Services · AI & ML

Answers from your data.

Retrieval, evaluation, private inference and MLOps — the engineering that turns a demo into a system you can run in production, with every answer traceable to its source.

  • 4areas
  • 20Services
  • EUdata residency
  • 0external calls, if you need it
The pipeline

From your documents to a cited answer

Each stage is a service you can buy alone; together they are the reference architecture behind most engagements.

upserttop-kcontextpolicyYour documentsfiles, DB, wikiChunk + embedbatch pipelinepgvectoror QdrantQuestionRetrievehybrid + rerankLLMhosted or OllamaAnswerwith citationsGuardrailsPII + eval
IngestFiles, databases and wikis, with incremental sync.
Chunk & embedSplitting and embedding tuned to your content.
Retrieve & rerankKeyword and vector search combined, then reranked.
AnswerEvery answer traceable to the passage it came from.
How we build it

Four disciplines, from data to production

AI projects fail on plumbing and proof, not on model choice. We build the retrieval, the evaluation and the operations around the model — and can run the whole thing on hardware you control.

Data & Retrieval

Getting your own content into a form a model can answer from — reliably, and with citations.

  • Data pipelines
  • Chunking & embeddings
  • Vector search
  • Hybrid retrieval & reranking
  • Grounded answers

Models & Evaluation

Choosing a model, shaping its context, and proving it works before it ships.

  • Model selection
  • Prompt & context engineering
  • Fine-tuning
  • Evaluation harnesses
  • Guardrails

Private & On-Premise Inference

Models running on hardware you control, so regulated data never leaves the building.

  • On-premise deployment
  • Data residency
  • GPU sizing
  • Throughput tuning
  • Air-gapped operation

MLOps & Operations

Keeping a model in production long after the demo is over.

  • Model serving
  • Monitoring & drift
  • Inference cost control
  • Retraining pipelines
  • Model registry
EU
EU-only processing
0
third-party API calls
2
runtimes · Ollama, vLLM
Private inference

Regulated data never has to leave the building.

Ollama or vLLM on your own servers and GPUs, residency documented for audit, and — when you need it — no third-party API calls and no egress at all.

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