Precision AI Resume Screening
Powered by Multi-Agent RAG
Eliminate subjective recruiting and LLM hallucinations. Our pipeline breaks job descriptions into atomic criteria, searches semantic resume chunks using dense vector embeddings, and validates every qualification with deterministic citation auditing.
The 5-Stage Multi-Agent Pipeline
Each specialized agent performs a discrete, auditable role to prevent single-prompt hallucinations and ensure mathematical reliability.
Deconstructs unstructured job descriptions into individual, unambiguous criteria categorized by type, importance, and minimum years of experience.
Parses candidate resumes into clean semantic partitions (Summary, Skills, Experience, Education) and generates dense vector embeddings.
Retrieves the top-k most relevant resume evidence blocks for each atomic requirement using cosine semantic similarity.
Executes batch evaluation across all requirements in a single pass, categorizing verdicts into matched, partial, gap, or insufficient evidence with exact citations.
Strictly audits every cited quote against retrieved resume chunks. Automatically strips ungrounded claims and downgrades suspect verdicts.
Calculates overall compatibility using deterministic formula-based weighting, eliminating arbitrary LLM score drift completely.
Why Multi-Agent RAG Outperforms Traditional LLM Prompting
Comparing single-prompt ChatGPT resume evaluation against our grounded multi-agent screening system.
- •Hallucinates Skills: Invents candidate skills or tools not present in the resume.
- •Compound Bundling: Groups multiple skills together (e.g. Docker + Kubernetes + AWS) into vague all-or-nothing scores.
- •Subjective Score Drift: Generates different arbitrary scores (e.g., 78% vs 89%) for the exact same input.
- •Token Exhaustion: Long resumes truncate or hit token limits, skipping vital background experience.
- ✓Audited Grounding: Substring-checks every cited sentence; ungrounded quotes are strictly removed.
- ✓Atomic Criteria: Deconstructs JDs into distinct requirement objects with dedicated evidence retrieval.
- ✓Deterministic Scoring: Mathematical weighted formula guarantees 100% score repeatability and transparency.
- ✓Dense Semantic Retrieval: Isolated vector search pinpoints exact evidence chunks regardless of document length.
Multi-Domain Tech Intelligence
Test candidate-job matches across software engineering, cloud architecture, AI systems, security, and product management.
Ready to Experience Grounded AI Screening?
Select any candidate profile and job posting from our pre-indexed library to watch the multi-agent RAG pipeline execute in real-time.