An inventory of source systems and document owners.
Enterprise knowledge
Enterprise RAG: when does a knowledge assistant make business sense?
RAG connects a language model to controlled company knowledge so users can ask questions with source context. The real work is not the chatbot interface: it is source quality, permissions, retrieval evaluation, freshness and operational ownership.
Independent selection
A provider's payment does not determine whether it is included.
Traceable data
Profiles distinguish public sources, provider-supplied information and AIPartnerLens analysis.
Fit before volume
The shortlist focuses on a few comparable providers, not a wall of logos.
Business problem
Start with the operating problem, not the AI label
Employees spend too long searching policies, product documentation, procedures or project knowledge.
Existing search returns documents but not the specific answer needed.
Teams are experimenting with general-purpose AI that does not have access to approved internal knowledge.
The organization has identifiable knowledge sources with recurring questions.
Answers can be grounded in documents and checked against citations.
Access rights can be propagated or enforced at retrieval time.
What should be true before you request proposals?
Document permissions and retention rules.
A representative question-and-answer evaluation set.
A process for updating, deleting and correcting source content.
A practical sequence from discovery to operations
1. Define the decision
Document the business problem, current process, volumes, exceptions, owners and the outcome the project must improve.
2. Check data and systems
Confirm which data, documents, applications and permissions are available before selecting a model or tool.
3. Test on representative cases
Use real or representative examples, explicit acceptance criteria and a baseline against the current process.
4. Prepare production and operations
Define security, monitoring, failure handling, human escalation, ownership, documentation and maintenance before go-live.
Evidence to request
Ask for evidence that matches this use case
- Retrieval metrics on a representative test set.
- Source citations and traceability.
- Permission tests across user roles.
- Freshness, indexing and deletion behavior.
- Monitoring for unanswered and unsupported responses.
Measures
Decide how value will be measured before the pilot
Answer usefulnessCitation correctnessRetrieval recallUnsupported-answer rateSearch time savedAdoption by target users
Providers with public signals related to this problem
This is not a ranking. Profiles appear when their public projection contains related use cases, services or technology signals. Verify fit against your exact constraints before contacting a provider.
Agency · Consulting firm · Integrator · Training provider
AGI-SO
AGI-SO designs, integrates and develops custom AI solutions for microbusinesses, SMBs and mid-market companies, from assessment and architecture through deployment, automation and training.
Review fit on the profileB
Agency · Integrator · Independent consultant
BatirUp
BatirUp designs custom AI agents and automations that connect to tools already used by microbusinesses and SMBs.
Review fit on the profile
AI chatbot, voicebot & callbot
Dydu
Dydu presents itself as a French conversational-AI specialist offering chatbots, voicebots, callbots and live chat for business customers.
Review fit on the profile
AI engineering & data
ILLUIN Technology
ILLUIN Technology designs custom systems in AI, GenAI, data science and software engineering, complemented by a product suite and its nAIxt studio.
Review fit on the profile
Data, cloud & AI
JEMS
JEMS covers the Data, Cloud & AI value chain and emphasizes production deployment of AI systems integrated into business processes and supported by a governed data foundation.
Review fit on the profile
Document GenAI & RAG platform
Kairntech
Kairntech publishes a GenAI platform focused on documents, RAG, assistants and workflows, with private-deployment options and selected public pricing.
Review fit on the profile
Custom AI agency
Neovision
Neovision is an AI agency based in Grenoble that designs custom solutions in computer vision, prediction, document automation and Generative AI.
Review fit on the profile
AI engineering, cloud & data
SFEIR
SFEIR positions itself as an AI Engineering Company that designs, deploys and operates AI systems in production, including agents, RAG and LLMOps.
Review fit on the profileRisks
What can make this project fail?
- A polished interface hiding weak retrieval.
- Leaking documents across permission boundaries.
- Stale sources remaining in the index.
- Measuring only model quality instead of end-to-end answer quality.
Questions for providers
Questions worth asking before a proposal
- 1.How do you test retrieval quality?
- 2.How are permissions enforced?
- 3.How quickly do source updates appear?
- 4.Can users inspect the source passage behind an answer?
- 5.What happens when the system has insufficient evidence?
Questions about enterprise knowledge
What is the difference between RAG and a generic chatbot?
RAG retrieves controlled source content before the model answers. A generic chatbot can respond from model knowledge alone and may not reflect current internal documents or permissions.
How should enterprise RAG be evaluated?
Use a representative question set and measure retrieval, citation correctness, unsupported answers, permissions and freshness. A demo on a few curated documents is not enough.
Does RAG eliminate hallucinations?
No. It can ground answers more effectively, but weak retrieval, ambiguous sources or model behavior can still produce incorrect responses. The system needs evaluation and clear fallback behavior.