AI implementation · Granada / Remote
You already know what you want to build. This phase is writing it to production standard: integrated into your stack, with tests, automated evals, observability and cost under control. I start from how we will measure that it works and finish with an operation your team can sustain without me.
This is the service for when the decision is already made — because we came from the design phase, because a pilot proved the idea works, or simply because you are clear about it — and it needs building properly. I join your repository, your CI and your code reviews, and work with the conventions you already have.
The distance between a demo and a production system is measured in the things the demo does not do: handling a provider error at three in the morning, not burning a month of budget in an afternoon, answering in under two seconds, versioning prompts so you know what changed when quality drops, and having a test that catches it before a user does. That distance is most of the work, and it is exactly the part that almost never gets budgeted.
Inside your application if it is Rails or Python, or as a standalone service behind an API if your stack is something else. With your conventions, your code style and pull requests someone on your team can genuinely review.
A set of cases with expected outcomes that runs on every change of prompt, model or parameter. Without it, tuning a prompt is a bet; with it, it is a change with a number before and a number after.
Unit and integration tests with model calls stubbed, so the suite stays fast and deterministic, plus a separate layer of tests against the real provider. All inside your pipeline.
Traces of every operation with its prompt, response and latency; cost per operation; alerts when quality or spend drift out of range. When something breaks, you will know which step broke and with what input.
Caching where it makes sense, the small model for tasks that do not need the large one, per-user limits and streaming responses. Cost is an architectural decision and it gets made while building.
Deployment, operating documentation and a handover session with your team: how to change a prompt, how to add a case to the evals, what to look at when something goes wrong. Being able to carry on without me is part of the job.
In a production AI system, the model call is the small part. The expensive part is queues, retries, idempotency, permissions and spend control: ordinary backend engineering with one new source of uncertainty on top, and that is where my ten-plus years building APIs and systems at scale come in. In practice it comes down to three things I bring to every project. Prompts live versioned in the repository like any other code, with their history and their review. The eval suite runs in GitHub Actions on every pull request, so a change that degrades quality shows up before it is merged. And traces, latency and cost per operation are instrumented on the first deploy, before anyone needs them.
Access, environment, reading the existing code, and agreement on scope, acceptance criteria and what "done" means in this project. One week, usually limited by access approvals rather than by the work.
Short cycles with something deployable in each one and a demo every week. Evals get written alongside the code rather than at the end. The range is wide on purpose: four weeks for a bounded feature, ten or more when several integrations are involved.
Load, provider outages, edge cases, latency, budget and alerts. This is where it is decided whether the system survives a Monday morning. Two to three weeks before opening it to everyone.
Documentation, a handover session and an agreed support period for the first weeks in production. After that your team owns the system; I stay available if something comes up that needs the original context.
AI consulting for companies: find out where AI, agents and RAG actually pay off, with a senior engineer. From idea to production system, no hype.
AI solution architecture: system design, model selection, evals strategy and a technical roadmap before writing code. Grounded decisions, not hype.
Tell me about your case and I'll tell you honestly whether AI is the right tool — and how I would implement it.