Capability03.08Foundation
AI Infrastructure
What every system stands on: your data, your models, your rules. Private, measured, governed.
Every intelligent system stands on the same foundation, its AI infrastructure: where the data lives, which models run, who can see what, and how you know it still works. We build that foundation privately, inside your own infrastructure where it needs to be, with retrieval that cites its sources and evaluation that runs on every change.
Retrieval runs in your browser, over this page’s own text. Every passage is quoted exactly as it appears here, with where to find it.
Live view is off on this device.
Includes
- LLM integration and model selection
- Retrieval (RAG) and knowledge bases
- Private and on-premise AI
- Evaluation, testing and observability
- Security, access control and governance, including EU AI Act readiness support
- Data pipelines
Applications
- Private retrieval over internal documents, where every answer cites its source
- An evaluation suite that gates every model or prompt change
- Pipelines that make years of documents searchable
Under the hood
- Hybrid retrieval: BM25 and dense vectors
- Re-ranking and paragraph-level citations
- Open-weight models served privately
- Evaluation gates on every change
- Tracing and observability for model calls
- Document-level access control
How we test it
Retrieval is measured against questions your teams write, each with the passages that should answer it: recall at k, citation accuracy, and how often the system correctly declines when sources are missing or disagree. Evaluation runs on every model, prompt or data change, and a release that scores worse does not ship.
Have something like this in mind?
Bring the problem in its messiest form. Measuring it is our job. We’ll tell you honestly whether AI is the right way to solve it.