Hire a RAG Developer

I build retrieval-augmented AI over your own data — accurate, grounded answers with citations, not hallucinations. GraphRAG and hybrid vector pipelines, reranking, evaluation, and the guardrails to keep it trustworthy in production. Shipped across procurement, inventory, compliance, and customer support.
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Trusted by teams at H-Farm, GB Viaggi, Pellemoda, and more · GDPR & EU AI Act–ready.

What I build

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Autonomous agentfor demand forecasting, stock health, and supplier coordination

Pellemoda S.r.l. — An autonomous inventory agent for Pellemoda — demand forecasting and supplier coordination on autopilot

2,000+ applications/yearprocessed through an automated admissions pipeline

H-Farm College — How H-Farm College automated an admissions pipeline handling 2,000+ applications a year

~€1M / yearin recovered capacity from automating 3,000+ bookings a day

GB Viaggi — How GB Viaggi automated 3,000+ bookings a day and unlocked ~€1M a year

More in the case studies.

How engagements work

Most clients start with a fixed-scope Sprint (€8,000, 2–4 weeks) and graduate to a Build for the full production system. See services & pricing or get a tailored figure with the cost calculator.

Frequently asked

What is RAG, and when do I need it?

RAG (retrieval-augmented generation) grounds an LLM in your own documents and data at query time, so answers reflect your knowledge — not just the model's training. You need it whenever the model has to answer from private, current, or domain-specific information (policies, manuals, tickets, catalogs).

GraphRAG vs vector RAG — which is right for me?

Plain vector RAG is ideal for 'find the relevant passage and answer.' GraphRAG wins when questions span multiple documents or relationships ('how does X affect Y across these contracts?'). I usually start with strong hybrid vector + reranking and add a graph layer only where it earns its keep.

How do you stop the model from hallucinating?

Grounding every answer in retrieved sources with citations, retrieval evaluation (so bad context is caught), reranking to surface the right passages, and guardrails that make the model say 'I don't know' instead of inventing. You get a system you can actually trust.

How much does a RAG system cost?

A working RAG assistant starts at €8,000 (Sprint, 2–4 weeks); a full production system runs €18,000–€35,000, plus modest monthly LLM + vector-DB costs. Try the AI Agent Cost Calculator for a tailored figure.

Can you build RAG over my private / internal data securely?

Yes — with access control, PII filtering, EU-region storage, and audit trails. The whole point of RAG is using your data; doing it compliantly is part of the build.

Do you handle the whole pipeline or just retrieval?

End to end: ingestion and chunking, embeddings, retrieval and reranking, generation with citations, evaluation, and the chat/app layer on top — plus deployment and monitoring.

Do I own the code?

100%, from day one. No lock-in, no proprietary black boxes; Builds include handover documentation.

Need RAG that gives accurate, grounded answers?

Book a free 30-minute call — tell me what you're building and I'll tell you if I can help and what it would take.

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