AI that finds better candidates faster — without the bias.
Sourcing, screening, scheduling automation.
HR AI touches decisions that affect people's livelihoods. That means the accuracy bar is high, the fairness requirements are non-negotiable, and the compliance surface is broad. We've built recruiting and HR automation systems that pass legal review and actually improve hiring outcomes.
Why Evolve Edge
Bias-aware by design Every screening model gets a disparate impact audit before it touches a candidate. Protected class proxies removed. EEOC guidelines built into the model card, not the disclaimer.
Recruiter workflow depth We've spent time in ATS systems, recruiter workflows, and hiring manager review processes. We build AI that fits how recruiting actually works.
Candidate experience focus AI that makes the candidate experience faster and clearer — not a black box that rejects people without explanation. Transparency that protects both candidates and employers.
Use cases
Skills-based matching that scores candidates against job requirements — with documented criteria and bias audit trails available for every decision.
AI-driven candidate sourcing across LinkedIn, GitHub, Behance, and job boards — with ICP-fit scoring and personalized outreach generation.
Automated scheduling that coordinates candidate, recruiter, and hiring manager availability — cutting the email ping-pong that wastes recruiter time.
Personalized status updates, interview prep materials, and rejection communications that treat candidates like humans, not tickets.
Compensation benchmarking, offer acceptance probability modeling, and counter-offer risk assessment — so you make offers that close.
Flight risk modeling trained on engagement, compensation, and career progression signals — with manager-level alerts before resignation.
Client perspective
“Time-to-hire cut from 47 days to 21. Recruiters spend their time on conversations that matter, not screening resumes that don't.”
FAQ
HRTech experts
Let's scope your project
We'll bring operational AI patterns from similar deployments and give you a concrete timeline on the first call.