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/// Services / AI

Artificial intelligence that reaches production.

We build LLM applications, retrieval systems, and ML pipelines that survive contact with real traffic — instrumented, evaluated, and operated. Demos are easy; we ship the version that holds up.

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/// How we approach it

Evals before features.

Most AI projects stall because nobody can say whether they got better. We start from a measurable outcome, build the evaluation harness first, and only then ship features against it. Retrieval, agents, and fine-tuning are means — the eval is the contract.

  • [ 01 ]Outcome and eval defined before a line of model code
  • [ 02 ]Retrieval and grounding over raw generation
  • [ 03 ]Human-in-the-loop where stakes are high
  • [ 04 ]Cost and latency budgets treated as first-class
/// Capabilities

What we build.

  • LLM & agent applications
  • Retrieval-augmented generation
  • Semantic & hybrid search
  • Document intelligence
  • Computer vision
  • ML pipelines & training
  • MLOps & evaluation harnesses
  • Model serving & inference
/// In the field
Eight eval suites in CI — 1,142 golden cases, 96.8% pass, two regressions flagged and the merge gate on hold until they clear.
Retrieval before generation — hybrid search over 12,408 contract chunks, four cited passages, and an answer where all five claims resolve to sources.
Nightly training run #412 — seven stages, an eval gate that must pass 8/8 before canary, and cost, latency and per-call budgets each at 70%.
Human-in-the-loop where it counts — anything under 0.85 confidence stops here; the other 92% goes straight through.
The Operate step — drift held under the 0.40 page threshold, €0.0042 per call against a €0.0060 budget, and 240 sampled online evals a day.
/// How we work

From prompt to operated model.

  1. [ 01 ]

    Frame

    We define the outcome, the eval that measures it, and the smallest slice worth shipping.

  2. [ 02 ]

    Ground

    Retrieval, data pipelines, and guardrails — so the model answers from your reality.

  3. [ 03 ]

    Ship

    Production inference, instrumented for cost, latency, and quality from day one.

  4. [ 04 ]

    Operate

    We watch the evals in production and harden against drift, then hand over or run it with you.

/// Proof

What the work returns.

Numbers from AI systems we run in production today.

1M+
Documents processed
92%
Straight-through processing
8
Eval suites in CI
24/7
Inference operated
/// Products

AI, productized.

AI payments

Let AI spend money — inside walls you define

Budgets, merchant allowlists, approval thresholds, and a kill-switch — agentic payments under human policy.

Every action auditedView case
Operations

The back office behind every front end

Customer 360s, ops queues, and both money rails on one surface — every privileged action reason-required and audited.

One console, the whole operationView case

Have an AI system to build?

Tell us the outcome you need to move. We'll come back with an eval, an architecture, and a plan.

Contact us