AI Workflow Automation for HR & Recruitment

Recruitment software is now one of the most heavily regulated places to deploy AI, and most teams underestimate that until an audit request lands. Any automated employment decision tool you ship in New York City falls under Local Law 144, which requires an independent bias audit and public disclosure before a resume-screener or ranked shortlist can touch a real candidate. Illinois' AI Video Interview Act forces consent and deletion workflows for any model that scores recorded interviews, Maryland restricts facial analysis, and the EU AI Act classifies recruitment and worker-management AI as high-risk — meaning logging, human oversight, and technical documentation are not optional features you bolt on later. We build these controls into the data model from day one, not as a compliance afterthought.

The core AI problem in HR tech is candidate-to-requisition matching, and doing it well means going far beyond keyword overlap. We build embedding-based matching that maps resumes and job descriptions into a shared vector space, then layer a skills ontology — ESCO in Europe or O*NET in the US — so that 'React' and 'front-end engineer' resolve to related competencies rather than missing each other. Retrieval-augmented generation lets a recruiter ask 'who in our talent pool has led a Series B fintech data team' and get grounded, cited answers pulled from parsed profiles instead of a hallucinated list. The hard part is calibration: we design the scoring so it surfaces adjacent-skill candidates without silently encoding proxies for age, gender, or ethnicity.

Nothing in recruiting works in isolation, so integration depth is where an MVP lives or dies. We build against the ATS and HRIS systems your customers already run — Greenhouse, Lever, Ashby, iCIMS, Workday, SAP SuccessFactors, and BambooHR — using their Harvest/Assessment APIs and HR Open Standards (HROpen/HR-XML) schemas so job, application, and candidate objects sync cleanly both ways. Resume parsing (via Affinda, Sovren/Textkernel, or a fine-tuned open model) normalizes messy PDFs and LinkedIn exports into structured profiles. Downstream we wire background-check providers like Checkr, calendar and scheduling for interview loops, and job-board distribution so a single requisition fans out to Indeed and LinkedIn without duplicate data entry.

Fairness has to be measurable, not asserted, so we instrument bias metrics as first-class telemetry. We compute adverse-impact ratios against the EEOC four-fifths rule across protected classes, track selection-rate disparities at each funnel stage, and store the model inputs and outputs needed to reproduce any decision for an OFCCP or Local Law 144 auditor. Structured-interview scoring is built to rubric anchors rather than free-form vibes, and we keep a human-in-the-loop gate on any reject or advance decision so the platform augments recruiters instead of quietly automating them out of the loop — which is exactly the line the EU AI Act draws.