The problem
A promising LLM or agentic prototype works in a notebook and stalls before production. No deployment path, no evals, no guardrails, no cost ceiling, and no one who owns it on call. The model was never the hard part. The infrastructure, governance, and ownership around it are.
of respondents have moved 40% or more of their AI pilots into production. The gap between a working prototype and production is where initiatives stall.
The approach
A fixed-scope audit of the stalled system against a production-readiness bar (deployment, evaluation, observability, guardrails, cost, and ownership), followed by a scoped buildout that closes the gaps. Delivered by the founder and machine-verified, with a mandatory production-safety gate before anything ships.
The standard we assess against: The Production-Readiness Bar →
Engagement
Fixed-scope audit first (1–2 weeks), then a scoped implementation statement of work. The audit stands alone; you can take the findings and run.
What's delivered
- Production-readiness assessment scored against a concrete rubric, with prioritized risk register
- Deployment pipeline that is reproducible and gated, with a rollback path (no click-ops, no long-lived keys)
- Evaluation harness + observability so regressions are caught before users are
- Guardrails: input/output controls and policy-as-code, with a human in the loop where it matters
- Cost controls: budgets, per-feature spend visibility, and a ceiling that pages before it bankrupts
- A written ownership model: who runs this, what they watch, what wakes them up
The outcome
A previously stalled system running in production, owned by a named person, with a safety story you can defend and a spend curve that finance signed off on.
In practice
What this looks like.
AI Production-Readiness Scorecard
SampleA sample scoring of where a typical stalled LLM or agentic POC sits against the six-dimension production bar before any buildout begins.
- Deployment Lives in a notebook or one operator's machine. No reproducible build, no gated pipeline, no rollback path; shipping means manual click-ops.
- Evaluation Quality judged by eyeballing a handful of prompts. No eval set and no regression suite, so a prompt or model change ships blind.
- Observability Application logs only. Prompts, tool calls, and token usage aren't traced, so a failure can't be reconstructed after the fact.
- Guardrails Behavior steered by the system prompt alone. No enforced input/output validation, no policy-as-code, no human approval on consequential actions.
- Cost controls Provider dashboard shows a running total. No per-feature attribution and no budget ceiling that pages, so spend is understood only when the bill arrives.
- Ownership A single champion understands the system. No named on-call owner and no runbook defining what to watch or what should wake someone up.
Situation. A fintech has an agentic assistant that drafts customer dispute responses and reads from internal transaction systems. It demos well to leadership but has stalled before launch: it runs from a developer's environment, has no deployment path, and security will not approve an agent that touches account data without enforced controls.
Path
- 01 Audit the prototype against the readiness bar (deployment, evaluation, observability, guardrails, cost, ownership) and rank the gaps by what actually blocks launch.
- 02 Stand up a reproducible, gated deployment pipeline with scoped IAM and no long-lived keys, so a release is repeatable and rollback-safe.
- 03 Wrap the agent in policy-as-code and input/output validation, with a human approval step required before any action that moves money or alters an account.
- 04 Add an evaluation harness plus tracing for prompts, tool calls, and spend, so regressions and cost spikes surface before customers do.
- 05 Write the ownership model (named on-call, what they watch, what pages them) and hand it to the team that will run it.
Shape of outcome. The assistant moves from a laptop demo to a governed, observable deployment: every consequential action passes an enforced policy gate, regressions surface in evals rather than in production, spend becomes attributable per feature, and a named owner runs it on call.
Representative: illustrates the method, not a specific client.
Think this is your situation?
Request an audit. You'll hear back from the person who'd do the work.