AI production-readiness audit
A fixed-price, fixed-scope audit of an LLM feature or agent — for teams whose AI works in the demo and needs to hold up in front of real users and real data.
Last updated:An AI production-readiness audit is a fixed-scope assessment of an LLM feature or agent against production reality: evaluation coverage, guardrails, prompt and tool design, model choice, cost and latency, and the failure modes that surface once real users arrive. Revenant Systems delivers a severity-ranked findings report with a hardening roadmap — at a fixed price agreed up front.
Who is the audit for?
The audit fits teams that have built an LLM feature or agent — in-house or with a contractor — and need it to survive contact with production: a launch approaching, output quality wobbling, costs drifting, or no clear way to tell whether a model change makes things better or worse.
- A working LLM feature or agent, pre- or post-launch
- No dedicated AI-infrastructure experience in-house
- Quality, cost, or safety questions without clear answers
What does the audit assess?
The audit assesses the six areas where LLM features fail in production: evaluation coverage (whether quality is measured at all), guardrails and failure handling, prompt and tool design, model selection with its cost and latency profile, prompt-injection and data-exposure risk, and production monitoring. Each is judged against the feature's actual traffic and stakes.
Revenant Systems builds and runs LLM features across Anthropic, OpenAI, Google, and OpenRouter models — and local models where data residency or cost demands — so the assessment reflects how these systems behave in production, not in a benchmark.
- Evaluation coverage and regression protection
- Guardrails and failure modes
- Prompt, tool, and agent design
- Model choice, cost, and latency
- Prompt-injection and data-exposure risk
- Production monitoring
What you receive
- A written findings report — severity-ranked, each issue with its production impact and a concrete recommendation
- A hardening roadmap — quick wins vs structural changes
- A “further investigation recommended” section for anything material outside the fixed scope
- A findings walkthrough call
How the engagement runs
- Typical turnaround: two weeks from start to report
- Fixed scope and fixed price, agreed before work starts
What we need from you
- Read access to the feature's code and prompts — or a guided walkthrough where access is restricted
- Sample inputs and outputs, including known failures
- Model and provider configuration, with usage or cost data where available
- A short walkthrough with the engineer who owns the feature
Access and client data are handled under our information security statement.
Frequently asked questions
Is the price fixed here too?
Yes — one scope, one price, agreed before the work begins. Nothing about the engagement is metered.
What if the audit finds issues outside its scope?
Material findings beyond the agreed scope go in the report under further investigation — described, weighted, and ready to be scoped separately if you choose to pursue them.
Can you do the hardening work as well?
Yes, as separately scoped follow-on work — evals, guardrails, or a redesign where the audit calls for one. The audit stands alone regardless: the roadmap is written for your own engineers to execute.
Each package is an audit or assessment offered on its own — the process behind it is covered in how we work.