Production AI is an engineering discipline, not a demo.
Most AI pilots die between the notebook and the org chart. Ours ship because they are built like software: versioned, evaluated, permissioned, observable — and owned by a team that stays.
Knowledge & retrieval systems
Search and question-answering over contracts, tickets, wikis and drawings — with the permission model of the source systems enforced at query time. Citations mandatory; hallucination measured, not hoped away.
Agentic process automation
Multi-step back-office flows — claims, onboarding, reconciliation — run by agents inside explicit guardrails, with human approval where money or risk moves. Full audit trail per action.
Internal copilots
Role-specific assistants for operations, support and engineering teams, wired into the tools where work already happens. Adoption is a design requirement, so we instrument it from day one.
Evaluation & model ops
The infrastructure that keeps AI honest: golden datasets, regression evals in CI, drift monitoring, cost and latency budgets. Often the first thing we build — and the reason the rest survives.
An AI-assisted delivery loop, gated by engineers.
Spec compression
Requirements, existing code and domain docs become an executable specification in days, not sprints. Ambiguity surfaces before anyone writes code.
Assisted implementation
Agentic tooling handles migrations, test scaffolding and boilerplate under a senior engineer's architecture. Throughput rises where work is mechanical, not where it is judgement.
Review gates
Nothing generated reaches production unreviewed. Static analysis, eval suites and human sign-off gate every merge — the same bar for machine-written code as for ours.