On this page
About 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.
The Challenge
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.
Our Approach
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.
What We Built
We delivered RegorTalent as a complete product across the stack — not a feature, but the platform end to end.
- ATS core — job management, candidate sourcing, a fast pipeline/kanban view, interview scheduling, and recruiter workflows, so every applicant is tracked from application to offer in one place.
- AI screening & matching — resume parsing plus semantic candidate-to-role matching over a vector store, so shortlists are built on real fit rather than keyword luck, each match ranked with the evidence behind it.
- AI-assisted interviews — an agent that runs structured first-round interviews, adapts follow-up questions to the role, and returns scored, evidence-backed evaluations for a recruiter to review.
- The web apps — responsive, high-performance recruiter and candidate interfaces in React, Redux, and Tailwind, engineered to stay smooth across data-dense pipelines and thousands of records.
- The backend & APIs — the services, data model, and integrations behind it all, with a centralised Axios API layer whose interceptors handle authentication and errors consistently across the product.
- Cloud & delivery — a containerised, autoscaling deployment with CI/CD and observability, so hiring never stops and the team could ship safely and often.
- Support tooling — an Atlassian-integrated support system that turned ad-hoc requests into a tracked pipeline feeding straight back into the roadmap.
The Engineering Decisions Behind It
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.
The Impact
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.
How we worked
- 1
Embed with the founder
We joined RegorTalent as a full-stack product partner rather than a single-layer vendor, working in short cycles with real recruiter feedback.
- 2
Ship the ATS core
Job management, candidate tracking, the pipeline view and scheduling came first, so there was a real product for the AI to work inside.
- 3
Add the agents, human in the loop
Screening, matching and interviewing arrived with evaluation and guardrails, and every output stayed recruiter-reviewed.
- 4
Build the cloud for always-on hiring
A containerised, autoscaled deployment with CI/CD and observability kept weekly releases routine.
- 5
Wire support into engineering
An Atlassian-integrated support system turned ad-hoc requests into a tracked pipeline that feeds the roadmap.
Technology
- React
- Redux
- Tailwind CSS
- TypeScript
- Node.js
- Python
- FastAPI
- PostgreSQL
- pgvector
- LangChain
- OpenAI / Claude
- AWS
- Docker
- Atlassian API
Lessons you can reuse
Keep a human in the loop
Every agent output shows its evidence and a recruiter decides. In hiring, that is not optional.
Match on meaning, not keywords
Embeddings in a vector store reason about genuine fit and keep every ranking explainable.
Centralise the plumbing
One API layer with shared interceptors for auth and errors is a big part of why post-deployment issues dropped about 20%.
Reuse components to cut debt
A shared component system cut the front-end codebase roughly 25% and made weekly releases safer.
“Brynex built RegorTalent end to end — the recruiter product, the APIs behind it, the AI that screens and interviews candidates, and the cloud it all runs on. They shipped like an in-house team, and the platform only got faster and more reliable. For an early-stage company, having one partner own the full stack was the difference.”
Questions
Does the AI make hiring decisions?
No. The agents screen, match and interview, and surface the evidence behind every call. Recruiters review that evidence and make every decision.
What AI technology does it use?
Large language models from OpenAI and Anthropic, LangChain-based agents, and embeddings in PostgreSQL with pgvector for semantic matching.
What did Brynex Labs own?
We were the end-to-end engineering partner: the recruiter and candidate apps, the backend and APIs, the AI agents, the cloud and the support tooling.
Can you build an AI product like this for us?
Yes. We build AI agents, SaaS platforms and the cloud they run on, as a single senior team.