Service · deep integration
AI at the core of your .NET estate
For mid-caps and software vendors whose IT lives in .NET and SQL Server: AI developed inside your application (document extraction, agents, classification) with your business rules, your architecture, your code. Not another tool next to the IT system: a new capability inside it. I work in your repo, alongside your developers, to your team's standards.
This is for you if…
- Your IT system or product rests on a .NET / SQL Server estate that AI should enrich, not bypass.
- You're aiming for a native AI capability: document extraction, classification, business assistants, directly inside the application your users already know.
- Your context is demanding: industry, banking, healthcare, regulated environments, where traceability and GDPR / AI Act compliance are non-negotiable.
- You have a technical team that wants a counterpart who can talk architecture, not just prompts.
What I deliver
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Document extraction in production
Scans and inbound documents turned into structured data: a swappable extraction engine (Azure Document Intelligence, Mistral OCR, Docling), business rules, transactional import.
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Agents integrated into the IT system
Semantic Kernel, Azure AI: agents that query your data and act inside your application, with your IT system's permissions and traceability.
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Clean architecture
Clean Architecture, DDD, tests: code your team can read, maintain and extend. Delivered in your repo, under your CI/CD.
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Full reversibility
No dependency on GiLabs: documentation, knowledge transfer, and abstractions that let you switch AI providers without a rewrite.
The process
The method of a serious software project, applied to AI, because that's what it is.
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Architecture review
Your IT system, your flows, your security and compliance constraints. We decide together where AI belongs and where it has no business being.
Deliverable: an architecture dossier validated by your technical team.
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Iterative development
Short sprints on your real data, code reviews with your team, quality measured at every iteration.
Deliverable: tested, deployable increments, in your repo.
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Production and handover
Deployment into your infrastructure, monitoring, documentation, training for the team taking over.
Deliverable: the AI capability in production, mastered by your developers.
Investment
This is the most committing tier, and the one that creates a durable asset: an AI capability your team owns, in code it masters, with no rent to pay to yet another vendor.
Investment
Fixed fee
Scoped in the architecture review, depending on integration depth. Scope, price and acceptance criteria agreed before the first line of code.
Frequently asked questions
Our developers are good. Why not build it in-house?
Often you can, and that is the right answer once the first project is behind you. What is rarely missing is .NET skill; what is missing is having already run an AI chain in production and knowing where it breaks: thresholds, error recovery, model drift, cost per document. The most common tell is a company hiring a .NET developer and a data scientist at the same time, when it is looking for one profile. I work in your repository, with your team, and the skills transfer is part of the fixed fee.
How do you work with our technical team?
In your repository, under your CI/CD, to your standards, with code review both ways. Clean Architecture, DDD and tests are not sales arguments here: they are what makes the code readable by your developers. The project starts with an architecture review that decides with them where AI belongs, and above all where it does not.
Does our data leave the company, and leave Europe?
That is an architecture decision, settled before the first line of code, not a side effect of picking a model. Depending on your constraints, processing stays on your own infrastructure, on EU-hosted services, or on US services under contractual safeguards. For regulated environments and professions bound by professional secrecy, the default is strict: processing in Europe or on premises. The traceability required by GDPR and the AI Act is designed in, not retrofitted.
Will we be locked into one model provider?
No, and that is an explicit architecture choice. Extraction and inference components sit behind abstractions: moving from Azure Document Intelligence to Mistral OCR or Docling, or from one model provider to another, happens without rewriting your business rules. That is what makes the capability last in a field where the state of the art moves every six months.