Enterprise AI Deployment & Productionization
The deployment gap is where enterprise AI goes to die: the pilot impressed everyone, and eighteen months later it's still a pilot. We take the prototype — yours or ours — and turn it into a secure, observable, production-grade system that passes review and holds under load.
MIT's finding made the rounds for a reason: the overwhelming majority of enterprise AI initiatives never reach production value. Not because the models fail — because the last mile is unglamorous engineering nobody scoped: evals that prove quality, security architecture that survives review, observability that catches silent degradation, and integration with the systems of record where the actual work lives.
That last mile is our entire business. We arrive when the demo works and the deployment doesn't, and we do the hardening: eval suites built from your real cases, guardrails and human gates where risk demands them, tracing on every model call and tool use, and the compliance evidence that turns your security team from blockers into approvers.
Deployment-gap audit
Week one: what stands between this prototype and production — enumerated, prioritized, and priced in engineering days, not discovery quarters.
Harden the core
Error handling, retries, idempotency, state management — the prototype's happy path becomes an engineered system.
Prove the quality
Golden datasets, regression gates, and production sampling — quality as a number that gates release.
Clear the review
Threat model, access controls, audit trails, and evidence — security review as a formality, not a funeral.
The system your organization already believes in, finally doing real work at real scale.
What launches Friday is still running, governed, and observable on Monday — Kubernetes-grade foundations underneath.
Security and compliance evidence generated by the system itself.
Our pilot worked but stalled before launch. Typical?
It's the single most common story in enterprise AI. Pilots are scoped to impress; production is scoped to survive. The gap — evals, security, observability, integration — is real engineering, and it's precisely scoped: we usually put a stalled pilot into production in four to eight weeks.
Can you productionize something another vendor built?
Yes, frequently. We audit what exists, keep what's sound, and harden what isn't. No rip-and-replace theater — the goal is your system live, not our architecture aesthetic vindicated.
What does 'production-grade' actually mean for AI?
Quality proven by evals and monitored continuously; failures handled and retried safely; access governed and audited; costs tracked per request; and rollback rehearsed. The same bar as any production system — plus the model-specific machinery most teams don't know they need yet.
Ready to skip the kickoff theater and ship?
Tell us about the AI initiative your last three vendors couldn’t close. We’ll scope the outcome on a short call.
Book a deployment →