Artificial Intelligence only works in business if it is based on structured and governed data. However, the majority of RAG (Retrieval-Augmented Generation) projects fail due to a lack of solid documentary foundations.
In this practical guide, discover the 5 essential technical pillars to transform your electronic document management In engine of business artificial intelligence.
Why does RAG not work (yet) in business
Underestimated technical challenges
According to Earley (2023), one of the main misunderstandings in AI projects is the belief that LLMs eliminate the need for document architecture. Experimental tests show that a metadata enrichment (improved documentary research) increases the accuracy of answers by 30% (53% to 83% relevance).
As pointed out Jeong (2023), a RAG system is more than just pasting a vector base to an AI model. It is necessary to orchestrate several components: documentary fragmentation, generation of embeddings, vectorization, specialized databases, prompt engineering, filtering by user rights...
Performances that are still unstable
Even the most advanced systems, tested on benchmarks like STARK (Wu, 2024), show limits. The “Hit @1” rate — probability that the first answer is correct — tops out at 18% in some contexts.
The governance problem
The majority of companies do not have the foundations to control what they give to their AI, especially in the absence of a Workflow automated system that ensures the dematerialization of flows and the intelligent classification of documents.
The 5 pillars of intelligent document management
✅ Pillar 1: Fragmentation and intelligent vectorization
The challenge of semantic fragmentation
Transforming unstructured documents (PDF, Word, emails) into blocks of information understandable by an AI requires a precise semantic division — “chunking”.
Impact on performance
Poor fragmentation can affect the quality of AI responses. According to Wu (2024), the performances of RAG engines depend heavily on the quality of the initial cutting: Poor chunking can cause the “Hit @1” rate to fall below 20%.
Efalia's expertise in intelligent fragmentation
At Efalia, we have developed contextual fragmentation engines that rely on the existing documentary structure: titles, metadata, business fields. All this, facilitated by optical character recognition, natively present in the ECM. This approach allows a semantic fragmentation, not only technical, thus optimizing the quality of the embeddings for your vector base.
✅ Pillar 2: Strengthened documentary governance
Safety requirements
An AI should never respond from a document that is obsolete, unvalidated, or intended for another service. La information governance involves:
- Fine rights management (user, group, role) in particular for the security of GED data.
- Full traceability consultations and modifications — optimization of documentary processes
- Life cycle policy documents — archiving optimization
- Compliance RGPD, ISO 27001 — EDM regulatory compliance
EDM vs Sharepoint: Why SharePoint is not enough
SharePoint works well for collaboration but has limitations for RAG:
- Distributed governance : each space can have its own rules
- No transversal ranking plan imposed
- Non-standardized metadata between teams
- Lack of unified traceability across the organization
✅ Pillar 3: Metadata and unified filing plan
Metadata as an AI grammar
Metadata is not a documentary “plus” but The grammar that the AI is going to use to understand what she is reading. They allow you to:
- Filter results according to objective criteria —> documentary performance
- Prioritize sources (signed version > draft)
- Limiting hallucinations through context
The importance of the ranking plan
The ranking plan acts like a logical mapping of your business. In an RAG system, this allows the AI to precisely target useful blocks of information.
The Efalia approach to smart metadata
Our Content Service Platform natively integrates business metadata logic. Unlike general solutions, we offer:
- Pre-configured business templates : human resources, quality, Finances with standardized fields
- Scalable ranking plan : adapted to your organization, modifiable independently with automatic classification by edm
- Automated lifecycle : status, deadline alerts, compliant archiving
This native structure prepare your content for artificial intelligence without disrupting your business processes.
✅ Pillar 4: Interoperability and business connectors
Connecting electronic document management to the real business
One isolated documentary platform becomes a new silo. The chatbot needs a transversal, controlled and updated access to the information distributed in your software ecosystem.
The essential connectors
- Automatic payout from your business tools (pay slips from SIRH) — data extraction
- Enrichment on the fly (automatic EDM classification from business tools)
- Synchronizing rights with the business directory
- Exposure via API to power AI agents and Copilots: artificial intelligence GED
Efalia's connector expertise
Backed by our experience with +2800 customers, we have developed connectors native with the main business tools:
- SIRH : e.sedit by Berger Levrault, HR Access by Sopra HR...
- ERP/Finance : SAP, Sage,...
Our API-first approach guarantees sustainable interoperability and avoids costly specific developments. Each connector automatically respects the governance rules defined in the EDM.
✅ Pillar 5: Vector agnostic base
Maintaining technological control
The vector database stores the mathematical representations (embeddings) of your documents. A base agnostic guarantees:
- Independence with respect to a specific LLM
- Querying by API without a proprietary solution
- Compliance with access rules (filtering by roles, services)
- Full control (accommodation, structure, security)
The Efalia vision: sovereign vector base
We believe in sovereign and controlled corporate AI.
Our approach integrates a vector-agnostic base that allows you to:
- Test all models : GPT, Claude, Mistral, Llama without redesign
- Keeping your data in France : SecNumCloud hosting
- Maintaining governance : automatic filtering by business rights
- Avoid vendor lock-in : guaranteed technological independence
This architecture prepares you for future AI developments while maintaining total control of your documentary heritage.
Impact on performance
In a study of Jing et al. (2024), the combination of rich metadata + hybrid queries improves 35% the quality of the answers on complex corpora.
Discover the detailed benchmarks and technical comparisons in our comprehensive guide.
Investment and planning for your project
Realistic budget and deadlines
Building a documentary base ready for the RAG request between 3 and 6 months according to initial maturity. Estimated budget: 30,000 to 100,000 euros depending on the volume and business complexity.
Recommended project phases
- Audit of the existing (files, EDM, SharePoint, email)
- Definition of the ranking plan and critical metadata
- Choice and implementation of the EDM engine/vector base
- Technical treatment documents (cleaning, structuring)
- Integration to business tools and security tests
This technical base makes it possible to Multiply AI use cases without rebuilding everything: testing different LLMs, creating several business agents, connecting Microsoft Copilot... without losing control over the source data.
The Efalia approach: EDM natively AI-ready
Efalia has been structuring and managing corporate documentation since 1982. This expertise in document management positions us today as the ideal player for prepare your data for Artificial Intelligence : we transform your edm a source of truth that is structured, governed and exploitable by artificial intelligence.
Our approach: from Electronic Document Management to the AI knowledge base
Our historical profession — structure, secure and interconnect documents — is now becoming the critical foundation for reliable business AI and intelligent document processing. We're not doing AI, we Let's get your data ready for it to work.
Our technical differentiation
- API-first architecture : total interoperability with your ecosystem
- Contextual fragmentation : proprietary semantic slicing engines
- Centralized governance : fine management by roles, professions and levels of confidentiality
- Agnostic vector base : test all AI models without redesign
- Native business connectors : +45 technological partners
Our commitment to sovereignty
- SecNumCloud Hosting : data in France, ANSSI certified
- Controlled source code : no external technological dependence
- Native compliance : RGPD, ISO 27001, sectoral regulations
Conclusion: 5 key points to remember
- Content quality determines AI performance. In addition, the use of EDM machine learning makes it possible to optimize the automated extraction of information and to reinforce the relevance of the results, thus contributing to intelligent document editing. : without documentary structuring, no reliable AGR.
- Metadata is the grammar of Artificial Intelligence: they improve the accuracy of answers by 30%
- SharePoint is not suited for enterprise RAG : distributed governance and lack of standardization
- Interoperability is critical : an isolated EDM cannot feed business AI
- Technological independence protects your investments : vector agnostic base for testing all models
The challenge of 2025: transform your Electronic Document Management from a simple filing tool into the strategic foundation of your business artificial intelligence.
? Ready to structure your EDM for AI?
Sources cited:
- Earley (2023) — Documentary Architecture and LLM
- Jeong (2023) — Complexity of integrating RAG systems
- Wu et al. (2024) — STARK Benchmark, NeuRips
- Jing et al. (2024) — When Large Language Models Meet Vector Databases: A Survey, arXiv
