AI integration for businesses: from prototype to production
A prototype does not survive production. We ship what does.
Many companies already have an AI prototype. It performs well in a demo, then stalls as soon as it meets real documents, real users and real security constraints. Moving to production is the stage where most projects stop.
Valtieri integrates AI into your tools and processes so that it lasts. We build agents and retrieval-augmented generation (RAG) systems connected to your internal repositories: procedures, contracts, document bases, business data. Every answer is grounded in a source you can verify.
The choice of model stays open. It depends on how sensitive your data is, the hosting you require and the cost of usage, not on a commercial partnership. The engagement lasts 4 to 6 weeks, with a budget confirmed in writing at scoping.
What you get
- An AI system in production, connected to your repositories and existing tools.
- A document indexing pipeline that keeps sources up to date and respects the original access rights.
- An observability dashboard: answer quality, latency, usage and cost per use case.
- Documented guardrails: input and output filtering, logging, human review checkpoints.
- An evaluation set built from your real cases, to measure every change before it is deployed.
- Operating documentation and a handover to your teams, or managed operations.
How it runs
Scoping
We select a use case with clear value, identify the useful data and its access rights, then set in writing the scope, success criteria and budget.
Build
We connect the sources, choose the model that fits your hosting constraints and assemble the system with its guardrails from day one.
Evaluation
The system is tested on real questions from your teams. Wrong or poorly sourced answers are corrected before any user gets access.
Go-live and handover
Progressive rollout, active monitoring, training of internal champions. Your teams take over operations, or Valtieri keeps running them under contract.
Frequently asked
Is our data used to train an external model?
No. The system queries your documents at the time of the question, without retraining any model. The choice of provider and hosting depends on your confidentiality requirements, including hosting in Europe or in your own infrastructure.
What happens if the AI gives a wrong answer?
Every answer cites its sources so that it can be verified. Sensitive cases go through human review, and the evaluation set detects any degradation before it reaches users.
Do we need an in-house data team?
No. You need a business lead available to validate answers and a technical contact for access. Everything else is handled during the engagement.
Further reading
In the same practice area : AI and automation
- Managed AI agents
Your agents in production, monitored and under control.
- Process automation
Repetitive work handed back to the machines.
- AI governance
Know which AI tools run in your organisation, and under which rules.
- AI training and adoption
Teams that use AI well, and safely.
- AI assistant for your customers
An assistant that answers accurately, and knows when to hand over.
- AI systems security
Your agents and chatbots tested the way an attacker would test them.
Let us talk about your context.
Thirty minutes to qualify the need. The written scope follows within fourteen working days.
Request a call