Six engineering capabilities, from architecture to evaluation.

What an AI system needs, end to end.

Each capability stands on its own and is also part of every build: a quote-automation system, for example, uses all six.

An independent view on how your software is built.

Consulting on architecture and on the way your team develops: cloud, web and mobile architecture reviews, target architectures for AI features, and the delivery process around them, from pipelines and testing to code review and AI-assisted development.

What it covers

Architecture reviews and target architectures (cloud, web, mobile, .NET and Azure); integration patterns over ERPs and legacy systems; multi-tenant and security foundations; development process assessment: delivery pipeline, environments, testing, code review, documentation, AI-assisted development practices.

What we deliver

An architecture assessment with decisions and their trade-offs, a target architecture and migration path, a development process review with a prioritized action plan, and hands-on support of your team while the changes land.

Typical use cases

• Preparing an existing platform for AI features
• Choosing a cloud and integration architecture before a build
• Reducing lead time and defects in a .NET delivery pipeline
• Adopting AI-assisted development with review and quality gates

Stack

.NET and Azure first; cloud, web and mobile architectures; CI/CD, test automation, observability, AI coding assistants and agent tooling.

Format

2 to 10 days of assessment, fixed price; then advisory days alongside your team.

Language models put to work on your data, reliably.

Retrieval, embeddings, structured extraction and context engineering: the techniques that turn a model into a feature that answers from your documents and data, with the right model for the constraints.

What it covers

Retrieval-augmented generation (RAG): chunking, embeddings, hybrid search and reranking; structured extraction with confidence scores; prompt and context engineering; model selection across hosted APIs, open-weight and private models; fine-tuning when it is justified; MCP as the connection to tools and data.

What we deliver

Retrieval pipelines on your documents and systems, extraction and classification services, model benchmarks on your tasks, cost and latency optimization, integration into your product or internal tools.

Typical use cases

• An assistant on technical documentation, catalogues and history
• Reading and structuring incoming requests (emails, PDFs, drawings)
• Summarization and classification of documents and tickets
• Semantic search over product and customer data

Stack

Azure OpenAI and OpenAI APIs, Mistral and open-weight models served with vLLM, Azure AI Search and pgvector, Microsoft.Extensions.AI, MCP.

Format

4 to 12 weeks as a project with an evaluation set, or embedded in your team.

Agents that act through tools, with the right boundaries.

Single and multi-agent workflows that plan, call tools, hand off and ask for approval, built with the OpenAI Agents SDK and the Microsoft Agent Framework, traced and evaluated from the first version.

What it covers

Agent and task design; tool definitions and boundaries; multi-agent orchestration and handoffs; human-in-the-loop approvals; guardrails against prompt injection and unsafe actions; state and memory; tracing and observability.

What we deliver

Agent architecture, implementation with the OpenAI Agents SDK or the Microsoft Agent Framework (.NET), MCP tool connectivity, approval checkpoints, an evaluation harness for agent behaviour, staged rollout.

Typical use cases

• Quote and order follow-up agents
• Lead enrichment, scoring and reminders
• Back-office orchestration across ERP, CRM and email
• Internal engineering and operations agents

Security and governance

Action boundaries, identity-aware permissions, approval steps for writes and higher-risk actions, server exposure controls, full traceability.

Format

Use-case framing, architecture, pilot workflow, governance hardening, staged rollout.

Everything around the model that makes it a system.

Data and document pipelines, anonymization, orchestration, caching, hosting, inference infrastructure, monitoring and run: the engineering that turns a working model into a system that holds up in production, in the cloud or in a private environment.

What it covers

Ingestion and document pipelines; personal-data detection and anonymization with our own engine; orchestration and caching; deployment on public cloud, Azure France region, sovereign cloud or on-site; private model deployment and inference infrastructure; monitoring, alerting and cost control.

What we deliver

Architecture and implementation of the complete system, private or local models when the data requires it (benchmarked and hosted in France or on-site), anonymization integrated in the pipeline, operations handover or run under subscription.

Typical use cases

• A quote-automation system connected to the ERP
• A private copilot for teams working on sensitive data
• A document-processing platform with anonymization
• AI features added to an existing SaaS product

Security and governance

Hosting matched to data sensitivity, anonymization before any external model, isolation per client, encryption, audit trail. Pilots can run on our own GPU infrastructure in France.

Format

Build in 8 to 12 weeks, then run and improve under a monthly subscription.

Proof that it works, and keeps working.

Evaluation is how an AI system earns the right to ship. We build the datasets, metrics and gates that measure quality before production and watch it afterwards, so a model or prompt change is a measured decision.

What it covers

Evaluation datasets built from real cases; offline evaluation (accuracy, extraction quality, groundedness, task success); LLM-as-judge with human calibration; regression gates on prompt and model changes; online monitoring and A/B comparison; red-teaming for prompt injection and unsafe outputs; cost and latency tracking.

What we deliver

An evaluation set you own, metrics agreed with the business, an evaluation harness in your CI, dashboards, and the evidence you need for compliance and management reviews.

Typical use cases

• Deciding whether to switch models or providers
• Qualifying a system before production
• Continuous monitoring of an assistant or agent
• Documentation for AI Act and internal risk reviews

Stack

Evaluation harnesses in .NET and Python, tracing with OpenTelemetry, dashboards in your monitoring stack.

Format

2 to 4 weeks standalone, and built into every pilot and build we deliver.

Your systems, exposed to assistants and agents safely.

The Model Context Protocol is how assistants and agents reach business systems. We build MCP servers over your ERP, CRM, ticketing and document stores, with authentication, scoped permissions and audit, and we integrate the servers your partners provide.

What it covers

MCP server design and implementation; authentication and authorization; scoped, least-privilege tools; approval for write actions; rate limiting and audit; exposing internal tools to enterprise assistants and developer tools; MCP clients inside your own products; integration of third-party MCP servers.

What we deliver

Production MCP servers in .NET, deployed in your environment, documented and monitored; a tool catalogue with permissions per role; integration into your agents and assistants.

Typical use cases

• ERP and CRM tools for an agent, with approval on writes
• Company knowledge exposed to an enterprise assistant
• Internal systems made available to developers' AI tools
• Partner integrations through a shared protocol

Security and governance

Least-privilege tools, identity passed through, approval steps, server exposure controls, logging of every call.

Format

2 to 6 weeks per server or integration.

Start small, prove it, then build.

Every engagement is scoped around a business metric and leaves you with deliverables you own. Senior engineers only, with the same people from workshop to run.

Commercial automation for industrial companies, launching in 2027.

Three solutions on one multi-tenant platform, hosted in France, with client isolation for regulated customers. A limited number of early-access pilots open in 2027.

Not sure where to start? Bring the product or the process, not the stack.

The most useful first conversation covers what the system must do, the data involved, the constraints, and how success will be measured. Tooling comes after.