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PoC to production AI systems
We turn a working AI prototype into a secure, observable system that can support internal teams and external users every day.
Built forInnovation leadersTransformation teamsCTOs and technical leadsProduct owners
A production system with measured quality, controlled access, live integrations, clear ownership, and a rollout plan.
Why a successful PoC can still fail in production
A PoC usually runs on selected examples, with an engineer close enough to fix each failed attempt. It may use copied data, broad permissions, and manual steps that are acceptable during a test. None of that survives contact with daily operations.
Production means real records, concurrent users, access controls, integration failures, model changes, and support expectations. A system can look accurate in a demo and still be unsafe or unreliable when the input changes. The gap is engineering work, but it is also operating work. Someone needs to own quality, exceptions, releases, and user adoption.
What we do
We audit the prototype against the workflow it is expected to support. That gives us a concrete production gap: what must be rebuilt, what must connect to existing infrastructure, how quality will be measured, and which decisions must remain with a person.
The system then gets the parts a PoC rarely has. These include role-based access, integration contracts, evaluation datasets, traces, alerts, fallbacks, and a controlled release process. We test it with real operating cases before widening access.
Who should consider this service
This service is for a team that already has a working prototype or pilot and now needs a reliable route to daily use. The sponsor is often an innovation or transformation leader. The technical owner is usually a CTO, technical lead, platform owner, or product engineering team that will be accountable after launch.
It is also a fit when the prototype came from an external experiment and the internal team needs to understand, own, and maintain the production version.
What goes into production
- A documented architecture with data and permission boundaries
- Integrations with the systems that hold the live workflow
- Evaluation cases based on real tasks, including failure cases
- Monitoring for quality, latency, cost, and tool failures
- Human review for decisions that carry operational or customer risk
- Release, rollback, and incident ownership
How we measure the move from pilot to production
We agree the acceptance criteria before rebuilding. The measurement normally covers task success, unsafe or unsupported outputs, response time, operating cost, exception rate, and adoption by the intended users. The system is ready when it meets those thresholds on real cases, not when a single demo looks convincing.
The first release usually goes to a controlled internal group. External access follows after the team has evidence that the workflow, recovery path, and support process work under live conditions.
Related production work
Our PoC to production success story covers a repeated delivery pattern across internal and customer-facing systems. We added integrations, evaluations, observability, security controls, and staged rollout paths before real users depended on the system.
Start with the work
Bring the workflow that needs attention.
Pick a time for a working session or send a short brief. Either way, we will come prepared to understand where the work gets stuck.
Talk through the work
Book a 20-minute consultation.
Bring the workflow that feels slow or fragile. We will determine whether it is a sensible candidate for an AI system.
Bartosz LuderaFounder, HarnessloopChoose a time for a 20-minute consultation.
Send a workflow brief
Prefer to write it down?
Tell us where work waits, repeats, or falls through the cracks.