Your AI product works in the demo. That is the easy part. Prototype-to-Production Hardening takes a working prototype or MVP — often assembled fast, sometimes AI-built — and makes it secure, stable, tested, and ready to carry real customers. We do not rewrite what already works; we close the gaps between “it works when I run it” and “it survives the people trying to break it.”
Who this is for
You have a product that demos well and real customers waiting on it. The team is small, the code moved fast, and the parts that make software safe to operate never got built because they were never the point during the demo phase. You are now facing paying users, an enterprise security review, or both — and you need the thing to hold. This is the engagement that gets you there.
What “demo-grade” actually means
Demo-grade is not an insult. It is a stage. But it is defined by what is missing, and in our experience the same list shows up again and again:
- No auth boundary — endpoints that trust the caller, or a client that enforces rules a determined user can simply skip.
- No tenancy isolation — one customer’s data reachable from another customer’s session, usually through an ID that is not scoped to the tenant.
- No tests — nothing that fails when a change breaks behavior, so every deploy is a gamble.
- No evals — no way to tell whether a prompt or model change made the AI better or worse; quality is judged by looking at a few outputs.
- No monitoring — when it breaks in production, you find out from a customer, not a dashboard.
- Secrets in source — API keys and tokens committed to the repo or baked into the client bundle.
Any one of these is enough to fail an enterprise review or cause an incident — and they usually travel together.
What we harden
Security hardening and failure testing. We red-team the AI surface against the OWASP LLM Top 10 — LLM01 Prompt Injection, LLM02 Sensitive Information Disclosure, and the rest — and, where the product uses tools or agents, the OWASP Agentic Top 10 and MITRE ATLAS techniques. We fix the trust boundary, the tenancy model, and secret handling, then re-test to prove the fix holds. Every finding carries reproducible evidence — request traces, not screenshots of a vibe.
Test coverage, an eval harness, and CI. We add tests around the behavior you cannot afford to break, build an eval harness so prompt and model changes are measured instead of guessed, and wire both into CI so a regression blocks the merge instead of reaching your users. See the LLM evals guide for how we structure the harness.
Infrastructure, monitoring, and error handling. We move secrets out of source, add structured logging and alerting on the paths that matter, and make failures degrade gracefully instead of hanging or leaking. You find out about problems from your dashboard, before your customer does.
Handover documentation. We leave a runbook: how it is deployed, what the alerts mean, how to rotate secrets, how to run the evals, and what we tested. The point is that your team can operate and change the product without us in the room.
How engagements run
When the scope is unclear, we start with an AI Product Readiness Assessment so the plan is grounded in real findings, not a guess. From there we work in fixed milestones, each with a written acceptance list agreed before we start. You see progress and evidence at every milestone boundary. Our full approach is documented in our methodology, and examples of the shape of this work are on the work page.
What “production-ready” means at handoff
We do not sell a certificate that your AI is “safe” — no one honestly can. What we deliver is a concrete, checkable acceptance list, and we hand off only when it is met:
- Authentication and authorization enforced on the server, with tenancy isolation tested across accounts.
- No secrets in source or client bundles; all secrets in a managed store with a documented rotation path.
- The AI surface tested against the OWASP LLM Top 10 (and OWASP Agentic Top 10 where agents are used), with reproducible traces for every finding and its fix.
- An eval harness that runs in CI, with a baseline recorded and regressions blocking the merge.
- Automated tests on the critical paths, green in CI on every change.
- Structured logging, error handling, and alerting on the paths that matter, mapped to NIST AI RMF functions.
- A handover runbook covering deploy, secrets, alerts, evals, and exactly what was tested — and what was not.
The last line is deliberate. We prove exactly what we tested and name what we did not, so you inherit a clear picture rather than false confidence. Related reading: the AI production readiness checklist.
Ready to scope it? Scope a hardening engagement.