Building an AI Interviewing & ATS Platform End to End for RegorTalent

RegorTalent is an AI-powered interviewing and applicant-tracking platform. It runs hiring end to end — sourcing and tracking candidates, screening and matching them to roles, and running AI-assisted first-round interviews — in one system instead of a patchwork of job boards, spreadsheets, and disconnected tools.

Brynex Labs was RegorTalent's end-to-end engineering partner. We built the product across the whole stack: the recruiter and candidate web apps, the backend services and APIs, the AI agents that screen and interview, the cloud infrastructure it runs on, and the support tooling around it.

Hiring is drowning in volume. Every role now attracts hundreds of applications — many tuned to game keyword filters — and recruiters cannot realistically read, screen, and interview them all by hand without slowing the whole process to a crawl. Strong candidates lose interest while they wait; weak ones slip through on keyword luck.

RegorTalent needed far more than a database of applicants. It had to source and track candidates, screen and rank them against a role on genuine fit, and run consistent first-round interviews at a scale no human team could match — while staying fast, fair, and reliable enough that hiring teams would trust it with real decisions. Delivering that meant one team owning the front end, the backend, the AI, and the infrastructure together, rather than stitching vendors across each layer.

Brynex embedded with RegorTalent as their full-stack product partner, not a single-layer vendor. We owned the product from the recruiter's screen down to the cloud it runs on, and shipped in the tight, feedback-driven loop an early-stage startup needs — short cycles, real recruiter feedback folded back in continuously.

Because AI in hiring carries real fairness and compliance weight, we built it as a human-in-the-loop system from day one: the AI does the heavy lifting of screening, matching, and first-round interviewing and surfaces the evidence behind every call, but recruiters make the decisions. That principle shaped the entire architecture.

We delivered RegorTalent as a complete product across the stack — not a feature, but the platform end to end.

AI agents with guardrails and a human in the loop: screening, matching, and interview scoring are AI-driven, but every output is evidence-backed and recruiter-reviewed. We built evaluation and guardrails around the agents so their behaviour stayed consistent and defensible — non-negotiable when the domain is hiring.

Retrieval-grounded matching: candidate–role matching runs on embeddings and a vector store rather than brittle keyword rules, so it reasons about genuine fit and every ranking stays explainable instead of a black box.

Redux for data-dense state, Axios interceptors for reliability: recruiters live in candidate lists and pipelines all day, so shared state is centralised in Redux with deliberate render-performance work, and a single Axios interceptor layer handles auth and errors everywhere — a big part of why post-deployment issues dropped around 20%.

Reusable components as a debt strategy: a shared component system cut the front-end codebase roughly 25%, which meant fewer regressions and faster, safer releases for a team shipping weekly.

Cloud built for an always-on pipeline: containerised and autoscaled so hiring load never takes the platform down, with CI/CD and monitoring that made frequent releases routine rather than risky.

Support wired into engineering: the Atlassian-integrated support system cut resolution time 30% and support costs 70%, while turning production signal into structured backlog input.

RegorTalent went from idea to a full, production hiring platform — one where AI screens and interviews at scale, recruiters make the calls on solid evidence, and the whole system runs reliably on cloud infrastructure Brynex built and operates.

Having a single partner own the front end, backend, AI, and infrastructure meant the product moved as one: faster screening, consistent first-round interviews, fewer bugs in production, and a support loop that tightened over time — the difference between a demo and a platform a company can actually hire on.