● Artificial Intelligence
We move from generic AI that knows nothing about your company to AI connected with your own knowledge: policies, manuals, contracts, histories. Quality retrieval, answers with verifiable citation and architecture ready for production.
The context
While everyone talks about agents and new models, AI projects that bring real value share one thing: they have a well-built RAG. RAG (Retrieval-Augmented Generation) is the difference between «generic ChatGPT» and «AI that knows my company». It has stopped being an experiment and is now critical architecture for any serious AI deployment with proprietary data.
Trend · 01
Modern architectures are no longer «retrieve fragments and generate», they are systems with quality controls, source verification and integrated traceability. The difference between a pilot alive at six months and one abandoned.
Trend · 02
BM25 (keywords) + Dense (semantic) with a reranker gives 12–15% more relevance than vector-only search. It's the heart of a RAG that truly gets it right.
Trend · 03
Claude Projects, Custom GPTs, NotebookLM and platforms like Glean allow starting a RAG without a technical team. For small and medium-sized companies, it's the entry point: before, it was unreachable.
The problem
Symptoms vary from company to company, but the patterns repeat. These are the four structural pains we find in practically every RAG project we audit.
01
The user asks about expense policy and the system returns remote work. Without hybrid search or reranking, the fragments are the semantically close ones: not the relevant ones. Garbage in, garbage out.
Impact
Users who give up after two or three bad queries. The RAG gets labeled as «doesn't work».
02
The system retrieves well, but the model ignores the context and responds with prior knowledge. Without strict instructions that force «I don't know» when confidence is low, the model improvises when it should abstain.
Impact
Worst of both worlds, you have RAG and it still invents. Total loss of user trust.
03
The RAG feeds on PDFs uploaded 18 months ago. Policies that no longer apply, old prices, changed processes. Without someone responsible for keeping documents alive, the system answers outdated information as if it were current.
Impact
Wrong information with the false confidence of «the AI said so». Decisions made on stale data.
04
«The system works», they say. But nobody knows what percentage of responses are correct, what percentage of retrievals are relevant or how many hallucinations slip through. Without metrics, the RAG lives as a black box and degrades silently.
Impact
Impossible to know if it's improving or worsening. Blind decisions on model, chunking and embeddings.
The assistant tells us prices from two years ago with complete confidence. And when we ask where it got that answer, nobody knows how to answer.
, What we hear in discovery calls
The cost
50%
fewer hallucinations with a well-implemented RAG versus pure LLM, the rest stay in the system with false confidence when it's badly built.
Source · Industry meta-analysis 2026
An uncomfortable conclusion
A badly built RAG is worse than no RAG, because it generates false confidence. The companies that win don't have «an assistant with documents», they have governed, evaluated and maintained knowledge infrastructure. And meanwhile, that 80% of unstructured knowledge stays invisible to AI.
The solution
The most common mistake is treating RAG as «upload my PDFs to a Custom GPT and done». The difference between a RAG that delivers and one that disappoints is in six construction and operation pillars. Well applied, they reduce hallucinations by half and improve efficiency by 30% to 70%. Badly applied, an assistant that ages and gets abandoned in six months.
01
Which question does the RAG resolve, and which it doesn't. Internal knowledge base, customer support, sales enablement or legal search are different cases with different corpora. Start with one well done, not «everything at once».
02
Conversion of PDFs, Word and HTML to structured markdown. Semantic chunking (not 512 tokens blindly) that respects sections, tables and clauses. Per-fragment metadata: source, date, department and permissions.
03
Suitable embeddings model (OpenAI text-embedding-3, Cohere, Voyage). Vector DB based on scale: Pinecone (managed), Qdrant or Weaviate (open-source), pgvector (if PostgreSQL is already there). No over-engineering.
04
BM25 (keywords) + Dense (semantic) outperforms pure vector search by 12–15% in relevance. A reranker (Cohere Rerank, ColBERT) orders the final results. Metadata filters for permissions and precision.
05
Rigorous instruction that forces «I don't know» when confidence is low. Source citation with each response, the user can verify. Base model (Claude, ChatGPT) chosen for reasoning and respect for context.
06
Reference set of questions with correct answers. Precision@K, recall, faithfulness, citation precision. Knowledge base owner who keeps it alive. Weekly metrics for cost, latency and quality.
The tools
«The RAG stack has four layers: base model that generates, orchestration framework, vector DB and packaged solutions for quick starts. These are the four tools we work with most depending on the starting point: the turnkey solution (Glean), the base model that respects retrieved context (Claude or ChatGPT) and the visual pipeline without writing code (n8n).»
Packaged enterprise RAG: connects out of the box with Drive, Notion, Confluence, Slack, Jira and more than 100 sources. Retrieval, per-person permissions, generation with citation and governance dashboard included. Saves building the pipeline from scratch.
Ideal for
Medium and large companies with knowledge spread across many tools and no technical team to build a custom RAG. When the priority is to get it into production quickly with governance ready.
Best instruction-following, respects strict RAG and returns «I don't know» when confidence is low. Low hallucinations, extensive context to fit many fragments and clean citations. Via API or Claude Projects for no-code start.
Ideal for
Regulated sectors (legal, finance, health), cases where precision and citation quality are critical and knowledge bases with extensive corpus. When the cost of a hallucination is high.
Widest ecosystem: Custom GPTs with Knowledge files, Actions via API and Assistants API with native file search. Multimodal out of the box and competitive cost. 63.6% of enterprise RAG implementations use GPT as the base.
Ideal for
Cases where the RAG needs to execute actions (external API calls), multimodal corpus (images + text) or you want to distribute as a Custom GPT. Best option when there's already an OpenAI ecosystem in place.
Visual construction of the RAG pipeline without writing code. Native nodes for each step: ingestion from more than 400 sources, chunking, embeddings, vector stores and agents with retrieval. Self-hostable when data can't leave.
Ideal for
Teams that already have n8n for automations, RAGs where the main complexity is ingesting many different sources and cases where you want full pipeline control without writing LangChain or LlamaIndex from scratch.
A sequence proven in 200+ companies. Each phase has deliverables before moving to the next, and is developed in collaboration with your internal team.
Diagnostic
We audit existing processes and the current stack. We map bottlenecks and optimization opportunities to ensure the success of the following phases.
Planning
We define target architecture, rollout plan, roles, and metrics before getting into the weeds.
Build
We execute in short iterations with your team. We create, adapt, and integrate with your existing tools.
Rollout
We start with a test and expand after validation. We train your team so adoption feels natural.
Follow-through
We measure and listen to feedback throughout so the result truly becomes yours.
Results
A well-built RAG shows up in three dimensions: the company's information stops being in silos and becomes queryable, decisions are made with verifiable proprietary data (not with invented answers) and the team spends hours producing, not searching.
−50%
A well-implemented RAG halves hallucinations. And with strict RAG + source citation, they reach the 0.11% range. Critical in sectors where the cost of a wrong answer is high.
+15%
With modern embeddings (text-embedding-3) and hybrid search + reranking, Precision@K rises 15% over basic implementations. The difference between RAG that delivers and RAG that disappoints, measured.
−30%
Skipping retrieval on simple queries saves 30% of compute. More caching, more efficiency. The monthly bill for embeddings and queries stops being a surprise for the CFO.
6–8 wk
From diagnosis to pilot in production in 6 to 8 weeks with consolidated stack (Claude or ChatGPT + LlamaIndex + Pinecone). With no-code solutions (NotebookLM, Claude Projects), validation in days.
The team no longer searches in Drive, they ask the RAG and go straight to the source. And when in doubt, they verify with the citation. Before we trusted blindly what Claude said; now we trust because we know where it gets it from.
, Operations Lead, B2B consultancy
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or write to us at info@theoptimalflow.com
