AI Transformation
Most AI pilots stall somewhere between a promising demo and something a business can depend on. We start with your workflows, find the places AI genuinely earns its keep, and build those into systems your team can run and your auditors can accept.
01
We map the real workflows and the data behind them, then rank opportunities by payoff against effort. Some honestly come back as “not yet”.
02
One narrow use case, built properly, with evals and a cost model — so what you get is a decision, not an impression.
03
Into the systems people already use, with the access control and audit trail that keep it there. Your team is trained to run it.
What we build
Flagship · AI agent platform
One agent. Every part of your enterprise.
Embedded in your product, or standalone across your org — handling support, operations and transactions with the isolation, audit trail and access control enterprise deployments demand.
Video consultation and identity verification, without the friction.
AI-powered remote onboarding that customers actually finish.
Warehouse management with predictive analytics built in.
Industries
Sectors
Company size
Same engineering standard at every size — from one team shipping weekly to a rollout through procurement, security review, and systems that predate us.
Expertise
Production LLM features — retrieval, evals, guardrails, and a cost model that survives real traffic. Built to be measured, not demoed.
Dashboards people actually open, on numbers that reconcile.
Ingest, model, and serve — from source of truth to query in seconds.
Single sign-on, federation, and least-privilege access that survives an audit.
Threat modelling, hardening, and monitoring — designed in, not bolted on.
How we ship
Agree what the problem is, what constrains it, and what “working” actually means.
A working build in days. Seeing something real settles questions a document can't.
Types, tests, evals, threat model. Where AI-assisted output earns production trust.
Small changes released daily behind feature flags, instead of one risky launch day.
Observability, cost, and drift. AI systems fail quietly, so we measure rather than assume.
Academy
Taught in-house by the engineers who ship our products, on real systems rather than exercises. Alongside the fundamentals, that now means learning to review and verify AI-assisted work — the judgment that makes fast output dependable. It's also how we hire.
Testing that catches what matters, evals for AI features included.
Ship a feature end to end: schema, API, interface, deploy.
Pipelines, observability, and the on-call habits that go with them.