Short answer
Taking an AI pilot to production means turning a demo that works on chosen examples into a system that holds up on real traffic, can be changed safely, and has an owner. In practice that is the engineering a pilot usually skips: an offline eval set and a regression gate, so changes are scored before they ship; online evals and tracing, so quality, cost and latency are visible on live traffic; guardrails and data handling that pass a security review; and a runbook with a named owner.
VenianAI engages around one number. Before any code, the metric the system should move, its baseline and the target are written into the statement of work, and the baseline is measured in week one on your data. There are four engagements: an Eval Audit, a Regression Gate, a Pilot-to-Production Sprint and a Reliability Retainer. Each is fixed in scope and in price, quoted after the scoping call.
Not the model. The same model sits under both. What differs is whether anyone can say how well it works, change it without breaking it, and notice when it starts failing.
| Pilot | Production | |
|---|---|---|
| Inputs | Examples someone chose, often by the person who built it. | Real traffic, including the inputs nobody planned for. |
| Quality | It looked right in the demo. | A pass rate on a graded eval set, against a baseline. |
| Changes | Prompts edited by hand and checked by eye. | Every prompt, model and retrieval change scored in CI before merge. |
| Visibility | None until someone complains. | Traces, online evals, cost and latency per task. |
| Failure | Someone notices, eventually. | An alert, a runbook, and a person who is paged. |
| Ownership | Whoever built it, while they still have time. | A named owner, with the system handed over in your repositories. |
Three things, in writing, in the statement of work. They are what the engagement is judged on, so they are settled before anything is built.
At the end, the system is judged against that number and nothing else. If it falls short, you get the measured gap and the reasons in writing, and the decision to continue, change scope or stop stays with you.
Four engagements, in the order most teams move through them: measure, gate, ship, keep it good. Each is fixed in scope and fixed in price, quoted after the scoping call.
2 weeks
For a team with an LLM feature in or near production and no quality signal it trusts.
What you get
Outcome
You know how good the system is today, where it fails, and what to gate on.
1 to 2 weeks
For teams that change prompts or models every week.
What you get
Outcome
A bad prompt or a model swap fails the build instead of reaching users.
6 to 8 weeks
For a stalled pilot, or a new agent or RAG use case with a named metric.
What you get
Outcome
A go or no-go at week six, judged against the number agreed in week one.
Monthly, 3-month minimum
For systems already in production.
What you get
Outcome
Quality holds as models, data and traffic change underneath it.
Four stages, six steps. The step that decides the rest is 02: the number is agreed before the build starts, not reverse-engineered from whatever got built.
Stage 1
01Discover
Map the process, find where the cost and delay actually sit.
02Model ROI
Agree the metric, the baseline, and the target. In writing, before we build.
Eval Audit, or week one of the Sprint
Stage 2
03Prototype
Narrowest version that can move the number. Evaluated, not demoed.
Pilot-to-Production Sprint
Stage 3
04Production
Integrations, guardrails, observability, security review, handover.
Sprint, with the Regression Gate
Stage 4
05Measure
Report against the week-one number. Monthly, whether or not it flatters us.
06Optimize
Cut cost, raise quality, widen scope. The system gets cheaper as it ages.
Reliability Retainer, or your team
The same five things, every week, so progress is something you read rather than something you are told about.
A written update
What shipped, what is next, and what changed in the plan and why. Short enough to forward.
The scorecard
Where the agreed metric sits against the week-one baseline, measured the same way every week.
The eval diff
For every change merged that week, its effect on the eval set, case by case.
Risks and decisions
What is blocked, what we need from you, and the date it is needed by.
A working session
The system running on your real data, not a slide about it.
Five things, named up front, because any one of them arriving late stalls a sprint.
One person who will run the system after handover, involved from week one rather than introduced at the end.
Read access to traces, logs and the data the system uses, plus the repositories and environments it will run in.
A few hours a week from someone who knows what a correct answer looks like. They label the cases the evals are calibrated on.
Someone who can sign off the metric and target at scoping and make the go or no-go call at the end.
Whoever reviews data handling, introduced in the first week, so the review is a checkpoint and not a surprise at launch.
Each engagement is fixed in scope and fixed in price. The price is quoted after the scoping call, once the metric, the system and the data are known, and it is written into the statement of work next to the deliverables.
Yes, and it is the usual first step. The Eval Audit stands alone: the failure taxonomy, the golden set, the evaluators and the baseline scorecard are yours whether or not anything follows. The baseline is then what decides whether a Sprint or a Regression Gate is the next move.
The go or no-go is judged against the number agreed in week one, measured the way the statement of work says it will be. If the target is not met, you get the measured gap and the reasons for it in writing, and the decision to continue, change scope or stop is yours.
You do. The code, the eval set, the evaluators, the CI configuration, the dashboards and the runbook live in your repositories and accounts from the first commit, and handover is to a named owner on your team.
Yes. We build on the models, cloud and tooling you already run, whether that is OpenAI, Anthropic or a model served on your own infrastructure, and add only what the system needs to be measured and monitored.
A 30-minute call. We look at the pilot or the use case, name the metric it should move, and tell you which engagement fits, or that none does.
hello@venian.aiReply within one business day, from an engineer.
What happens next
A reply within one business day
From an engineer who would work on it, not a sales team.
A 30-minute call
We map one process, name the metric it should move, and tell you plainly whether AI is the right tool for it.
A written scope
If it is, a one-page scope: the metric, how we baseline it, the target, and which engagement fits.