screening workflows and improved adaptability based on specific business requirements.
V. Role-Based Interview Question Builder by RChilli in Agent.ai
RChilli introduces the Role-Based Interview Question Builder to help recruiters and hiring managers create structured, role-specific interview questions faster.
The agent analyzes job descriptions and candidate resumes to generate relevant interview questions tailored to the role, required skills, and candidate profile.
Key Benefits
- Role-based interview questions
- Personalized using JD + resume
- Better alignment with skills and experience
- Consistent interview quality across teams
- Reduced preparation time
VI. Talent Skill Intelligence by RChilli in Agent.ai
A new Talent Skill Intelligence capability has been introduced to analyze candidate resumes and convert unstructured skill data into a clear, structured, and standardized format. This enhancement gives recruitment and talent teams better visibility into candidate skills without manual review.
Key Highlights:
- Skill Extraction and Organization: Identifies and organizes skills from resumes into a structured format.
- Skill Normalization: Standardizes skills for consistent understanding and interpretation across teams.
- Improved Talent Visibility: Provides clearer insight into a candidate’s actual capabilities.
- Reduced Manual Effort: Minimizes the need for manual skill identification and assessment.
Benefits:
- Supports smarter hiring decisions.
- Enables fairer and more consistent candidate evaluation.
- Improves workforce planning with better skill intelligence.
VII. Role Skill Intelligence by RChilli in Agent.ai
A new Role Skill Intelligence capability has been introduced to analyze job titles and job descriptions and identify the skills required for success in each role. This enhancement helps organizations define role requirements more clearly and build a stronger foundation for skill-based hiring.
Key Highlights:
- Role Analysis: Analyzes job titles and role descriptions to identify required skills.
- Skill Categorization: Organizes skills into meaningful groups for better understanding and evaluation.
- Supports Skill-Based Hiring: Helps teams align hiring decisions with actual role requirements.
- Improved Role Clarity: Brings more consistency and clarity to role expectations and hiring criteria.
Benefits:
- Strengthens skill-based hiring and evaluation.
- Improves job design and role definition.
- Creates better alignment between roles and candidates.
Latest Updates
Street Address Parsing Improvement
Enhancement to the address parsing logic to prevent non-address text from being incorrectly identified as part of an address. In some cases, junk or unrelated words (such as service names) appeared before the actual location and were mistakenly included in the parsed address.
Key Improvements
- Improved filtering to ignore non-address content during address extraction.
- Prevents junk words (e.g., company/service descriptions) from being treated as address components.
- Ensures that only valid location information is considered during parsing.
Benefits
- Provides more accurate address extraction.
- Reduces incorrect parsing caused by non-address text appearing before the location.
- Improves data quality for location-related fields.
Latest Updates
1. Enhancement in UpdateIndex API
The Search Engine UpdateIndex API now supports improved handling of SubUserID across indexing operations to ensure accurate indexing and cleanup.
- Insert / Add SubUserID: SubUserID can be added to documents that previously had no SubUserID, including both main and nested/child documents, with correct indexing and count tracking.
- Update SubUserID: Updates apply to main and nested documents, with automatic recalculation of SubUserID counts. If the old SubUserID has no remaining documents, it is removed automatically.
- Delete via UpdateIndex API: When documents are deleted, SubUserID counts are recalculated. SubUserIDs with zero remaining documents are removed; otherwise, they remain.
2. SearchEngine | Multi Domain Skill Matching Feature
The Multi Domain Skill Matching (Skill Domain Expansion) feature has been implemented and tested in the SearchEngine Match APIs to enhance skill matching accuracy using ontology-based domain expansion.
This feature is supported across Match, MatchWithId, MatchWithMultipleSubUserIds, and OneMatch APIs for all match types. It requires Ontology Based Search to be enabled along with the Skill Domain Expansion flag in the request.
The enhancement works seamlessly with existing capabilities such as Match using Indexed IDs, Ontology Skill Matching, Skill Ranking, and Skill Group Matching, ensuring improved relevance and consistency in skill-based matching results.
Latest Updates
Taxonomy API | Multi-Domain Support for Skills
The Taxonomy API has been enhanced to support multi-domain (multi-ontology) mapping for skills, improving domain coverage while preserving backward compatibility.
Key Highlights:
- Response Structure Enhancement: Skills now support multiple domain ontologies via a new SkillOntologies array.
- Backward Compatibility: The existing SkillOntology (single domain) field remains unchanged and continues to be returned.
- Related Skills Domain Mapping: Each entry in RelatedSkills now also includes both SkillOntology and SkillOntologies, enabling domain mapping for related skills as well.
Latest Updates
1. Skill Filtration and Matching Based on JobProfile Ontology
The Search Engine Match API has been enhanced with ontology-based skill filtration to improve the contextual accuracy of skill matching.
Key Highlights:
- Introduced a new matchOntologySkills feature in the Match API.
- When enabled, the API filters and matches only those skills whose ontology category aligns with the relevant Job Profile ontology in both Job Descriptions and Candidate Profiles.
- Reduces irrelevant skill matches and improves overall match relevance and precision.
This enhancement ensures more meaningful and context-aware skill matching in the Search Engine.
2. SearchEngine | Ontology-Based Related Skills Arrangement by Skill Group
The SearchEngine skill matching logic has been enhanced to improve ontology-based matching by organizing and filtering related skills based on their skill groups.
Key Highlights:
- Ensures that only related skills within the same skill group are considered during ontology-based matching.
- Eliminates duplicate and unrelated skill matches across the matching process.
- Improves overall accuracy, consistency, and relevance of skill matching across regions and endpoints.
This enhancement delivers more precise and structured ontology-based skill matching in the SearchEngine.
3. SearchEngine | Degree Weight Optimization in All Match Types
The Degree Weight Optimization feature (mergeDegreeScore) has been implemented and tested across all match types and methods in the SearchEngine, extending its availability beyond JD to Resume matching.
Key Highlights:
- Expanded Match Type Support: Now supported for JD to JD, Resume to JD, and Resume to Resume match types.
- Method Coverage: Works with Match, MatchWithId, MatchWithMultipleSubUserIds, and OneMatch methods.
- Optimized Degree Scoring: Enables improved degree and qualification scoring when "mergeDegreeScore": true is provided in the request.
- Degree Support: Supports Semantic Degree and Degree with Specialization; Partial Degree is not supported.
This enhancement ensures consistent and optimized degree-based scoring across all SearchEngine matching scenarios.
4. SearchEngine | Degree with Specialization Feature – All Match Types
The Degree with Specialization feature has been fully implemented and tested across all match types and supported methods in the SearchEngine module.
Key Highlights:
- Supported Match Types:
- JD to Resume
- JD to JD
- Resume to JD
- Resume to Resume
- Supported Match Methods:
- Match
- MatchWithID
- MatchWithMultipleSubUserIds
- OneMatch
Important Notes:
- The feature requires “Include Source Type” to be set to true.
- The feature does not work when Partial Match is enabled.
This enhancement ensures consistent and accurate degree matching with specialization support across all SearchEngine matching scenarios.
Latest Updates
AI Agents
1. Candidate Prescreening Agent
The Candidate Prescreening Agent automates the initial screening of candidates applying through Oracle Recruiting Cloud. It analyzes candidate profiles and application data against predefined job criteria to generate prescreening insights.
This enables recruiters to quickly identify suitable candidates, reduce manual
screening effort, improve hiring efficiency, and maintain a consistent and unbiased
prescreening process.
2. Interview Question Generator
The Interview Question Generator automatically creates job-specific interview
questions for candidates in Oracle Recruiting Cloud. By analyzing candidate
profiles and job requirements, it generates structured and targeted questions that
help recruiters conduct consistent interviews, assess candidate suitability more accurately, and reduce the time spent on manual question creation.
3. Job Description Optimizer
The Job Description Optimizer generates clear, relevant, and well-structured job
descriptions based on job requisition details in Oracle Recruiting Cloud. This agent
helps recruiters save time, maintain consistency across job postings, and improve the overall quality and clarity of job descriptions.
4. Job Application Analyzer
The Job Application Analyzer evaluates candidates against open positions by
analyzing job requirements and candidate profiles. It provides a Matching Score,
Skill Gap Analysis, and Candidate Ranking, enabling recruiters to assess candidate fit quickly, identify gaps, and make informed, data-driven hiring decisions.
5. Profile Augmentation – Talent Data Refresh
The Profile Augmentation – Talent Data Refresh Agent automatically enriches
and updates candidate profiles in Oracle Recruiting Cloud. It refreshes key
information such as skills, certifications, languages, mobile numbers, and other
profile details, ensuring candidate data remains accurate and up to date while
reducing manual maintenance efforts.
Latest Updates
LLM Parser – GPT-5 Model Temperature Configuration
The LLM Parser has been enhanced to support GPT-5.x models with configurable temperature settings. This upgrade enables better control over model behavior, resulting in higher parsing accuracy, improved contextual understanding, and more reliable enrichment output.
Key Highlights:
- GPT-5.x Model Support: Ensures compatibility with the latest generation of OpenAI models.
- Temperature Configuration: Allows fine-tuning of response determinism and creativity for optimized parsing results.
- Improved Parsing Quality: Delivers more accurate, context-aware, and consistent data extraction.
- Faster Enrichment Processing: Optimized performance for efficient resume parsing and enrichment.
Latest Updates
The Dynamic Field Matching enhancement
The Dynamic Field Matching enhancement introduces flexible, entity-level field mapping within the BooleanSearch API. This update allows clients to dynamically configure how
resume and job description entities are matched, providing greater customization, accuracy, and control over Boolean search behavior.
Key Highlights:
Dynamic Entity Mapping:
Clients can now define custom entity-level mappings in real time to adjust Boolean
search results based on their business requirements.
Smart Default Mapping:
When no custom configuration is provided, the system automatically applies RChilli’s
optimized default sub-entity mappings, ensuring seamless backward compatibility.
Enhanced Search Accuracy:
Improved understanding of hierarchical entity relationships leads to more precise and relevant Resume–Job Boolean matching.
Latest Updates
Enhanced Skill Extraction in JD Parser
The JD Parser has been improved to accurately extract multiple skills listed together within a
job description. Previously, when skills were written in combined formats such as
“Functional, Regression & Integration Testing”, the parser extracted only the last skill
(Integration Testing).
With this enhancement, the parser now recognizes and extracts all relevant skills
separated by commas, slashes, or ampersands.
Example:
Input: Functional, Regression & Integration Testing
Output: Functional Testing, Regression Testing, Integration Testing
This update ensures more comprehensive and precise skill extraction, improving overall
parsing accuracy.
Latest Updates
1. Job Mapping Feature in Oracle Browser Assistant
The Job Mapping Feature in the Oracle Extension introduces an automated and efficient way to associate candidate applications with specific Job Requisitions within Oracle. This enhancement simplifies the recruiter workflow by seamlessly linking candidates to job openings directly through the Browser Assistant.
Feature Details
- Recruiters can now fetch active Job Requisitions from Oracle and select a Job ID when adding candidates via the Browser Assistant.
- Once submitted, the candidate’s application is automatically mapped to the selected Job ID in Oracle.
- The application status is updated only after a successful import into Oracle, ensuring accurate synchronization.
- Recruiters can view all new candidate applications under their respective Job IDs in Oracle Requisitions.
2. Search and Match in SAP Browser Assistant
The Search and Match feature has been introduced in the SAP Browser Assistant to enhance recruiter productivity and improve candidate-job matching.
Key highlights:
- Search and Match: Recruiters can now search candidates using keywords and filters such as Job Title, Skills, and Location, ensuring faster and more relevant results.
- Job Application: When a Job Requisition is opened, the assistant automatically scans and indexes resumes, displaying candidates with real-time match scores for easy mapping.
- Candidate Indexing: Includes Onboard Candidate Indexing, and Users can click on the “Index Candidate” button to trigger indexing for all existing candidates.
Latest Updates
1. Analyze API Enhancement
The Analyze API now supports a new Job Profile Related Skills feature. When the jobRelatedSkills flag is enabled in API requests, the response includes a dedicated section listing skills that are contextually related to the identified job profile.This enhancement provides richer, job-specific skill insights for both Resume and Job Description (JD) searches, improving the precision and depth of analysis.
2. Search and Match API v4 – Default Feature Enablement for Enhanced Matching
In Search and Match API version 4, several advanced matching features are now enabled by default, ensuring improved accuracy, contextual relevance, and smarter search results without additional configuration.
The update maintains backward compatibility, allowing users to disable these features via request parameters if required.
Key Enhancements
- Skill Ranking: Automatically prioritizes candidates based on skill proficiency and relevance.
- Ontology-Based Search: Uses RChilli’s skill and job ontology to expand related skill and job matches.
- Semantic Degree Search: Enhances educational matching by identifying equivalent or related degrees semantically.
- Merge Degree Score: Combines degree-based matching scores with overall profile relevance for better accuracy.
- Configurable Control: Each feature can still be disabled through API request parameters for legacy compatibility.
3. Skill Experience Matching Feature Implementation in Search and Match API
The Skill Experience Matching feature has been introduced in the SearchEngine Match modules for the JD to Resume match type. This enhancement allows the matching algorithm to factor in skill-specific experience durations mentioned in Job Descriptions (JDs), improving the precision and contextual relevance of candidate matching.
Key Highlights
- Feature Integration:
Skill Experience Matching is now available in the following methods:
- Match
- MatchWithId
- MatchWithMultipleSubUserIds
- Configuration:
- Enable the feature by setting "skillExperienceMatch": true in the request.
- Ensure the JD Parser or ParseAndIndex API includes "experiencewithskill": true in the apisetting.
- JD Indexing (v4):
- Updated to support the SkillHistory object.
- Accessible via the GetDocumentDetail API.
- Error Handling:
- Introduced error code 2189: "skillExperienceMatch type must have value either true or false."
- Introduced error code 2189: "skillExperienceMatch type must have value either true or false."
- Scope:
- Currently applicable only to JD to Resume match type.
- Not applicable to OneMatch, skill proficiency, or dynamic weightage features (to be added in future versions).
Latest Updates
Enhance Degree Normalization and Specialization Handling in Parsers (ISCED Standard)
Enhancements in both the Resume Parser and JD Parser, focusing on improving degree normalization and specialization extraction based on the UNESCO ISCED 2011 Standard.
- Standardized Degree Mapping:
All parsed degrees are now mapped to ISCED levels for globally consistent education classification. - Accurate Specialization Extraction:
Specializations are precisely separated from degree names for both Resume and JD parsers.
Example: “M.Tech in Computer Science” → Degree Level: Master | Normalized Degree: Master of Technology | Specialization: Computer Science. - Improved Normalization Rules:
- “Postgraduate” and “Graduations” now correctly map to Master’s level.
- “Post Graduate Diploma” recognized as a distinct, valid qualification.
- Enhanced handling of complex degree strings for better data accuracy.
- Configurability:
Added configuration options for degree levels and specialization defaults.
Default configurations applied for Resume Parser v8.1.0 and JD Parser v3.2.
Latest Updates
1. Skill Sorting Based on Skill Index
A new sorting logic has been implemented to arrange skills according to their Skill Index within the resume data.
- Ensures the skills are presented in a structured and prioritized order.
- Improves data consistency across parsed outputs and enhances downstream analytics accuracy.
2. Month Experience Extraction
A custom enhancement has been added to improve month-level experience extraction.
- Extracts both years and months for total experience, providing granular experience data.
- Boosts accuracy for resumes with short-term or overlapping roles.
3. Enhanced Employer Extraction
Improvements in employer name recognition and tagging to ensure better completeness of employment data.
a. Improved Employer Extraction Logic
- Refined parsing algorithms improve the detection of organization names, even in unstructured or inconsistent formats.
b. Fix: Employers Missed by Neuro Tagging
- Resolved an issue where certain employer names were not being tagged due to neuro-model limitations.
- Ensures that all detected employer entities are consistently tagged and mapped.
c. Fix: Partially Extracted Employers
- Corrected cases where only partial employer information (e.g., missing company suffix or prefix) was extracted.
- Enhances the completeness of employer name extraction for more reliable experience records.
Latest Updates
1. Enhanced Email Alert Feature for Clients
The Email Alert feature has been enhanced to provide more flexibility in managing notifications for both admins and users. Previously, email alerts were only enabled for admins, with users
having them disabled by default.
Key Features:
Admin Control: Admins can now enable or disable email alerts for individual users.
User Alerts: Once enabled, users will start receiving email alerts for relevant
notifications.
Benefits:
Provides admins with better control over user notification preferences.
Increases flexibility in managing email alerts for both admins and users.
2. New Bulk Import Users Functionality in ERP System
A new Bulk Import Users functionality has been introduced in the ERP system, allowing Oracle and SAP clients to quickly and efficiently add multiple users to the Browser Assistant.
Key Features:
Bulk User Import: Clients can now import users in bulk using a CSV file.
Benefits:
- Reduces the time and effort required for adding users individually.
- Enhances operational efficiency for Oracle and SAP clients.
Latest Updates
1. Enhancement to JD Parser API - Addition of Skill Group Name
The JD Parser API has been enhanced to include the Skill Group Name for all skills in the database, improving the detail provided in skill-related responses.
Key Features:
Skill Group Name: The JD Parser API now returns the skill_group_name in the Skill
and Multi-Skill Search responses.
Configurable Setting: A setting has been added to control whether the skill group name is included in the response.
Benefits:
Provides more comprehensive skill information.
2. Skill-Wise Experience Extraction Enhancement in JD Parser
The JD Parser has been enhanced to extract skill-specific experience requirements, improving the accuracy of candidate matching in the SearchAndMatch API. This enhancement allows the parser to match experience tied to specific skills, such as "5 years of experience in Software
Testing," which was previously unsupported.
Key Features:
- Skill-Specific Experience Extraction: The JD Parser now extracts experience
requirements tied to individual skills.
- Dynamic Setting: A dynamic setting has been implemented to adjust experience
matching based on skill-specific criteria.
Experience Matching Logic: The updated parser calculates total experience per
skill/domain, ensuring candidates meet the minimum experience required for each skill in the JD.
Benefits:
-Improved precision in matching candidates to job descriptions based on skill-specific experience.
- Enhanced accuracy, especially for senior or specialized roles like QA Manager.
Latest Updates
Enhancement to Taxonomy API - Addition of Skill Group Name
The Taxonomy API has been enhanced to include the Skill Group Name for all skills in the database, improving the detail provided in skill-related responses.
Key Features:
Skill Group Name: The Taxonomy API now returns the skill_group_name in the Skill
and Multi-Skill Search responses.
Configurable Setting: A setting has been added to control whether the skill group name is included in the response.
Benefits:
Provides more comprehensive skill information.
Latest Updates
1. Enhanced Total Experience Calculation in Resume Parser
The Resume Parser now calculates the total professional experience in months, summing up the durations mentioned in the experience section. This enhancement improves the accuracy of experience tracking by converting years and months into a total duration in months.
2. Enhancement to Resume Parser API - Addition of Skill Group Name
The Resume Parser API has been enhanced to include the Skill Group Name for all skills in the database, improving the detail provided in skill-related responses.
Key Features:
Skill Group Name: The Resume Parser API now returns the skill_group_name in the
Skill and Multi-Skill Search responses.
Configurable Setting: A setting has been added to control whether the skill group name
is included in the response.
Benefits:
Provides more comprehensive skill information.
Latest Updates
New feature "Skill Groups" in SearchAndMatch API
A new feature, Skill Groups, has been introduced in the SearchAndMatch API to improve the
accuracy of skill-based matching. This feature groups related skills together and applies
weightage at the group level, rather than to individual skills, to enhance match relevance.
Key Features:
- Skill Groups: Related skills are now grouped into Skill Groups, allowing for more
contextually accurate matches.
- Group-Level Weightage: Weightage is applied to the entire skill group, ensuring a more
holistic approach to matching rather than focusing on individual skills.
- Improved Matching Accuracy: Grouping related skills reduces query fragmentation and
results in better ranking and more relevant matches.
- Updated Taxonomy Database and API: The taxonomy database has been updated to
include Skill Groups, and the Taxonomy API now returns skills along with their
associated groups.
- SearchAndMatch API Update: The SearchAndMatch API has been updated to
leverage Skill Groups during the matching process, ensuring more precise and
semantically relevant results.
Benefits:
- Enhanced accuracy and relevance by grouping related skills.
- Improved ranking and reduced query fragmentation.
- More semantically aligned matches, ensuring better outcomes in skill-based matching.
Latest Updates
[JD] Specialization Extraction Without Degree
In JD parser now extracts specialization information from job descriptions (JDs) even when no degree is mentioned. This allows for the extraction of detailed specialization data, ensuring comprehensive parsing of the JD content, even in cases where degree information is absent.Latest Updates
1. Job Profile Level Match Enhancement in SearchAndMatch APIThe SearchAndMatch API has been enhanced with the Job Profile Level Match feature to improve the relevance and accuracy of job matching by considering the seniority level of job profiles.
Key Improvements:
- Job Profile Level Classification: Job profiles are classified by seniority levels (e.g., Junior, Mid, Senior, Managerial) based on job title keywords or mappings within the API.
- Seniority-Based Matching: The API filters related job profiles to match the same level of seniority as the input job profile, ensuring more precise matches.
- Reduced Irrelevant Matches: By considering seniority, the feature eliminates irrelevant matches, such as suggesting junior roles for senior positions.
- Ontology-Based Search Dependency: The Job Profile Level Match functionality will only work when the Ontology-Based Search feature is enabled.
Benefits:
- Enhanced job profile matching by aligning roles based on seniority.
- Improved relevance and precision of job suggestions, reducing mismatches.
- More accurate and contextually appropriate recommendations.
2. OpenAI Integration for Enhanced Semantic Matching in Search and Match API
The Search and Match API now integrates OpenAI to enhance semantic matching between
skills and job profiles, improving precision and recall.
Key Features:
- Semantic Expansion: Expands skills and job profiles into semantically related sub-skills.
- Semantic Scoring: Matches are scored based on semantic similarity.
- Structured Responses: OpenAI integrates seamlessly with existing search logic.
Benefits:
- 20% Increase in Match Accuracy (precision/recall).
- Improved Candidate and Job Matching.
Latest Updates
Agentic Browser Assistant for Candidates and Recruiters
The Agentic Browser Assistant introduces several new features designed to streamline the user experience for candidates and recruiters. Users can now log in automatically by uploading their resume, match job description (JD) text directly to their resume, and view their last four JD matching scores. Additionally, users can easily update their profile with the latest resume details. These updates improve efficiency, accuracy, and ensure profiles are always current.
Oracle API Testing Enhancement in Oracle Client Panel
A new enhancement has been implemented in the Oracle Client Panel, allowing clients to test their API details directly from the interface. This feature streamlines the process of verifying API integrations, making it easier for clients to ensure their configurations are correct.
Test API Details Feature:
- Users can now input their API credentials and URL directly in the Oracle Client Panel.
- After clicking the “Test API Details” button, the system will return a response indicating the connection status or any error details.
New Features Added to MyAccount Search and Match
The following new features have been implemented in MyAccount Search and Match. These features aim to enhance the matching process by optimizing how resumes and job descriptions are scored.
New Features in MyAccount Search and Match:
- Ontology Based Match
- Default: True
- Semantic Degree
- Default: True
- Degree with Specialization
- Default: True
- Degree Weighting: 80% Degree | 20% Specialization
- Default: True
- Degree Weight Optimization
- Default: True
- Semantic Match
- Default: False
- Skill Ranking
- Default: False
- Partial Match
- Default: False
- Job Zone Weightage
- Default: False
These new features have been added with default values set to optimize the matching and scoring process. The goal is to improve the relevance and accuracy of resume and job description matching.
RChilli Oracle HCM API – Enhancement: Certification Date Field Display & Phone Number Formatting
- Enhanced Date Fields: Improved handling of Oracle date fields so that they now display correctly with their associated description fields, ensuring users can view complete and accurate date information.
- Enhanced Phone Number Formatting: Phone numbers are now formatted consistently for better readability across the application.
Latest Updates
Taxonomy Shopify
|
Table Name |
Total Count [Aug’25] |
|
Skill |
42337 |
|
SkillAlias |
344087 |
|
MultiLangSkill |
755,187 |
|
MultiLangSkillAlias |
5196791 |
|
MultiLangJobProfileAlias |
4003573 |
|
MultiLangJobProfile |
750794 |
|
JobProfileAlias |
311595 |
|
JobProfile |
33777 |
|
Abilities |
70931 |
|
JobPorfileSectorRelation |
33773 |
Latest Updates
Ontology-Based Search Feature Implementation in Resume to JD and Resume to Resume Match
The Ontology-Based Search feature has been integrated into the Resume to JD and Resume to Resume match type within the Search and Match API. This enhancement significantly improves the matching process by identifying entity values that may not be explicitly present in the document, ensuring more accurate and semantically relevant matches between resumes and job descriptions (JDs).
Semantic Degree Search Feature Implementation in JD to JD Match
To improve degree matching accuracy, the Semantic Degree Search functionality has been extended to the JD to JD match type within the Search and Match API. This update enhances the matching process by addressing issues with exact keyword matching, considering semantically similar degrees to ensure better matching for similar degree types across job descriptions.
Latest Updates
Address LoV Mapping
The RChilli Oracle API has been enhanced to support the conversion of free-text address fields into country-based Oracle dropdown values for seamless integration with Career Sites. This enhancement introduces a LoV (List of Values) mapping mechanism for address fields, including County, State/Province, City, and ZipCode, based on the selected Country. This is critical for Oracle Career Sites that require dropdown selections for address data, ensuring that free-text address inputs are accurately mapped to predefined values.
Latest Updates
Symbols Restoration in the Parser Response
The Resume Parser now ensures that all symbols are accurately restored and represented in the parsed response. This includes special characters (e.g., "®", "©") that are now properly retained, enhancing the integrity of the extracted data.
Skill Sorting by Index
A new setting has been implemented to ensure that skills are returned in the exact order as defined in the resume. Previously, skills were sorted arbitrarily, but with this update, they are now displayed in the order they appear in the document, improving data alignment with the candidate’s resume.
Latest Updates
1. Language Wise Document Matching in Search and Match API
The Language Wise Document Matching feature has been implemented in the Search and Match API, enhancing document matching capabilities with both default and dynamic language filters.
- Default Matching:
- Automatically matches documents in the same language by default.
- Dynamic Language Filter:
- Users can specify their preferred language for matching resumes and job descriptions.
- Improved Match Quality:
- By prioritizing documents in the same language or based on the specified language, the feature ensures more accurate and relevant results.
- Technical Documentation Update:
- Documentation has been updated to reflect the new language-wise matching functionality.
2. Degree with Specialization and Semantic Degree Search Enhancement
The Degree with Specialization feature has been enhanced to improve degree matching accuracy, with added support for Semantic Degree Search.
Key Improvements:
- Required Degree Priority: Ensures the required degree is prioritized in the matching process.
- Degree Mapping Priority: User-defined degree mapping takes precedence over the preferred degree section.
- Duplicate Degree Handling: Excludes duplicate degrees in the preferred section from matching.
- Semantic Degree Search Integration: Now supports Degree with Specialization for more accurate, context-driven matching.
Latest Updates
1. Job Profile Extraction Accuracy Improvement
The Resume Parser has been optimized to enhance the accuracy of job profile extraction by resolving issues related to "junk" job profile data. Improvements have been made to the parsing algorithm and machine learning models to ensure more precise extraction. Extensive testing was performed on various resume formats to validate these improvements, resulting in fewer incorrectly tagged job profiles.
2. Employer Name Extraction Accuracy Improvement
The Resume Parser has been improved to enhance the accuracy of employer name extraction, addressing issues where employer names were missed or incorrectly tagged. Enhancements to the parsing logic now ensure better handling of employer names, especially in cases involving compact formats, single-word employer entries, and tabular or inline text structures. These improvements result in increased accuracy and consistency in extracting employer data across various resume formats.
Latest Updates
Level 5 User Login Enhancement for MyAccount
The user experience for Level 5 users has been enhanced to allow direct login to the MyAccount dashboard after successful signup. Previously, users were redirected to the meeting setup page for the Marketing team. Now, upon successful signup, Level 5 users will be taken directly to the dashboard, where they can parse their trails credits within MyAccount.
- Impact: This update streamlines the user journey, providing Level 5 users with immediate access to their dashboard and credits.
- Outcome: Users can now log in directly and access the dashboard without unnecessary redirects.
- Support for Major/Minor Fields in Oracle Browser Extension/Data Reprocessing
The Oracle Browser Assistant has been enhanced to support the extraction of Major and Minor fields from resumes. After parsing, these fields from the Education section will be extracted and displayed within the Oracle UI for a more comprehensive view of the candidate's qualifications.
This update enhances the resume parsing process by providing more detailed insights into candidates' educational qualifications, improving data visibility and accuracy.
JobId Column Added in Oracle Extension Logs
The JobId function has been successfully integrated into the Oracle Browser Assistant, enhancing the tracking and logging capabilities. This addition allows for better monitoring and management of job-specific logs, ensuring more efficient log analysis and troubleshooting.
This enhancement improves the visibility and traceability of logs, aiding in more effective issue resolution and performance monitoring.
Enhancements and Fixes for Oracle Client Panel on MyAccount
The Oracle Client Panel on MyAccount has undergone significant design enhancements, aimed at improving user navigation and overall interface usability. This update includes updated tab names for easier access and a more streamlined layout, ensuring a smoother user experience.
These improvements make the Oracle Client Panel on MyAccount more intuitive and user-friendly, enhancing the overall experience for clients.
Latest Updates
1. Integration with Google Vision OCR for Document Conversion
- The Document Converter API has been upgraded to integrate Google Vision OCR for document-to-text conversion, replacing the previous ABBYY OCR solution.
- This integration brings the capabilities of Google Vision OCR to the Document Converter API, improving the accuracy and performance of text extraction from scanned or image-based documents. The new functionality has been successfully implemented and is now fully operational within the API.
.
2. RChilli Plugin API Job Description Recommendation Plugin
The Job Description Recommendation Plugin has been implemented in the RChilli Plugin API to enhance job descriptions by analyzing and identifying missing or unclear elements. It aims to improve job descriptions to attract qualified candidates.
Features:
- Analyzes key elements of job descriptions, including responsibilities, skills, education, and certifications.
- Suggests improvements to make job descriptions more inclusive, clear, and comprehensive.
- Generates a rewritten job description that attracts qualified talent.
To know more details, refer Plugins
Latest Updates
Oracle Browser Extension Frontend Update
- The Oracle Browser Extension has been updated to enhance the mapping functionality for Education, Language, School, and Certification fields. When mappings are available for these fields, a "Select" dropdown will appear, allowing users to search and select values from predefined options.
LOV Mapping Enhancement
The LOVMapping has been enhanced to allow userd to control section additions to Oracle based on ContentItemID, mapping availability, and configuration settings.
This improvement ensures more flexible and accurate data handling, reducing unnecessary additions and providing clients with better control over the data flow into Oracle. Error handling and logging have been implemented to ensure reliability and transparency.
Latest Updates
- UpdateIndex API Upgrade to Version 4.0
The UpdateIndex API has been enhanced to support Search and Match v4.0. To know more details, refer Update Index.
- Ontology-Based Search for JD to JD Match
The Ontology-Based Search feature has been implemented in the JD to JD match type, expanding its application from the JD to Resume match type.
This enhancement allows for more accurate matching of job descriptions (JD) by leveraging ontological relationships, improving the overall match quality. The feature enables better understanding and alignment of job description content through semantic search, enhancing the match results for JD to JD comparisons. For more details, refer to Ontology Search.
Latest Updates
1. Block Boundaries Extraction Improvement
- The ResumeParser API has been enhanced to implement block-level boundary detection, ensuring that semantically similar sections, such as "Education" and "Summary of Qualification," are treated as distinct entities.
- This update effectively prevents the merging of content across sections, thereby preserving the accuracy and integrity of the data extracted.
- With this enhancement, each section is now parsed individually, ensuring that the overall parsing process remains efficient without any degradation in system performance. Following implementation, both the accuracy and performance of the parser were thoroughly evaluated and benchmarked to confirm the success of the optimization.
.
2. Education Category Mapping Enhancement
- The ResumeParser API has been enhanced to improve the accuracy of mapping within the Education category.
- The update ensures that institutes and degrees are correctly associated, regardless of their position within the education section or structural formatting.
- This enhancement guarantees that institutes listed at the end of the education section are accurately mapped, and the correct relationships between degrees and institutes are maintained, even when institutes are provided as headings and degrees as subheadings. The result is a more accurate and reliable parsing of education-related data.
3. Degree Data Validation Enhancement
- The ResumeParser API has been enhanced to improve the accuracy of degree extraction in the Education section by addressing the merging of extra or unintended data into degree fields.
- A validation logic has been implemented to conditionally merge degree entries found on the same line, ensuring that degrees are only combined when appropriate based on contextual patterns or separators. This enhancement prevents incorrect data aggregation and ensures accurate degree values are extracted from resumes.
4. Education Category, Board Parsing Enhancement
- The ResumeParser API has been enhanced to resolve the misclassification of "Board" information in the Education section, ensuring it is no longer incorrectly parsed as a "University."
- The category handling logic has been updated to accurately differentiate between "Board" and "University" terms, particularly in school-level education entries. Enhanced checks have been added to prevent misclassification when terms like "Board" appear in resumes, ensuring the correct identification of educational institutions.
5. Education Category, Subject Parsing Enhancement
The ResumeParser API has been enhanced with a fallback mechanism to treat subjects, such as "Information Technology," as degree entries when no explicit degree is specified. This improvement ensures that the parser accurately captures the educational qualification, even when only the subject is mentioned, enhancing the accuracy of the parsed education data.
Latest Updates
New /embeddingMatch Endpoint for In-Memory Document Matching
Improvement in Degree Matching Score for JD-to-Resume
The Degree Weight Optimization feature has been enhanced with the introduction of a new option in the Search and Match API. By sending the "mergePreferredDegreeScore": true parameter, users can now optimize the way qualification matches are handled, particularly for JD to Resume matching. For more details, refer to Degree Weight Optimization.
Contextual Based Entity Extraction in SimpleSearch API
The SimpleSearch API feature has been enhanced with Contextual-Based Entity Extraction, leveraging an AI model to improve search accuracy. Instead of searching for exact terms, the system now understands the broader meaning behind entities. For example, "ME" in an education query is understood as Mechanical Engineering, not just the abbreviation "ME".
- Enable/Disable Flexibility: Users can enable or disable this feature using the "contextualBasedSearch"
- Source Value Inclusion: The Source value for all extracted entities is now included for transparency.
This enhancement improves document matching by ensuring searches are based on context and intent, leading to more relevant and accurate results. The feature has been deployed on the staging server. For more details, refer to Simple Search.
Latest Updates
- Enhanced Multiline JobProfile and Employer Tagging
- The ResumeParser API has been enhanced to accurately tag JobProfile and Employer entities in multiline experience sections. This improvement ensures correct entity tagging, maintaining parsing accuracy without any degradation. This enhancement enhances the handling of detailed and multiline professional experience entries.
- Skill Ranking Analysis and Enhancement
The skill ranking logic in the ResumeParser has been enhanced to prioritize skills extracted from the "Skills" section. This enhancement resolves issues in the skill ranking process, ensuring more accurate and prioritized skill scores.
Latest Updates
Startup Plan Validity Period Update in MyAccount
The Startup (Incubator) Plan has been updated to a new validity period of 3 months. For new users purchasing this plan, it will expire either after 3 months or once the allocated credits are fully consumed, whichever comes first.
Added New LOV for Oracle
This release introduces support for a new List of Values (LOV) type called SOURCE. Users can now upload and manage source-related values via a CSV file in the MyAccount interface. For more details, refer List of Values Mapping in Myaccount.
Key Details:
SOURCE CSV file format includes the following fields:-
- sourceValue → Mapped to oracleValue
- sourceMedium → Mapped to contentItemId
Custom Taxonomy Feature Implemented for Oracle Users
The Custom Taxonomy feature has been implemented for Oracle users, allowing them to add, update, or remove taxonomies for Skills and Job Profiles independently. This gives clients the flexibility to tailor their taxonomy to meet specific business needs, ensuring it stays accurate and up to date across the system. For more details, refer Custom Taxonomy.
Key Benefits:
- Customizable taxonomy for skills and job profiles
- Real-time updates to ensure consistency
- Full flexibility for clients to manage their taxonomy
Latest Updates
SearchEngine | Implementation of Semantic Degree on JD to Resume Match
The new Semantic Degree feature, now in beta, enhances the matching process between job descriptions and resumes by improving the semantic understanding of degrees. This update boosts the accuracy of degree matching and reduces mismatches related to educational qualifications. For more details, refer Semantic Degree.
Improvements:
- Fixed issues with semantic education mismatches
- Enhanced accuracy in degree matching for better comparisons
Latest Updates
Enhanced UpdateIndex Functionality
-The UpdateIndex method has been enhanced to allow users to dynamically insert or remove custom values within resumes or job descriptions (JD) without requiring document parsing through the ResumeParser or JDParser APIs.
-For detailed usage instructions, refer to the UpdateIndex.
Enhancement to Ontology Search Feature
-The Ontology Search feature has been enhanced to allow users to further break down SkillRelatedSkills into subcategories: Similar, Tools, Feature, and Parent.
-Each subcategory now supports its own configurable weight, enabling more precise and customizable search relevance, for more details, refer Ontology Search.
-Additionally, the Ontology Search feature is now applicable to the Simple Search method, extending its capabilities for broader use cases.
Latest Updates
Improved extraction of education degree and specialization for version 3.2
Latest Updates
API setting implemented to remove duplicate skill extraction from JD
Latest Updates
Improved normalization of education degree for version 3.2
Latest Updates
Optimized Resume Parsing for Extensive Experience Details
A new setting has been introduced to improve parsing efficiency for resumes with extensive experience data. By enabling the isMaxExperience and maxNumberOfExperience settings, users can limit the number of experience entries to process, speeding up the parsing time for resumes with long professional histories.
Multi-Line Degree Field Parsing Improvement
The parsing of multi-line degree fields has been enhanced to address issues where only the first line was previously captured. Now, the parser correctly merges and processes multi-line degree entries (e.g., "Bachelor of Technology\nComputer Science"), ensuring accurate extraction of the full degree title, thereby improving the quality of educational data from resumes.
Latest Updates
Improvement in the extraction of education degree and certifications
Latest Updates
- Enhancement in Resume Country Redaction
This enhancement improves the resume country redaction feature by ensuring accurate and consistent masking of country-related information. The update provides better handling of country names in different formats, improving data privacy and ensuring that sensitive information is correctly redacted across various resume types.
Latest Updates
Improved Address Extraction
The Address Extraction logic has been enhanced to more accurately identify candidate addresses within resumes. This improvement increases the precision of address detection, leading to better data quality and downstream processing.
Refined Month Name Handling
Enhanced the handling of month names during format conversions by eliminating unnecessary spaces. This improvement boosts the accuracy and reliability of date parsing across documents.
Improved Role Extraction from Experience Sections
Enhanced the role extraction logic to focus exclusively on job profiles listed under relevant experience section headings. This refinement prevents extraction of unrelated roles mentioned elsewhere in documents, improving data accuracy.
User-Configurable Experience Date Handling
Introduced user-configurable logic for experience dates where, if the start date exceeds the end date, the system automatically sets the start date equal to the end date. This enhancement ensures consistency and accuracy in experience date representation.
Enhancement in City and County Field Deduplication
When the City and County fields contain identical values and the City name is longer than two characters, the County value will be removed from the City field. This update eliminates duplication and helps maintain cleaner, more accurate location data.
Latest Updates
- PDF to HTML Conversion Enhancement
The conversion of PDF resumes into HTML format has been enhanced using Aspose PDF. This update ensures that text, images, tables, and complex layouts are accurately preserved during the conversion process. It also supports scanned PDFs and implements robust error handling for unsupported formats. The solution integrates seamlessly with the resume parsing module, improving data extraction and overall reliability.
- Deduplication of Qualifications List for Unique Competency Entries
The qualifications list generation has been enhanced to eliminate duplicate competency entries. This improvement ensures that only unique skills are included in the qualifications list, preventing redundancy and ensuring data integrity. The solution preserves the separation of job-related and non-job-related skills, while ensuring the list reflects accurate and distinct competency names.
Latest Updates
Enhanced Skill Extraction and Ranking System
- This enhancement introduces a filtered skill extraction process with customizable parameters, allowing better control over skill data extraction based on skill type, source, and duplicate removal.
- The new ranking system factors in multiple elements, such as skill type, source, experience level, and relevance, ensuring skills are accurately ranked and prioritized.
- Customization options are provided to adjust ranking weights and configure cutoff thresholds, offering greater flexibility for clients, especially in the context of Oracle job-related skills.
- Default Source Selection and Job ID Integration for Candidate Export
- This release enhances the candidate export process by introducing handling for Default_Source. If configured, the Default_Source is used in the format "Recruiter <Name> <Email>", otherwise, the frontend-selected source is applied. Additionally, a Job ID field is added, allowing for job ID selection or auto-selection during export, offering greater flexibility and customization for clients.
- Enhanced Resume Update Handling with Duplicate Rules
- Improved handling when users submit the same resume with updated data via Oracle Browser Assistant.
- The system applies backend-defined duplicate rules to appropriately handle updated data.
- Example: If "Experience" is set to "Append," new experience data will be added without overwriting the existing data in the Oracle UI.
- Better integration of updated data while respecting user-defined rules for data retention and modification.
Latest Updates
- Enhancement to Get Document Detail Method in Search and Match
- This enhancement introduces a new input parameter, showCustomValue, to the Get Document Detail method in the Search and Match functionality. When set to true, the matched customValue will be included in the response, providing users with more detailed and relevant information. This update enhances the flexibility of the method by allowing users to retrieve custom values when needed. For more details, refer Get Document Details.
- Enhancement to Ontology Search Feature
- The Ontology Search feature has been enhanced to include JobProfileRelatedProfiles for ontology-based matching. This update improves JobProfile matching accuracy by incorporating related profiles during the search. Additionally, dynamic weightage has been introduced to the JobProfileRelatedJobProfile, allowing for more precise and flexible matching based on the relevance of related job profiles. For more details, refer Ontology Search.
Latest Updates
SAP Browser Assistant redesign with graphical representation of RChilli Logs.
Latest Updates
Implementation of Job Profile Related Job profiles relationship in jobprofilesearch endpoint.
Added over 150K Job Profiles Related Skills.
Added 100 K+ Skill-Related Skills.
Latest Updates
Improved the candidate name extraction logic to better identify candidate names positioned on the right side of resumes.
Improved Email Extraction from the hyperlinks in the PDF Resumes
Enhanced the extraction logic for phone numbers and email addresses to ensure accurate parsing even when proper indentation or formatting is not maintained
Latest Updates
JobProfile Related Skills Search
A new functionality, JobProfile Related Skills Search, is introduced in the Simple Search method. It improves skill-based matching between resumes and job profiles by integrating related skills associated with the JobProfile.
- Retrieves related skills from the Taxonomy API and incorporates them into the search and scoring process.
- Ensures candidates with related skills are considered for matching, even if those skills are not explicitly listed in the resume.
- Improves candidate matching accuracy by considering a broader set of skills.
To know more details, refer JobProfile Related Skills Search.
Latest Updates
Skill Score Based on Skill Proficiency Level in Match Methods
A new functionality, Skill Proficiency Level, is introduced in the Match methods (Match, MatchWithId, MatchWithMultipleSubUserIds, and OneMatch). By adding the skillProficiencyWeight parameter in the request, skill scores are now adjusted based on proficiency levels. The skill query is enhanced to consider proficiency while matching. This feature requires resumes to be indexed using the Search and Match v4.0 API.
To know more details, refer Skill Proficiency Match.
Latest Updates
- Skill Gap Detection Plugin API
A new Skill Gap Detection plugin API has been added to the RChilli Plugin, enabling AI-driven analysis to identify gaps between a candidate's skills and job requirements. It highlights missing skills using job zones and industry standards. For more details, refer Plugins. - JD Builder Plugin API
The JD Builder plugin API is now available in the RChilli Plugin, designed to optimize job descriptions by analyzing input and generating rewritten JDs with key role-specific details. It helps HR teams attract better-matched candidates. For more details, refer Plugins. - Bias Detection Plugin API
The Bias Detection plugin API is now integrated into the RChilli Plugin, helping employers identify biased language and assess tone in resumes and job descriptions. It offers actionable suggestions to enhance inclusivity. For more details, refer Plugins.
Analytics graphs are added to the landing page to display critical insights on product usage, providing a visual representation of key metrics for better monitoring and decision-making
In Oracle: The Source field in Browser Assistant has been updated to include more specific platforms. Now, sources such as Facebook, LinkedIn, Indeed, Monster, and others will be displayed accordingly.
A new functionality, Skill Proficiency Level, is introduced in SimpleSearch. Users can enable this feature by adding the "skillProficiencyWeight" parameter in the request, allowing skill scores to be adjusted based on proficiency levels. The skill query is modified to search for proficiency-based skills within the resume document. To use this functionality, resumes must be indexed using Search and Match v4.0 API.
To know more details, refer Skill Proficiency Match.
Latest Updates
PDF Layout Accuracy Improvement
- Enhanced PDF Layout processing for improved accuracy in resume parsing.
- Optimized Resume Parser API to handle varied PDF structures efficiently.
- Ensured consistent and reliable data extraction across different layouts.
Taxonomy DB Optimization | Adding Missing Job-Related Skills
- The Taxonomy Database has been enhanced by adding missing job-related skills, improving skill coverage and accuracy in ontology-based search and matching.
Latest Updates
Ontology Search Enhancement
- Users can now modify the semantic/taxonomy skill weightage for the Ontology-Based Search
- The introduction of the SkillRelatedSkills parameter allows users to assign a different weight to related skills instead of using the same weight as actual skills.
- This provides better control over skill relevance, ensuring a more refined and accurate search experience. For more details, refer Ontology Search.
Dynamic Weightage Enhancement
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The resume to JD matching process has been enhanced by introducing SkillWithoutExp, enabling users to assign a separate weight to skills without experience.
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When SkillWithoutExp is used, skills not found in the experience section will be scored based on the defined weight, instead of using RequiredSkillSet This allows fine-tuning of skill-based matching, helping prioritize experienced candidates more effectively. For more details, refer Dynamic Weightage.
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Enhanced the Oracle Extension by adding a new feature that allows users to select a source when uploading a resume from a dropdown list (e.g., Facebook, LinkedIn, Indeed, etc.). The selected source is saved in the Oracle system along with the resume, ensuring better tracking and data management. Users can provide and define the source list as needed.
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UI/UX Enhancement
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A new design has been implemented in the My Account for Oracle, SAP, and ServiceNow, enhancing the user experience with an improved interface and streamlined navigation.
Resume Parser Enhancement
- The issue with "Category - Experience without Heading" has been fixed, ensuring accurate parsing of experience details even when a heading is missing.
- Logic for handling the Present Period in experience extraction has been updated, improving the accuracy of work duration calculations.
Latest Updates
Question Generator Plugin API
A new AI-powered Question Generator Plugin API has been introduced to automate tailored interview question creation. Using an LLM, it analyzes key resume sections (experience, education, skills) to generate relevant questions that assess a candidate's suitability for specific roles. For more details, refer to Plugins.
Cover Letter Plugin API
A new Cover Letter Plugin API has been introduced to create personalized and professional cover letters by analyzing the provided resume and job description. This API extracts relevant skills, experiences, and qualifications from the resume and aligns them with the job requirements for a tailored and impactful cover letter. For more details, refer to Cover Letter Plugin. For more details, refer to Plugins.
Skill Ranking Plugin API
The new Skill Ranking Plugin API ranks skills extracted from resumes based on factors like relevance, recency, proficiency, and section quality. It extracts soft skills, behavioral skills, and operational skills from various sections and prioritizes the top 30 skills for matching by default. For more details, refer to Plugins.
- The employer name extraction from resume descriptions has been significantly improved, providing higher accuracy in identifying employer names and delivering more reliable results.
- Improved Employer extraction from the project sections.
- Enhancement in the extraction of the normalized degree for multilingual languages.
- The API Setting page in MyAccount has been enhanced with the addition of the Objective Section to the drop-down menus for SkillSource, SoftSkillSource, OperationalSkillSource, and BehaviourSkillSource This update enables the extraction of skills directly from the Objective Section, improving flexibility and customization in skill extraction.
Latest Updates
GeoSearch Feature Enhancement
- The GeoSearch feature now includes minRadius and maxRadius parameters, allowing users to define the search radius from a specified longitude/latitude or city/state/country. This update enhances precision and flexibility in location-based searches. For more details, refer Geographical Search.
Search and Match v4.0
- Introducing Search and Match v4.0, offering an enhanced approach to matching job profiles with resumes. This version is designed for users seeking profile-specific matching, prioritizing relevant skills and qualifications over overall candidate experience, ensuring more precise and tailored results. For more details, refer Search and Match v4.0.
Role-Wise Experience in Search and Match v4.0
- The Role-Wise Experience feature, introduced in Search and Match v4.0, provides an improved method for matching job profiles with resumes. This enhancement focuses on profile-specific matching, prioritizing role-relevant experience over a candidate’s overall work history, ensuring more accurate and targeted results. For more details, refer Role-Wise Experience.
Integrated Skill Proficiency Level Indexing in Search Engine 4.0 to refine skill scoring based on proficiency.
- Updated Data Migration Logs and added an Audit Report in Data Hygiene
- Integrated ServiceNow module in MyAccount.
Latest Updates
Czech language parsing has been released.
Improvements
- Entity Tagging from NLP with Deep learning for Organization, Job, Degree, and Institutes
- PDF conversion improvement for column and parallel formats
- Multiple keyword block heading tagging and splitting (e.g Education, Certification and Trading will be considered as education block)
- USA address city, state extraction improvement
- Combine/complete multi-job profile in employment (e.g. Manager/Team Lead/Administrator)
- Tagging/Extracting institute, degrees with fuzzy logics
- Improvement in Institute and degree name completion
- Improvement in tagging/extracting employer w.r.t Location, Job profile and Job period
- Improvement for address city for UK when the city is present in multiple countries
- Improvement for address w.r.t UK ZIP code
- Improvement for Singapore and Hongkong addresses
Bug Fixing
- PDF header text issue fixing
- Start and end date fixing in education when education period is a current period (e.g 2018 - Present)
- Fixing entity issue for apostrophe('s) for Degree, Institute, Sub-institute, and Employer
New Technologies Used
- Tensor-flow
- Spacy
- Word2vec/Glove
- CRF (Conditional Random Fields)
- RNN (Recurrent Neural Network)
- Backward Propagation
- Gradient Descent
- LSTM (Long short-term memory)
- Graph DB
- Weka NER
- Maximum Entropy
- HMM
- Brat
- Text Normalization
- Data Normalization
- Pattern matching
- Tagging
- Fuzzy matching
- Case analysis
- Order analysis
- Delimiter analysis
- Length analysis
- Domain analysis
- Gap analysis
- Density analysis
- Semantic analysis
- Sentence Analyzer
- NLP
- Machine Learning
- Image Analyses (Face Detection)
- Language Detection
- Execution of Sorting using Custom Fields.
- Improvement in searching using Custom Fields in Boolean Search method.
- Enhancement made in the Geolocation Search.
- Execution of Facets on Custom Fields.
- Enhancements made in the Job Profile Search in Boolean Search Method.
