Architecture and integration

Who it's for

Technology leaders, enterprise architects, IT leadership and data teams at large organizations, mid-size companies and public bodies who must choose, justify or review an AI architecture; SMBs starting a structuring project.

Typical problem

“Three vendors are proposing three different solutions, our proof of concept doesn't pass the enterprise architecture review, and nobody can justify the costs.”

What we do

• Reference architecture: RAG, agents, model gateway, guardrails and logging.
• Choice of models and hosting, compared in writing: cloud (Azure AI Foundry, Azure OpenAI, AWS Bedrock) or local and sovereign, depending on data sensitivity.
• Integration with enterprise systems: Microsoft 365 and Copilot, ERP and CRM, data platforms.
• Security and identity: Entra ID, managed identities, private networking, least-privilege access.
• Non-functional requirements: operating costs (FinOps), latency, availability, observability.
• Architecture dossier, architecture decision records (ADRs) and architecture review of an existing AI project.
• Proof of concept on your data when the choice needs to be validated.

Deliverables

• Architecture dossier: C4 diagrams, components, data flows, security and identity.
• Architecture decision records (ADRs).
• Written comparison of options: models, hosting, estimated operating costs.
• Criteria for evaluating answer quality.
• Architecture review report and prioritized recommendations, for an existing project.
• Proof of concept, if applicable.

Who it's for

Data and development teams at large organizations, mid-size companies and public bodies building document assistants and agents; organizations that want a first build handed over to their own team.

Typical problem

“Our proof of concept answers well in demos, but sometimes makes things up, and nobody knows how to measure or maintain it.”

What we do

• Design and development of document assistants (RAG): ingestion, chunking, hybrid search, citations, an “I don't know” answer.
• Design and development of agents with limited tools, with human approval on actions that have effects.
• Evaluation and test benches: quality, hallucinations, citation accuracy, regressions on every change.
• Frameworks suited to your environment: LangGraph, Semantic Kernel, MCP, Copilot Studio extensions.
• Hardening against prompt injection and red teaming before go-live.
• Production deployment and handover to your internal team: code, documentation, training.

Deliverables

• Working RAG application or agent, with code and documentation handed over to your team.
• Test bench (question set, criteria, acceptance thresholds) and evaluation report.
• Hardening and red teaming report.
• Operations guide and handover sessions.

Who it's for

Organizations that don't want one more tool, but AI inside the software they already use.

Typical problem

“Our staff spend their day juggling email, our management software and Excel files. Add one more tool and nobody will use it.”

What we do

• Mapping of systems and integration points: APIs, databases, files.
• Connecting AI to your tools, with access limited to what is strictly necessary.
• Use of open protocols such as MCP (Model Context Protocol) where relevant.
• Testing with your teams before go-live.

Deliverables

• Working integration in your systems.
• Technical documentation and user guide.
• Matrix of the access granted to the AI.

Who it's for

Teams that have a working proof of concept and need to run it reliably, from SMBs to the data and platform teams of large organizations.

Typical problem

“The demo worked well. In production, the answers change, costs go up, and nobody knows why.”

What we do

• CI/CD pipeline with quality gates: evaluation sets run on every prompt, model or data change.
• Versioning of prompts, models and data, with rollback.
• Monitoring of quality, latency, operating costs (FinOps) and errors.
• Infrastructure as code (Bicep, Terraform) and reproducible environments.
• Operating procedures, periodic red teaming and handover to the operations team.

Deliverables

• Documented deployment pipeline, with quality gates.
• Infrastructure as code.
• Monitoring dashboard: quality, costs, latency.
• Evaluation set and acceptance thresholds.
• Operations guide.