We build AI
that works
in production.
Not prototypes. Not demos. End-to-end AI systems — from inference architecture to enterprise deployment — for organisations that cannot afford failure.
Enterprise AI doesn’t fail in the demo.
It fails in production.
The gap is never the model — it’s the system around it. The evaluation, the guardrails, the observability, the deployment layer that separates a working demo from something an enterprise can actually run.
Omos — the production layer for enterprise AI
In build / in useMost AI pilots never survive into production. Omos is the system that takes them there — evaluation, guardrails, audit trails, and vendor-neutral model routing, deployed inside your own boundary so your data never leaves. It already runs in production beneath our own products.
Products we design, build, and operate under our own name — the same engineering discipline we bring to client engagements. See how each one is built →
The model is the easy part
Frontier models are becoming a commodity. The system around them — retrieval, evaluation, guardrails, observability — is where production is won or lost.
Demos lie. Production doesn't.
We hold every build to the standard of running unattended after we've moved on — not to the standard of looking good in a slide.
Own your intelligence
Vendor-neutral by default, deployed inside your boundary. Your data, your models, your IP — never locked to someone else's roadmap.
Discovery
We map your data, workflows, and constraints — and pinpoint where AI creates real ROI, not novelty. If the honest answer is that AI isn't the fix, we say so here.
Scope
A tight, costed plan: the proof we'll build first, the metric that defines success, and how it grows into production. No open-ended retainers.
Build
A working proof shipped fast, then hardened into a production-grade system — evaluation harness, monitoring, and rollback included, not bolted on later.
Handoff
Documentation, deployment, and knowledge transfer to your team — or an ongoing partnership if you'd rather we keep running it.
Scope your AI project
Four short questions. A structured brief, streamed live — problem, approach, stack, timeline, risks, next steps. No account required.
What stack do you work with?
We're stack-pragmatic. In practice that's TypeScript/Next.js and Python or Node services, PostgreSQL and vector stores (pgvector, Pinecone, Weaviate) for data, the major model providers (Anthropic, OpenAI, open-weight models) for the intelligence layer, and modern cloud infrastructure. We pick what fits your problem and your team, not what's trendy.
Who owns the IP?
You do. Work we build for you is yours — code, models, and data. We keep only our own pre-existing tools and generic building blocks. IP ownership is spelled out in the engagement agreement before any build starts.
How is our data handled?
Your data stays yours and is used only to deliver your project. We minimise what we touch, isolate client environments, and never train shared models on your private data. We'll align to your security and compliance requirements as part of scoping — our Security and Privacy pages describe the baseline.
What's the minimum engagement?
We start small on purpose. A Discovery plus a scoped proof-of-concept is the usual entry point — enough to prove value on a real problem before committing to a larger build. If a project isn't a fit, we tell you early.
How do we get started?
Two ways: generate a free AI Brief on this page to see how we'd approach your problem, or book a call directly. Either way, we come back with where we'd start and honest timeframes.
The full versions live on our Security, Privacy, and Terms pages — written to be read before a first call, not after a problem.