Most AI initiatives fail not at model quality but at integration. Data isn't prepared, sources aren't traceable, responsibilities aren't defined. We start where value is actually created.
Six focus areas.
Retrieval-Augmented Generation
RAG over company knowledge. pgvector, re-ranking, source citations — no hallucinated answers.
LLM integration
OpenAI, Anthropic, open source. We pick the model by use case, not hype, with fallback layers built in.
Multi-agent systems
Orchestrated agents for complex workflows. With deterministic guardrails and human in the loop.
AI strategy
Roadmap, governance, EU AI Act. What may be automated — and what stays deliberately human?
MLOps
Operations, not prototypes. Monitoring, drift detection, eval frameworks — and a clear update path for models.
Data engineering
Clean pipelines are a prerequisite, not a by-catch. We invest in the data foundation before training models.
How we work.
Discovery
We understand the use case before picking models. What's the bottleneck? What's the honest success metric?
Prototype
Fast focused spike — with real data, not synthetic. That tells us whether the idea holds.
Hardening
Eval framework, guardrails, monitoring. Only when the system runs reliably does it move to production.
Operations
We stay on it. Model drift, new data, new requirements — AI is not a delivery date but an ongoing contract.
What we build with.
See how we implemented it.
Theory is cheap. Here's a concrete mandate where this service was delivered into production.
30-minute intro call.
30-minute intro call. Confidential, free. We listen and tell you honestly whether and how we can help.