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AI & Data

Agents, RAG, data pipelines and intelligent automations — in service of the business.

Generative AI applied to your business use cases: internal assistants, semantic search over your document base, extraction and synthesis, business copilots. We like AI that reduces repetitive work, not AI that blinks. All our pipelines are evaluated, logged, and supervised.

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What we deliver

Concrete, measurable, no surprises.

  • Production-grade RAG

    Retrieval Augmented Generation over your documents: chunking, embeddings, vector store, reranking, verifiable citations.

  • Agents & tool use

    Functional agents (function calling, planning, guardrails) — wired to your tools: CRM, ticketing, product database.

  • Fine-tuning & evals

    When the prompt isn't enough: light fine-tuning, LoRA, automated evaluation dataset, A/B in production.

  • Data pipelines

    Batch/streaming ingestion, dbt, Airflow/Prefect, data quality observability. Reproducible, auditable pipelines.

  • Vision & OCR

    Data extraction from invoices, contracts, forms, KYC, scans — vision language models, structuring and validation.

  • Governance & costs

    Budget tracking, semantic cache, fallback to a cheaper model, token optimisation. AI without an end-of-month surprise.

Non-negotiables

Our production standards.

Tools adapt to your context; our standards never move. Every project inherits this baseline — it is written into your contracts, not just this page.

  • Your data never reused for training
  • Models self-hostable on your infrastructure
  • Sourced, verifiable answers — zero invented citations
  • Per-request cost measured and capped
  • GDPR and AI Act compliance documented
  • Reversibility: the solution follows you, not the reverse

Process

Four steps, one standard.

  1. 01

    Use case discovery

    Identification of a high-value use case with measurable ROI. Experience mockup, success metrics.

  2. 02

    Prototype & eval

    Prototype in 2 to 4 weeks. Evaluation dataset, hallucination rate, citation accuracy, latency. Factual go/no-go.

  3. 03

    Production

    Secure deployment, observability (latency, cost, quality), rate limiting, guardrails, audit log. GDPR compliance.

  4. 04

    Continuous optimisation

    Prompt A/B, model A/B, targeted fine-tuning, data refresh. Documented quarterly improvement.

FAQ

Frequently asked questions.

Which models do you use?
We are agnostic: OpenAI, Anthropic, Mistral, self-hosted open-source models. The choice depends on the use case, cost and confidentiality.
Is my data used to train the models?
No — by default we use APIs in zero data retention mode (OpenAI Enterprise, Anthropic, Mistral) or self-hosted models.
How do you handle hallucinations?
RAG with verifiable citations, guardrails, structural validation, continuous evaluation dataset. Every critical output is traceable.
What ROI for an internal RAG use case?
On an internal document assistant, average search time drops from 15 minutes to under 2. Typical ROI within 6 months for >100 employees.
What about GDPR compliance?
Impact assessment (DPIA), lawful basis, minimisation, right to erasure. No personal data in a prompt without prior framing.
Do you host the models?
Yes — for sensitive cases, we deploy Mistral, Llama or equivalent inside your VPC (Scaleway, OVH, GCP, AWS).

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