# AI proposes, deterministic logic disposes: the pattern that keeps AI production-ready > Source: > Published: 2026-10-02 10:30:00+00:00 **TL;DR** AI in production does not decide. It proposes, to a system that has the right to say no. Every AI system I have put into production shares this same three-layer pattern. **AI proposes.** OCR, a language model, extraction: it reads the document or the input data, detects, extracts, and attaches a confidence score to every proposal it makes. It never writes directly to the database. **Business rules validate.** A deterministic layer, made of ordinary code, testable and auditable, checks every proposal the AI makes: expected formats, consistency against existing reference data, business thresholds. What passes these checks gets accepted. What fails is blocked before it reaches the system. **A human decides.** A confidence score that is too low, a business rule that gets violated, a case never seen before: the proposal goes into a validation queue, and a person decides. Every human decision is stored and fed back into the system, which improves with use this way, without heavy model retraining. In this architecture, a model's hallucination is no longer a diffuse, uncontrollable risk. It is an explicitly handled case. An AI that gets something wrong becomes a proposal rejected by the validation layer, never wrong data reaching a production database directly. This is exactly how [the document pipeline](https://gilabs.fr/en/blog/pipeline-idp-production/) I delivered in a demanding industrial context runs day to day. It holds up over time not because the AI never makes a mistake, but because its mistakes structurally have nowhere to go without passing through a check. In a system like this, the part actually occupied by artificial intelligence is small. Everything that makes the system trustworthy comes down to ordinary software engineering: tested .NET code, a structured SQL database, explicit business rules, automated tests. That is what I build in a [.NET AI integration](https://gilabs.fr/en/services/dotnet-ai-integration/): AI inside the existing code, under its rules. I am often asked which model I use for a given project. That is almost never the most useful question. The question that actually matters is: what happens when this AI gets it wrong? Sometimes the answer allows full automation, as on [the Convention Online AI chain](https://gilabs.fr/en/blog/convention-online-ia-autonome/), where a mistake is fixed on the next pass. If the answer is not clear before the first deployment, the system is not yet ready for production. *Originally published on [gilabs.fr](https://gilabs.fr/en/blog/ia-propose-deterministe-dispose/).* *I'm Yann Gilliot, founder of [GiLabs](https://gilabs.fr/en/). GiLabs helps SMEs, mid-caps and professional firms succeed in their AI transformation: process mapping, a costed roadmap, then building the agents and applications that run in production, wired into what you already have.*