An AI that knows your company. And respects your hierarchy.
We build your AI assistant on your own data — documents, emails, contracts, business tools. You choose where the intelligence lives, and each role sees only what it should.
What is an enterprise memory (RAG)?
It's an AI chatbot connected to your internal documents — emails, contracts, plans, ERP, Drive: a true knowledge base everyone queries in plain language, with role-based access and sourced answers. Your choice: encrypted cloud or a sovereign LLM hosted on your premises.
How it works
Your sources feed a central memory. Everyone queries it in plain language — but only touches their own scope.
The sources
Databases, emails, files, business tools: everything your company produces.
The memory
The AI reads, understands and files every document. It answers by citing its sources — never made up.
The access
Everyone queries the memory in plain language, filtered by their role.
Two models, depending on your security level
From encrypted cloud to fully sovereign — you stay in control.
Encrypted data · zero-retention commitment · no training on your data.
Who can access what
Filtering happens at retrieval time, not after. A salesperson will never see a payslip.
Role in the company
Management
7 / 7 domains accessible
Company memory
- Strategy & ManagementAccess
- Finance & AccountingAccess
- HR & PayrollAccess
- Engineering & CodeAccess
- Sales & CRMAccess
- Legal & ContractsAccess
- Communication & EmailsAccess
Dedicated memories, one overall view
Each person and each team keeps its own memory. They aggregate into a general memory: Management sees everything, everyone else only their scope.
General memory
Complete view of the company
Aggregates all · Management accessManagement
Team memory
personal
Accounting
Team memory
personal
Sales
Team memory
personal
Engineering
Team memory
personal
What feeds the memory
Internal sources or online services — everything goes through the same access rules.
A salesperson will never see a payslip, whether it comes from an internal database or a Drive.
What you can ask it
Everyday questions, instant and sourced answers — whatever your field.
«What thermal regulation applies to the Les Tilleuls site?»
«Summarize the termination clause of the unit 4B lease.»
«Which supplier invoices are still unpaid this month?»
«How many days of leave after 4 years of seniority?»
«Which leases expire next quarter?»
«What is the maintenance procedure for line 4?»
Your data stays your data
Built for European requirements — GDPR, EU AI Act, controlled hosting.
GDPR by design
Minimization, traceability, cascading right to erasure. Built for compliance, not bolted on afterwards.
Sovereign hosting
Encrypted cloud or servers on your premises / a European data center. You choose where your data lives.
Sourced answers
Every answer cites its source documents. Verifiable, auditable — no black box.
Audit trail
Who asked what, from which sources. Essential for GDPR and the EU AI Act (major obligations in 2026).
What it changes, concretely
The orders of magnitude reported by studies on enterprise document-AI deployments.
of time saved on document search (up to)
months for a return on investment (median)
of answers cite their sources
available, with no extra agent cost
Indicative ranges from 2025-2026 sector studies (Gartner, Deloitte, client cases). Results depend on the quality of your data.
Frequently asked questions
Can you use AI on confidential documents without GDPR risk?
Yes. Your data stays within a controlled perimeter: zero-retention encrypted cloud, or a sovereign LLM hosted on your premises / in a European data center. No data is used to train a third-party model — GDPR and EU AI Act compliant.
What's the difference between RAG and fine-tuning?
RAG lets the model retrieve information from your documents at answer time (fresh, sourced); fine-tuning retrains the model, mostly useful for style. For an enterprise memory, you almost always start with RAG.
How do you build an internal chatbot on your documents?
We connect your sources (Drive, email, ERP, internal databases), index them, then expose an assistant that answers while citing its sources — without rebuilding your tools. Deployment happens scope by scope, gradually.
How long and what budget to deploy?
A first useful scope goes live within a few weeks. The budget depends on the volume of sources and the chosen model (encrypted cloud or sovereign). We start with a free audit to frame the project.