Operator vertical
AI Matching & Credentialing for Staffing Firms
Resume and credential extraction, req-to-candidate ranking, and recruiter research agents for $20–200M staffing firms and PEOs, implemented in your ATS.
Built for: COO or VP Delivery at a $20–200M staffing firm or PEO
What breaks before AI
- ATS search is keyword theater while matches sit on the bench
- Credential misses kill starts
- Recruiters burn hours on research that should be grounded in your notes
- Timesheet and bill-pay exceptions become write-offs
Ranking and extraction you own, tuned to your fill data, integrated with the ATS you already pay for—not another parser SaaS.
Workflow pages operators search
Each page owns a specific job-to-be-done query. Start with the bottleneck that hurts this quarter.
Resume & credential extraction
Search: resume parsing AI implementation
Structured skills, licenses, and dates into the ATS so matching is not a PDF pile.
Req-to-candidate ranking
Search: AI matching engine for staffing
Score the bench against the req with reasons a hiring manager will accept.
Recruiter research agents
Search: AI recruiter agent for staffing agency
Research accounts and draft outreach grounded in your notes—not a generic sequence tool.
Timesheet & bill-pay exceptions
Search: staffing timesheet exception AI
Flag timesheet and invoice mismatches before they become write-offs.
Business outcomes AI can move
- Fill reqs faster with ranked shortlists, not keyword search
- Cut recruiter research hours per submittal
- Reduce credential misses that kill starts
- Catch timesheet and bill-pay exceptions before payroll
AI capabilities
Each capability maps to a business KPI, revenue, cost, risk, or cycle time, not model accuracy on a slide.
Revenue
Req-to-candidate ranking
Score the bench against the req with reasons a recruiter can defend to a hiring manager.
Recruiter research agents
Research accounts and draft outreach grounded in your notes, not a generic sequence.
Cost
Bill-pay exception detection
Flag timesheet and invoice mismatches before they become write-offs.
Cycle time
Credential and resume extraction
Structured skills, licenses, and dates into the ATS so matching is not a PDF pile.
Production use cases
- Resume and credential extraction
- Req-to-candidate ranking
- Recruiter research agents
- Timesheet and bill-pay exception detection
How to engage
Start bounded. Scale only when the KPI moves.
Matching diagnostic
From $5,000Bench vs open reqs: miss rate and ranking opportunity.
Scoped production mandate
From $15kExtraction + ranking live in your ATS in 6–10 weeks.
Embedded partnership
From $25k/moExpand agents, credentials, and bill-pay exception ops.
Frequently asked questions
How is this different from Bullhorn or Jobvite AI?
Those tools add features inside their ATS. We implement ranking and extraction you own, tuned to your fill data, and integrated with the ATS you already pay for.
How do you scope AI for our industry?
Week-one discovery locks business KPIs—revenue, cost, risk, or cycle time—not model vanity metrics. We map your data sources, compliance constraints, and existing stack, then propose a fixed-scope mandate with acceptance criteria or redirect you to a simpler path.
Do you have experience in our sector?
Proven depth sectors (financial services, federal, real estate, telecom, higher education) reflect production systems we've shipped. Growth and operator verticals (MGAs, credit unions, property management, 3PL, RCM, staffing, licensed iGaming, FFL) are selective mandates where those patterns transfer with industry-specific tuning. Biotech, insurance, and regulated work often runs under NDA.
What do we get at the end of an engagement?
Deployable code in your repos, monitoring and runbooks, documentation your team can operate, and knowledge-transfer sessions. The Production Standard is contracted in writing—no handoff to a junior bench after the sale.
What's the fastest way to start?
Book a discovery call or start with a bounded diagnostic: $2,500 Postgres audit, insurance claims-fraud diagnostic from $7,500, or the free MLOps readiness assessment. Each path surfaces whether AI is the right tool before a larger commitment.
The Production Standard
The Production Standard
Six commitments contracted in writing on every engagement.
01
Deploy or redirect
If AI isn't the right tool, we say so in week one and redirect budget to what will ship. No POC theater.
02
Metrics before models
Business KPIs locked in discovery. Model selection follows the metric, not the hype cycle.
03
Governance from day one
Model cards, access controls, evaluation harnesses, and audit trails, not a compliance bolt-on at go-live.
04
Production artifacts
Deployed code, monitoring dashboards, CI/CD pipelines, and incident runbooks. Not recommendations for someone else to implement.
05
6-week production target
Scoped projects designed for production in 6–10 weeks. Boutiques move; global programs wait for steering committees.
06
Capability transfer
Your team can operate, extend, and maintain what we build. IP and documentation transfer is included, not upsold.
Every scoped project includes a production deliverable checklist signed off before close. If we can't commit to deployable output, we won't take the engagement.
Selective intake · 2026
Your next system shouldship to production.
Agentic development from architecture through deployment, with the Production Standard contracted in writing.