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AI agency: AI agents and AI automations that actually pay off.

Practical AI implementation for business: AI agents for business, RAG assistants over your knowledge base, automatic classification and AI automations. No fluff, no flashy demos, products in production.

Contact usUse cases

Cases where AI saves real time.

Not everything is AI. Before proposing an agent we validate the case justifies the cost of keeping it in production.

  • Trapped knowledge

    Manuals, contracts, internal docs that nobody consults because searching takes longer than asking the colleague next door. RAG assistant over your knowledge base.

  • Manual classification

    Each lead, ticket or incoming email has to be classified by hand. AI does it well with half the effort, leaving only edge cases for humans.

  • Repetitive generation

    Reports, proposals, meeting summaries, product descriptions. Tasks an AI agent with your context produces in seconds.

  • Tier-1 customer support

    Chatbot solving FAQs with real information (RAG), not generic answers. Human only intervenes for complex cases.

Four steps for AI that holds up in production.

  1. 01

    Concrete case, not AI for fashion

    We identify which real process saves time or money with AI. If there’s no case, we say so. Implementing AI without clear ROI is wasted budget.

  2. 02

    Pilot with real data

    Prototype in 1–2 weeks with YOUR data, not an idealized demo. We measure precision, latency, cost per query.

  3. 03

    Production with guardrails

    AI in production always with guardrails: validations, cost limits, fallback to humans for edge cases, auditable logs.

  4. 04

    Iteration with feedback

    Each month we review real performance, tune prompts/context, improve accuracy. AI isn’t a closed project, it’s a living product.

Models, frameworks and infrastructure we master.

  • OpenAI
  • Anthropic Claude
  • Google Gemini
  • LangChain
  • LangGraph
  • LlamaIndex
  • Pinecone
  • Weaviate
  • Qdrant
  • PostgreSQL pgvector
  • Make
  • n8n
  • Python
  • TypeScript

Key concept

RAG: AI that cites instead of inventing.

Retrieval-Augmented Generation (RAG) is the technique that connects an AI model with your real knowledge base. The AI consults your documents, manuals or FAQ before answering, and cites the source. The difference between a believable assistant and one that hallucinates.

See RAG-dedicated landing

About AI agency and AI agents for business.

01

How is an AI agency different from AI consulting?

AI consulting typically delivers reports and strategy. An AI agency delivers products in production: agents, assistants, integrations working. We are the latter, though we also do AI consulting when diagnosis is needed before building.

02

What is RAG?

Retrieval-Augmented Generation. The AI consults your knowledge base (documents, manuals, FAQ) before answering, instead of inventing. That’s why it gives precise and citable answers, not generic ones. More detail at /en/retrieval-augmented-generation/.

Which task would AI do better than your team today?

A quick call to identify the first case with clear ROI. If there’s no case, we say so.

Let's talk