We build AI.
Practical AI, built to work in production.
Recohut is an AI company. We make enterprise operations agentic, turn internal data into agent-ready tools, and deploy open-weight models in customer-controlled infrastructure.
What we do
Three ways we build production AI with you.
Make operations agentic, make data agent-ready, or deploy open-weight models in infrastructure you control. Every path is grounded in architecture we operate or benchmark ourselves.
We make your enterprise operations agentic — safely.
Pick any operation in your company — work, HR, finance, compliance — and we turn it into a governed agentic workflow: it observes state, reasons over approved tools, and acts with humans in the loop. Our RecoX system is the reference implementation.
We turn your internal data into safe, agent-ready tools.
We make your databases, warehouses, and documents answerable by AI — governed by semantic contracts and permissions, with every answer cited or refused. Our RecoSearch system is the reference implementation, working inside the AI clients your team already uses.
We deploy and operate open-weight models in infrastructure you control.
For teams moving beyond managed APIs, we benchmark the model, serving runtime, precision, and accelerator against the real workload—then ship a secure OpenAI-compatible endpoint with cold-start, cache, observability, rollout, and cost controls. The application stays portable across vLLM, SGLang, TGI, and future backends.
How we build
From a useful idea to a dependable AI system.
At Recohut, we move through six evidence-based delivery gates. The technology changes with the problem; the questions we use to protect quality, safety, and business value do not.
- 01
Outcome + boundaries
Frame
Should AI be used here at all?
Evidence to move on
A measurable baseline, clear decision rights, known constraints, and a precise definition of done.
- 02
Feasibility + behavior
Prove
Can a thin slice create reliable value?
Evidence to move on
A runnable prototype, representative examples, an initial evaluation set, and an honest cost-quality read.
- 03
Workflow + failure discovery
Validate
Does it hold up with real users and systems?
Evidence to move on
Integrated user flows, edge and security cases, observable traces, and feedback from realistic usage.
- 04
Trust + control
Harden
Can people depend on it when things go wrong?
Evidence to move on
Approvals where needed, fallbacks, permissions, regression evaluations, and tested incident paths.
- 05
Integration + ownership
Launch
Can it run safely inside the business?
Evidence to move on
Production integration, release controls, service targets, named owners, and a support operating model.
- 06
Quality + economics
Scale
Is performance sustainable at real volume?
Evidence to move on
Load evidence, capacity and cost controls, continuous evaluations, and learning from live outcomes.
This is a progression, not a waterfall—we revisit earlier gates whenever the evidence changes.
Outcome first. Evidence at every gate. Ownership through scale.
Talk to Recohut about the right next step.
Tell us the operation, dataset, or model workload you want to bring into production — we'll scope the right governed, production-shaped build.