AI RECRUITING WORKFLOW AUTOMATION

Parallel-Agent Resume Screening300 Resumes/Hour, Explainable Scoring

Four specialist agents — skills, experience, culture-fit, compensation-band — evaluate every resume concurrently, then write evidence-backed shortlists straight into Greenhouse, Lever, Workday, and 8 other ATS platforms. Built for EU AI Act and NCOIL transparency from day one.

300 resumes/hour per worker
92% match precision
5x faster time-to-shortlist
EU AI Act + NCOIL ready
WHERE HIRING PIPELINES BREAK

The Five Failure Modes of Modern Recruiting

We have shipped this pipeline against every one of these. Pick the one that describes your week.

Recruiters Drown in Inbound Resumes

A single high-volume requisition pulls 800-2,400 applications in 72 hours

Black-Box Scoring Blocks Compliance

Off-the-shelf AI screeners output a single number with no evidence

ATS Becomes a Dead-End Database

Greenhouse, Lever, and Workday hold the data but cannot reason over it

Generic LLM Screeners Hallucinate Skills

Single-prompt GPT screeners invent experience the candidate never had

Time-to-Shortlist Loses You Candidates

Median time from application to recruiter contact is 6-9 business days

Recruiters Drown in Inbound Resumes

The Pain Point

A single high-volume requisition pulls 800-2,400 applications in 72 hours

Downstream Impact

Top candidates churn out before anyone reads their resume; recruiters review the top 50 by submission time, not by fit

Real Cost

30-45% of qualified candidates never get a human review

Engineering view: Each failure mode is solvable. The hard part is solving all five without trading one for another.

WHAT WE BUILD

Six Engineering DecisionsThat Move the Numbers

No marketing fluff. These are the architectural choices behind the 300/hour and 92% precision.

Parallel-Agent Evaluation

Four specialist agents — skills, experience, culture-fit, compensation-band — run concurrently per resume, orchestrated with LangGraph. No monolithic prompt, no hidden reasoning.

Layout-Aware Resume Parsing

LlamaParse handles structured layouts; unstructured.io takes scanned PDFs, multi-column CVs, and design-heavy portfolios. Identity-stripping happens before any agent sees the document.

Skills Taxonomy Matching

Qdrant vector search over an OpenAI-embedded skills taxonomy resolves "K8s" to "Kubernetes", "RN" to "Registered Nurse", and surfaces adjacent-skill candidates the keyword filter misses.

Evidence-Backed Scoring

Every sub-score ships with the literal resume span that produced it. Recruiters audit the reasoning in two clicks; legal gets the provenance log it needs for EU AI Act and NCOIL.

Bi-Directional ATS Write-Back

Scored shortlists, evidence spans, and audit trails write back to the candidate record in Greenhouse, Lever, Workday, BambooHR, iCIMS, Ashby, SmartRecruiters, JazzHR, JobScore, Recruitee, and Workable.

Bias-Audit Framework

Counterfactual testing across protected classes runs on every rubric change. Compensation-band agent is isolated so stated salary cannot anchor downstream scoring. OpenLineage captures every decision.

ARCHITECTURE

The Pipeline, End to End

Six stages from requisition open to ATS write-back. Every stage is observable, replayable, and audited.

1

Job Opens in ATS

Requisition created in Greenhouse, Lever, Workday, or any of 11 supported platforms. Webhook fires the moment the role goes live.

Greenhouse Harvest API · Lever webhooks
2

Resume Ingestion

Webhook payload delivers candidate record. Our queue picks it up in under 400ms and routes to the parser layer.

Event-driven · sub-second SLA
3

Layout-Aware Parsing

LlamaParse extracts structured sections from clean PDFs and DOCX. unstructured.io fallback handles scans, columns, and portfolio formats.

LlamaParse · unstructured.io
4

Parallel-Agent Evaluation

Skills agent, experience agent, culture-fit agent, and compensation-band agent run concurrently. Each emits a sub-score with evidence spans.

LangGraph · Anthropic Claude · OpenAI embeddings
5

Weighted Aggregator

Rubric weights (configurable per role family) combine sub-scores into a final match score. Confidence intervals flag low-evidence ranks for recruiter review.

Qdrant skills taxonomy match
6

ATS Write-Back

Final score, sub-scores, evidence spans, and provenance metadata write back to the candidate record. Recruiter sees explainable ranking inside their existing tool.

OpenLineage provenance · Sentry observability
Production Stack
LlamaParseunstructured.ioLangGraphAnthropic ClaudeOpenAI EmbeddingsQdrantOpenLineageSentry
WHO SHIPS THIS

Industries Where the Numbers Hold Up

Volume, compliance, and credential complexity each stress the pipeline differently. The architecture handles all three.

High-Volume Retail & Hospitality

Seasonal hiring spikes of 5,000-15,000 applications per week. Parallel screening absorbs the surge without adding recruiters.

Sub-day time-to-shortlist at peak

Customer Support & BPO

RPO firms running 50,000+ applications per week through a single pipeline. Multi-tenant ATS write-back across client orgs.

5x reduction in time-to-shortlist

Tech Recruiting

Engineering and product roles where skills taxonomy matters more than keywords. Qdrant resolves frameworks, languages, and adjacent experience.

92% match precision on senior IC roles

Professional Services & Consulting

Licensed-role hiring with credential verification, region-locked compliance, and partnership-track signaling baked into the rubric.

Auditable scoring for compliance review

Healthcare Recruitment

Licensure parsing, multi-state credential matching, and shift-pattern fit scoring for clinical and allied health roles.

Credential-aware shortlisting
MEASURED OUTCOMES

The Numbers Engineers Care About

Benchmarked on production traffic across high-volume retail, RPO, tech recruiting, and healthcare deployments.

300/hr
Resumes screened per worker
Previously: 12-18/hr manual
92%
Match precision vs. recruiter baseline
Previously: 60-70% keyword filter
5x
Reduction in time-to-shortlist
Previously: 6-9 day median
100%
Explainable scores for compliance
Previously: Black-box screeners
50+
Projects delivered
96%
Client satisfaction
4
Production AI products
IMPLEMENTATION

From Discovery to Multi-ATS Production in 8 Weeks

Two-week pilot. Eight-week production. No production rollout without the bias audit.

Week 1

Discovery & Rubric Digitization

We sit with your recruiters and translate their existing judgment into a structured rubric — skills weights, experience anchors, culture signals, comp bands per role family.

1
Week 2

Pilot: One ATS, One Role Family

Live integration with one platform (Greenhouse, Lever, or Workday) on one role family. Parallel screening runs alongside human screening; we benchmark precision against the recruiter baseline.

2
Week 3-6

Bias Audit & Production Hardening

Counterfactual testing across protected classes, OpenLineage provenance instrumentation, Sentry observability, recruiter-override loops feeding back into the rubric.

3
Week 6-8

Multi-ATS Rollout

Expansion to additional ATS platforms in your stack (BambooHR, iCIMS, Ashby, SmartRecruiters, JazzHR, JobScore, Recruitee, Workable) and full role-family coverage.

4
ENGINEERING FAQ

Questions Recruiting Ops + Legal Actually Ask

No surface-level answers. These are the conversations we have on the discovery call.

We split each resume across four specialist agents — a skills agent, an experience agent, a culture-fit agent, and a compensation-band agent — orchestrated with LangGraph. Each emits its own evidence span and confidence, then an aggregator applies a weighted rubric. That structure is what gets us to 300 resumes/hour with 92% match precision against a human-recruiter baseline, instead of one monolithic prompt that hides its reasoning.
READY TO SHORTLIST IN HOURS, NOT DAYS?

Let's Wire Your ATS to Explainable AI

Two-week pilot on one ATS and one role family. We benchmark against your recruiter baseline before anything ships to production.

Founded 2019 · 50+ projects delivered · clients in US, UAE & Pakistan

2-week pilot Bias-audit included 11 ATS platforms supported