Industry · HR Tech
AI Solutions for HR and Workforce
Candidate matching, internal knowledge assistants and workforce planning without bias risk.
Overview
HR is one of the few areas where a model's fairness is a legal question, not just a quality one. Any system that touches hiring, progression or pay has to be testable for disparate impact, and that testing has to exist before deployment rather than after a complaint.
We build matching, planning and knowledge systems for HR teams and workforce platforms with bias evaluation as a release gate: models are scored across protected groups on your own data, results are documented, and anything that touches a decision about a person keeps a human accountable for the outcome.
The lowest-risk, highest-return work is usually internal enablement. Policy assistants, onboarding support and knowledge capture from departing experts pay off quickly and touch nobody's employment decision.
Where AI pays off in HR & Workforce
Candidate and role matching
Ranking based on demonstrated skills rather than proxies, with the matching rationale shown to the recruiter.
Internal knowledge assistants
Grounded answers over policy, benefits and process documents, cutting repetitive queries to the HR service desk.
Workforce planning and attrition
Demand and attrition forecasts at team level, used for planning rather than for decisions about named individuals.
Knowledge capture and onboarding
Structured capture of expertise from experienced staff, turned into searchable guidance for new joiners.
What we design around
HR & Workforce constraints
- Bias testing across protected groups as a release gate
- Human accountability for any employment decision
- Employee data minimisation and retention limits
- Transparency to candidates and staff about automated assistance
Services we apply here
How we build for HR & Workforce
Proof in production
Related case studies
Frequently asked
HR & Workforce: common questions
How do you test an HR model for bias?
We measure outcome rates and error rates across protected groups on your own historical data, document the results, and treat a failing test as a blocker to release rather than a note in the appendix.
Can AI make hiring decisions?
We do not build systems that decide. Models rank, summarise and surface evidence; a person makes the decision and their reasoning is recorded alongside it.
What is the fastest HR use case to deploy?
An internal policy and benefits assistant. It removes a large share of repetitive service-desk queries, needs no personal data beyond identity, and carries no employment-decision risk.
Let’s solve your data challenge