Blog
Everything you need to ground your AI agents in validated data.
Designing a sovereign, GDPR-compliant RAG in 2026: storage location, controlled inference flows, multi-tenant isolation, and traceability — with pgvector, European open models, hybrid search, and reranking.
In 2026, the model and frameworks are commodities: an AI agent ships in a weekend. The real variable is the data layer. Three symptoms that betray an unready base, and the four criteria of AI-ready data.
Your AI pilots dazzle in demos and disappoint in production? The culprit is rarely the model. Here are the five signs that reveal a knowledge base not ready for RAG — and how to fix each one.
RAG impresses in a demo and disappoints in production. Three patterns change everything: hybrid vector + BM25 search with RRF fusion, mandatory cross-encoder reranking, and decomposing complex questions into sub-queries.
AI agents managed by major US hyperscalers raise compliance issues that many CIOs prefer to ignore. Let's set things straight.
Technical docs are a corpus apart: code, structure, versions, exact terms. How to build an internal copilot that chunks without breaking code, searches in hybrid mode, and respects versions — to genuinely help dev teams.
Anonymize or pseudonymize before vectorizing? The GDPR distinction, why perfect anonymization is rare on free text, the techniques (NER, consistent pseudonymization), and the reflexes to adopt.
Vector RAG retrieves isolated passages; GraphRAG links entities via a knowledge graph. Strengths, weaknesses, ingestion cost and a hybrid approach: when the graph genuinely changes the game.
A RAG amplifies the quality — or mediocrity — of its corpus. The complete method to audit your base before AI: inventory, authority/freshness scoring, contradiction and gap detection, Knowledge Ops, and health KPIs.
Single-pass RAG fails on multi-hop and comparison questions. Agentic RAG lets an agent decompose, retrieve iteratively, and stop at the right time — with its guardrails and cost. When to use it, and when not.
RAG over contracts and internal policies: sourced search, comparison and summaries. Why traceability (document, article, version), versioning and permissions are the precondition for legal use.
The GDPR compliance guide for your RAG projects: legal basis, minimization, personal data in embeddings and prompts, the right to erasure cascading into vectors, subprocessors, and articulation with the AI Act.
Vector similarity grasps meaning but misses the literal (codes, acronyms). Hybrid dense + BM25 search fused with RRF, then cross-encoder reranking: the combo that makes a RAG genuinely precise.
Self-hosted open models or closed APIs? 2026 landscape (Llama 4, Mistral, Qwen 3, DeepSeek, Gemma), five trade-off axes, a per-use decision table, and a sensitivity-based routing strategy.
Every user query reveals what is missing from your base. Detect gaps (vs mere retrieval problems), diagnose the three causes, and close the loop for a living corpus driven by real usage.
The complete guide from RAG POC to production: chunking, embeddings, hybrid search + RRF, reranking, pgvector/HNSW, and RAGAS evaluation. 2026 technical choices, code, and a go-live checklist.
Support is the fastest-ROI RAG use case. Copilot vs self-service, sourced architecture, anti-hallucination guardrails and metrics: how to cut resolution time without sacrificing quality.
The real EU AI Act timeline (2025-2027), risk tiers, where an enterprise RAG assistant lands, and a concrete compliance checklist — jargon-free, for DPOs and product teams.
Your RAG's relevance is decided at chunking, before the embedding. 2026 strategies from fixed to semantic, late chunking and contextual retrieval, with trade-offs, pitfalls and concrete recommendations.
AI sovereignty is no longer ideological — it's a board-level trade-off. The three levels of dependence (model, infrastructure, data), the 2026 model landscape, hosting in Europe, build vs buy, and a 90-day roadmap.