Anonymized - delivery portfolio
Moving AI prototypes into reliable internal and customer-facing production
Working prototypes repeatedly stalled before real use because they lacked live integrations, measured quality, access controls, observability, failure handling, and an owner for production operation.
Representative anonymized engagement. This page describes a repeated production delivery pattern across anonymized operational work. The task-success metric comes from one measured rollout. The figures are not a promise of future results.
The problem
The prototypes proved that a model could complete the intended task on selected examples. They did not prove that the surrounding system could support daily use. Live data arrived in different shapes, users had different permissions, integrations failed, and the team had no agreed threshold for releasing a new prompt or model.
In that state, every failed run returned to the person who built the PoC. Internal users stopped trusting the result, and external rollout remained too risky.
The production gap
We reviewed each prototype against the workflow it was meant to support. The review covered data access, integrations, known failure cases, model and tool behavior, latency, operating cost, and the people responsible for exceptions.
The production build added role-based permissions, tested integration contracts, evaluation datasets, traces, alerts, fallbacks, release checks, and an incident owner. Where the system could affect a customer or business record, the first release kept a clear human approval step.
Rollout
The first users were internal operators who already understood the workflow. Their corrections became evaluation cases and helped the team separate model errors from weak source data or missing business rules.
External access followed after the system met the agreed release threshold and the support path worked under live conditions. Across the portfolio, the first production release usually took two to five weeks. More than 30 prototypes moved through this process.
How the result was measured
For one selected rollout, the pilot completed 74% of representative tasks successfully. After the production rebuild and controlled iteration, the same evaluation set reached a 98.4% task success rate. The team kept the evaluation set as a release gate for later model, prompt, and integration changes.
The broader portfolio used the same measurement discipline, but the acceptance threshold and business metric differed by workflow.
Read more about the PoC to production service.