The engineering that gets an AI pilot into production
Centricone Technologies builds the platform underneath your models: an evaluation harness you can trust, tracing and cost reporting per request, guardrails and fallbacks, and a pipeline that makes changing a prompt or a model a release rather than an event.
The reason pilots stall is almost never the model. It is that nobody can prove a change made it better and nobody knows what it costs. We build those two things first, and the rest gets much easier.
support coverage across US and Canada time zones
of code reviewed, tested, and documented before release
It has been six weeks from launch for six months
The demo is good and the evidence is not
It performs well in front of an audience, and there is nothing scored, repeatable, or written down that would justify putting it in front of customers.
Every prompt change is a leap of faith
No scored test set, no regression check, and no way to tell an improvement from a coincidence — so changes get made and quietly reverted.
Nobody knows what it costs or when it broke
No tracing, no spend attributed to a feature, and no alert when latency or answer quality falls over between releases.
for teams with a model and no way to ship it
Centricone covers the platform around the model: evaluation, tracing and cost, guardrails and fallbacks, the deployment pipeline, and the re-evaluation cadence that keeps it honest as providers change underneath you.
Deliverables
What you get
- An evaluation harness running in CI
- Tracing, latency, and cost per request
- Guardrails, fallbacks, and rate limits
- Versioned prompts, models, and configs
- A deployment pipeline with rollback
- A scheduled regression run against provider updates
How a platform engagement runs
Read the pilot honestly
What it does, what it costs, and what evidence actually exists. Ends with a written list of what is missing before it could go in front of customers.
Build the evaluation harness
Scored cases from real usage, wired into CI, so that every later change in the engagement is measurable rather than argued.
Instrument, then guard
Tracing, cost, and latency first; guardrails, fallbacks, and limits once we can see what actually happens under real traffic.
Ship it, then keep it honest
Release pipeline, rollback, canaries, and a scheduled regression run, handed to your team with the runbooks to operate them.
When platform work is the wrong engagement
This is the engagement that gets an existing thing out of the door. It is not the one that decides what to build.
- There is no model yet. Build it first — this engagement exists to ship something that already works in a demo.
- The pilot is stalling because it was the wrong use case. No amount of platform work fixes that; start with an AI readiness assessment.
- It is one prompt in one internal tool used by four people. That deserves far less platform than this.
- The real gap is delivery practice generally rather than AI specifically. That is cloud and DevOps.
Why teams bring the pilot to Centricone
Evidence before optimisation, every time
Cost and quality reported side by side
Prompts and models versioned like code
A rollback that does not need a redeploy
The capability behind this engagement
Model selection and evaluation, retrieval, guardrails, logging, and cost control as capabilities rather than as an engagement — covered in more depth on the AI development services page.
Frequently asked questions
Not sure this is the right shape? Tell us the situation and we’ll say which engagement fits.