Take over and harden the code
Review of the existing code, prompts, processing pipeline and dependencies. We keep what works, restructure what will not hold up under load and add tests.
The real work starts after the demo
AI POC to Production: From Prototype to Live Service
Taking over an AI POC means turning a convincing prototype into a service people use every day. We take over your code, your data and your model choices, then add what production requires: integration with your tools, access rights, evaluations, monitoring and cost control.
For
Companies whose AI prototype (assistant, agent, extraction) impresses in demos but is not in real use
Deliverables
Diagnosis and takeover plan, evaluation set, code, configuration and operations documentation
Getting started
A diagnosis of the POC’s code, data, prompts and results
Next step
Fixed-price takeover on a defined scope, or a dedicated team if the service evolves continuously
What we deliver
An AI POC that works in a demo is not enough.
A prototype assistant, agent or document extraction tool often performs well on a few hand-picked examples. In production, it faces incomplete documents, unexpected questions, users with different permissions, larger volumes and model call costs that add up.
We pick up the POC where it left off, whether it was built by your team, a vendor or a no-code tool, and take it all the way to a service that can be run, maintained and measured.
Measure response quality
Building a representative set of examples, including cases where the solution should decline to answer, and repeatable evaluations whenever the model, prompt or data changes.
Integrate the service with your tools
Authentication, enforcement of user permissions on the data accessed, connection to your ERP, CRM or product, interfaces and human review workflows.
Run it and control costs
Logging, monitoring, handling of errors and outages, usage tracking and choosing the right model for each step to keep running costs predictable.
Signs to watch for
When should you take over an AI POC?
People like the prototype, but nobody uses it
It sits apart from business tools, with no authentication and no up-to-date data. It needs to be built into users’ actual workflow.
Responses are unpredictable
Without an evaluation set, every prompt or model change can fix one case and break others. Quality needs to become measurable.
Costs or response times are drifting
Uncontrolled model calls, response times that are too long, expensive document processing: the architecture must be reviewed before scaling up.
The team that built the POC is no longer available
The code is undocumented and the technical choices are no longer explained. We make the system understandable and transferable.
From work to deliverables
How an AI POC takeover unfolds.
POC assessment
Code, data, prompts, models used, results obtained and observed limitations. You receive a list of risks and a prioritized takeover plan.
Evaluation set and success criteria
With your business teams, we define the reference examples and target thresholds, so decisions are based on measurements rather than impressions.
Productionization
Architecture, testing, security, tool integration, monitoring and documentation. Changes are delivered in stages and demonstrated.
Go-live and follow-up
Gradual rollout, monitoring of response quality, errors and usage, then improvements based on user feedback.
What your team receives
- Assessment and takeover plan
- Evaluation set and measured results
- Code, configuration and operations documentation
Delivery in practice
AI applications built and run by Etixio.
Bobby: querying our ERP data
A conversational assistant developed by Etixio to access information in its ERP from a business question, with the necessary connections and permissions.
An agent that summarizes the medical record
For a hospital, an agent explores the patient record and provides anesthesia teams with a chronological summary.
The choices that matter
Take over, rebuild or stop?
Not every POC deserves to be productionized. The assessment separates what can be taken over, what must be rebuilt, and cases where a simpler approach — traditional search or deterministic automation — is enough.
The choice of model (OpenAI, Claude, Gemini or a self-hosted model) depends on the task, data confidentiality, latency and budget. See our OpenAI, Claude and Gemini integrations, and our approach to enterprise AI solutions.
How we work together
Start with an assessment.
Share the POC code, examples of requests and documents, and what users expect. The assessment can be followed by a takeover as a fixed-price project on a defined scope, or by a dedicated team if the service needs to evolve continuously.
Frequently asked questions
AI POC takeover: your questions.
What is an AI POC?
An AI POC (proof of concept) is a prototype that shows an AI use case is feasible on a few examples, such as an assistant that answers from documents or automatic data extraction. It validates an idea, but usually includes none of the integration, security or operations needed for real-world use.
Why do so many AI POCs never reach production?
Because the difficulties appear after the demo — incomplete data, access rights, variable response quality, model call costs, monitoring — and the prototype was not designed to handle them. A takeover is precisely about adding these elements.
Can you take over a POC built by another vendor?
Yes. We start with an assessment of the code, data, prompts and results obtained. We tell you what can be kept, what must be rebuilt and the effort required to reach production.
How do you measure the quality of an AI solution?
With a set of reference examples defined with your business teams, including difficult cases and requests the solution should decline to answer. Evaluations are rerun whenever the model, prompt or data changes.
How long does it take to put an AI POC into production?
It depends on the state of the prototype, access to data and the integrations required. The initial assessment makes it possible to estimate a staged takeover plan, with a first rollout on a limited scope.
How do you control the cost of an AI solution in production?
By tracking usage, choosing the right model for each step of the processing pipeline, limiting the context sent to models and caching whatever can be cached. A more expensive model is not necessarily the best choice for every task.
Your AI POC deserves to go live.
Show us the prototype, a few usage examples and what your users expect. We will tell you what needs to be taken over to put it into service.