PoC to Production
Your pilot works in the demo and stalls before production. We take it the rest of the way in a scoped engagement: production architecture first, shadow mode beside the system you run today, then 5%, 25% and all of the traffic, with evaluation gates and monitoring at each step and a rollback that has been exercised before anyone needs it.
- Timeline
- 8-16 weeks
- Team
- architect, ML engineer, platform engineer, QA; security reviewer on demand
- Typical stack
- Your stack. Shadow and canary routing in your serving layer, an eval harness in CI, monitoring and rollback in your platform, cloud or on-prem.
What you get
- 8 to 16 week engagement
- production path with gates
- operational handoff to a named owner
Outcomes
- The pilot serves real traffic in production, having passed one evaluation set at shadow, 5%, 25% and full traffic
- An ROI case built from numbers measured at each traffic step, not from the demo
- Operational handoff: a named owner, runbooks and alerts for the production data that changes after launch
- Cost per request and behaviour measured at volume before full traffic, with the fix in scope if either moves
- A rollback that has been run at least once, with the time it took on record
What we need from you
- The pilot as it runs today: code, prompts or model weights, and the demo data it was built on
- A sample of production data, including the records the demo never saw
- The system the pilot would replace or sit beside, and the person who owns it
- Traffic figures: requests per day, peaks, and the latency the business needs
- The decision the ROI case has to support, and who signs it
Proof points
- Evaluation results at shadow, 5%, 25% and full traffic against one eval set
- Cost per request and latency at each step
- The rollback exercise record, with the time it took
- Runbooks, dashboards and alert rules handed to the named owner
Frequently asked questions
Three reasons, and they are the three buyers give us. There is no ROI case, so nobody will fund the production work. The demo ran on clean data, production data is messy, and nobody owns the pipeline when an upstream column changes. And cost and behaviour that only show up at volume: longer real prompts, retrieval doubling the context, tool choices that are not the same twice. The engagement is built around those three, in that order.
Yes. An agentic AI PoC goes through the same gates, with an evaluation set that covers multi-step, tool-calling runs rather than single answers, and a permission model for what the agent may touch in production. If you do not have an agent yet and want one built, that is the Agentic AI service. This engagement is for a pilot that already exists.
With gates, not a launch date. The model runs in shadow mode first: live inputs, outputs compared with the current system, nothing user-facing. Then 5% of traffic, then 25%, then all of it. Each step has to pass the same evaluation set before the next one opens, and the rollback is exercised before the first 5%. The post below explains which evaluation failures should block a release.
Shadow mode exists to find that out before any user does. When it happens, the fix is in scope: a data contract for the inputs that moved, a check that fails loudly the next time, and a person who owns it. What we will not do is retrain quietly and hope it holds.
A number measured at 5% and at 25% of traffic, with the cost per request beside it, written for the person who has to sign the production budget. Not a projection from the demo. If the number does not justify going further, the engagement says so at that gate, and you have spent the short end of the range rather than the long end.
Your team. The engagement ends with your team owning the code, the models and the runbooks, and knowing how to run them. If you want the operations side run for you after that, the MLOps service does it; the guide below is the long version of what that involves.
Eight to sixteen weeks, depending on complexity, with a fixed scope and a fixed price agreed before work starts. The range is wide because the data and the integration decide it, not the model. A pilot with clean inputs and one integration sits at the short end; one with a data pipeline to build and a system to replace sits at the long end.