Speed,Rigor,Objectivity
Technical hiring managers
Make decisions on evidence, not on the impression of a polished resume.
Hiring Intelligence Workforce
##Meet Your AI Technical Hiring Team
Replace days of manual candidate analysis with an automated AI team that reads resumes, verifies every claim against public sources, audits code on GitHub, and delivers a ranked, evidence-backed shortlist. Instead of a single AI pass, seven specialized agents work in a structured pipeline — from raw resume to a final, defensible hiring decision.
Meet the team
Hiring Orchestrator
Routes and coordinates the team The central brain of the workforce. It receives the hiring request, understands the job requirements and scorecard, then delegates each task to the right specialist in the right order. It keeps the pipeline flowing and consolidates the final output for the hiring manager.
Resume Analyst
Reads between the lines of every resume First stop for every candidate. Runs a forensic pass over the resume: extracts every claim and tags it as claimed, verifiable, or inferred, flags date gaps, vague ownership ("led a team" — how many, doing what?), and inflated titles. Produces a provisional score and hands a verification checklist to the next agent.
Background Researcher
The fact-checker who cites every source Takes the verification list and goes to work on the public web. Confirms, contradicts, or marks each claim as not found — and attaches a source URL to every single finding. Also surfaces new signals beyond the resume: press mentions, publications, past company info.
GitHub Lead
The technical signal coordinator Given a candidate's GitHub profile, it inventories all public repositories and selects the 3–5 highest-signal repos for deep analysis. Delegates each selected repo to the Project Reviewer, then aggregates the findings into one technical report mapped to the scorecard dimensions.
Project Reviewer
The code auditor who separates real engineers from resume-padders Receives repositories one at a time and performs a thorough assessment: code quality, architecture decisions, and commit history. Distinguishes genuine original work from forks, tutorial clones, and boilerplate, and validates authorship authenticity — did the candidate actually write this?
Synthesizer / Scorer
The verdict engine Once every agent reports back, it merges the evidence using a provenance hierarchy (verified > claimed > inferred), computes a weighted score across all scorecard dimensions, identifies key strengths and red flags, makes a clear hire / no-hire / maybe recommendation, and generates interview probes tailored to the gaps it found.
Comparator
The final ranker When multiple candidates have been evaluated, it normalizes scores for fair comparison, delivers specific comparative justifications (not just "82 vs 79"), flags ties and explains what breaks them, and pinpoints the decisive dimensions. The result: a ranked shortlist with a pool-level summary.
What Does It Do?
Multi-layer verification
Every resume claim is cross-checked against public sources — nothing passes without confirmation.
Code authenticity detection
Repository audits separate real engineers from candidates padding their resumes with forks and tutorial clones.
Provenance-weighted scoring
Verified facts outweigh self-reported claims — provenance decides the final score.
Interview probe generation
For each candidate, targeted questions are generated to hit the gaps and uncertainties found.
Multi-candidate ranking
Head-to-head comparison with normalized scores removes bias from the decision.
Evidence-backed dossier
A final candidate dossier with cited sources — ready for the hiring panel.
Meet Your AI Technical Hiring Team
Replace days of manual candidate analysis with an automated AI team that reads resumes, verifies every claim against public sources, audits code on GitHub, and delivers a ranked, evidence-backed shortlist. Instead of a single AI pass, seven specialized agents work in a structured pipeline - from raw resume to a final, defensible hiring decision.
Who Is This For?
ScaleAutomationConsistency
IT recruiters
Make decisions on evidence, not on the impression of a polished resume.
VerificationCodeAuthenticity
Engineering teams
Check a candidate's real code before you spend an interview slot.
Senior rigorLow costSpeed
Startups
Get big-HR-department evaluation quality without building the department.
AsyncRemoteEvidence
Remote Hiring
Deep candidate vetting exactly where in-person verification is hardest.
Key Facts and Statistics
Days → minutes
An evaluation that takes a recruiter days is produced in a single run.
7 agents
A specialized pipeline instead of a single AI pass.
100% sourced
Every finding carries a cited URL — no claim goes unverified.
Frequently Asked Questions
The candidate's resume, a GitHub profile link, and the job scorecard. You can submit one candidate or many at once.
Yes. The Background Researcher confirms, contradicts, or marks each claim as not found — with a source URL for every finding.
The Project Reviewer audits selected repositories: code quality, commit history, and authorship authenticity, separating original work from forks and tutorial clones.
By a provenance hierarchy — verified facts outweigh claims, which outweigh inferred information. The score is weighted across all scorecard dimensions.
Yes. The Synthesizer generates probing questions tailored to the gaps and uncertainties found for each specific candidate.
Yes. The Comparator normalizes scores, breaks ties, and delivers a ranked shortlist with specific comparative justifications.
Yes — engineers, data scientists, architects. It shines on high-volume screening and remote hiring.
How it works?
How Your Hiring Intelligence Team Works
The team operates as a fully automated candidate evaluation system. Multiple agents collaborate to turn a raw resume into a defensible, evidence-backed decision.
Step 1: Submit candidates
You provide a candidate (or many) with resume, GitHub profile, and job scorecard.
Step 2: Resume analysis
The Resume Analyst parses, scores, and flags claims, and builds a verification list.
Step 3: Background verification
The Background Researcher checks every claim on the public web and cites sources.
Step 4: Code audit
The GitHub Lead selects the highest-signal repos and delegates them to the Project Reviewer for a deep code audit.
Step 5: Verdict
The Synthesizer merges all signals, computes a weighted score, and issues a hire / no-hire / maybe recommendation with interview questions.
Step 6: Ranking and delivery
The Comparator ranks the candidates and delivers a ready shortlist with a pool-level summary.
The Result for Your Hiring
- Decisions grounded in evidence, not resume impressions
- Every claim verified and attached to a source
- Real code audits instead of surface-level profile checks
- A defensible, ranked shortlist ready for the panel
- The panel walks in prepared, with interview questions in hand
Your Hiring Intelligence Team works in the background — so you focus on decisions, not on clicking through resumes.
Ready to automate your business?
Connect with our team to see how AI agents can transform your pipeline.
