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 operational ownership are.
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. Senior-only delivery, machine-verified, with a mandatory production-safety gate before anything ships.
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: reproducible, gated, rollback-safe (no click-ops, no long-lived keys)
- Evaluation harness + observability so regressions are caught before users are
- Guardrails: input/output controls, policy-as-code, 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 in production with a named owner, a defensible safety story, 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.