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Artificial Intelligence software.

Focused integrations, operational tools, and compliance systems for the standards shaping artificial intelligence.

Two different engineering problems, one label.

Two very different engineering problems get lumped under “AI software”: building a bounded agent that reliably automates one specific B2B workflow, and running large language models on infrastructure that never leaves EU jurisdiction. Both require discipline generic AI tooling skips — explicit tool permissions, retrieval that respects existing authorization boundaries, and evaluation against the actual task rather than a generic benchmark.

Sovereign deployment has become its own requirement as customers in regulated sectors — finance, healthcare, public administration — rule out US-hosted model APIs entirely, whether for GDPR transfer risk, the EU AI Act's data governance obligations, or plain procurement policy. Self-hosting an open-weight model changes the latency, cost and evaluation tradeoffs; it doesn't remove the need for the same guardrails a hosted agent needs.

A delivery path your team can inspect.

Artificial Intelligence should end in a system people can operate, not a document that creates another translation step.

01

Make the requirement testable.

Turn source text, technical standards, and deadlines into explicit product decisions.

02

Fit the systems already in place.

Map owners, data sources, approvals, and failure paths before choosing the software shape.

03

Ship evidence with the feature.

Include the operational trail, documentation, and maintainable architecture the requirement demands.

Bring us your current system and deadline.

If artificial intelligence software is on your roadmap, describe what you run today and where the requirement gets stuck. An engineer will reply with a concrete first step within one business day.