Generic AI answers you. Médula runs your business.
A model trained only on your data, hosted on your own infrastructure.
Not a rented model. Yours, trained on your data and hosted inside.
We build the core, not the accessory.
The medulla is what goes at the center: it coordinates information before distributing it to the rest of the body. That's what we build — the intelligence core of the organization, trained by us and hosted inside.
The engine (CLM)
A 1B–14B model built on open bases, calibrated with your country and industry context, then trained on your company's real data and sources. Every client gets a different engine.
Governance
Security, audit, and RAG over your live data: what goes in, what comes out, and who sees what. Connected to the continuous generation of data from your operation.
The application
A tailored front-end — chat, copilot, or agents — so the business can use it. The interface changes; the brain stays the same and improves over time.
Things that are already happening to you.
You don't need to be technical to recognize them. They happen every week, in every company running on borrowed AI.
“Come back in 5 hours”
Your team waits because the AI you rent hit its usage limit. Work stops because of a quota you don't control.
Confidential things walk out
Someone pastes a contract or client data into a public chat to move faster. It leaves the company and never comes back.
Every day starts from zero
Generic AI doesn't remember your operation. You explain the same thing again and again, and none of it becomes company knowledge.
You pay more and own nothing
The bill grows every month and your company is exactly as smart as yesterday. You're renting outcomes, not building an asset.
Your company already uses AI. It's just not yours.
Every day someone on your team tells a public AI something that should stay inside: a contract, a client's data, how they solved a problem. That AI learns. Your company doesn't.
It's not a one-off. It's what happens every day without a model of your own.
0%
of employees already use generative AI at work
0%
of them pasted company information directly into a personal AI
Source: LayerX Security, 2025.
Data leak
Contracts, code, and customer data leave your perimeter without any record.
Zero traceability
When something goes wrong, no one can reconstruct what was asked or what the decision relied on.
Nothing accumulates
You give your learning curve away to a third party: the knowledge stays there.
A real business question. An answer with a source. You approve.
The model doesn't make things up: it answers by citing the internal document where the answer lives, and waits for your decision.
Does this client fit the risk profile for this product?
Fits with caveats: income and payment behaviour are within range, but total exposure exceeds the segment limit. I recommend approving a reduced amount.
Cited sourceInternal credit policy, section 4.2 — limits by segment
Every answer is logged: who asked, what the model answered, who approved it.
Generic vs. expert. That's the whole difference.
The interface doesn't matter. What matters is what's behind it: a model that knows a bit of everything, or one that knows everything about your business.
We're not a security layer over someone else's model. We are the model.
The brain belongs at the center of the business, not off to the side.
We don't sell a brain and walk away. We keep it alive.
20x–100x
cheaper to operate
compared with paying per use for generic AI
Better
than generic AI at your business
because it learns from your operation, not the whole internet
100%
of data inside your infrastructure
never travel to a third-party server
We work on the open ecosystem
NVIDIA
Mistral AI
Google
Meta · Llama
Hugging Face
Qwen
DeepSeek
OllamaNVIDIAMistral AIGoogleMeta · LlamaHugging FaceQwenDeepSeekOllamaNothing you build on someone else's brain belongs to you.
Data never leaves.
The model is trained and runs inside your own infrastructure, not a third party's.
Fixed cost, no surprises.
An own engine runs at a predictable cost; heavier internal use doesn't spike the bill.
Knowledge accumulates.
Every interaction improves your model. With someone else's API, that learning is never yours.
Your AI asset grows over time.
The sooner you migrate, the more data, decisions, and know-how get captured in a model that is yours and appreciates.
For the IT team: we're not here to replace you.
You remain the owners of the system. We deliver the trained engine; you decide how it integrates and how it is governed.
External validation
The same thesis that Mistral is pushing with Forge and the on-premise deployment joint ventures of Anthropic and OpenAI: the company owns its model, it is not a tenant.
How does the work start?
- 01
Diagnosis
Where is the Shadow AI and which critical process gains from an own model.
- 02
Foundation
We choose the open base and calibrate it with your country and industry context as a starting point.
- 03
Training
We train the model with your data and connect it to your real sources. Every company receives a different CLM.
- 04
Evolution
The model updates with the continuous generation of data from your operation: an asset that improves and appreciates.
Don't rent your company's brain.
Book 30 minutes and we'll show you what information is leaking out today, unnoticed.