Cognitive infrastructure for the enterprise
The algorithm passes.
The data remains.
We design the knowledge layer that lets any artificial intelligence — today’s and tomorrow’s — understand, query and reason verifiably over your company’s information assets.
AI-agnostic
by design
Grounded
answers traced to source
On-premise
where control matters
Enterprise
not consumer
The thesis
The market looks at the model.
We look at what remains
when the model changes.
Every few months a new model surpasses the previous one. GPT, Claude, Gemini, local models: none of these is a lasting competitive advantage, because anyone can adopt it the next day.
A company's true defensible asset isn't the artificial intelligence it uses, but the format in which its knowledge is structured. We call it the Knowledge Genome: the encoding of organizational memory — procedures, documentation, tacit know-how — into a form that is readable, queryable and verifiable by any AI engine, present or future. A company that owns its Knowledge Genome doesn't depend on a model vendor: it can swap the underlying engine without losing knowledge, traceability or control. The model is interchangeable. The knowledge genome built around it is not.
The tools
Seven disciplines, one objective.
We don't build general-purpose language models. We use mathematics, statistics and systems engineering for a single purpose: turning company knowledge into an asset that can be reliably queried — with answers traceable and grounded in real sources, never hallucinated — by any AI, today or in five years.
Mathematics & Statistical Modeling
Probabilistic models and applied statistics to quantify the uncertainty of every generated answer, not just produce it.
In practice: Confidence scoring on every AI-generated output, with configurable thresholds for human intervention.
Wiki, Knowledge Graph & RAG
Retrieval-augmented generation architectures anchored to structured knowledge graphs, not simple semantic similarity. Knowledge is shifting from RAG to the wiki: a format AI agents consult and update more naturally.
In practice: Enterprise chat that answers by citing the exact procedure or document it comes from, verifiable line by line.
Agentic Workflow Orchestration
Orchestration of AI agents across local models and frameworks like OpenClaw and Hermès, with explicit autonomy boundaries.
In practice: Digital workers that execute multi-step processes while keeping an auditable log of every decision made.
Deterministic vs. generative automation
A clean separation between what must remain deterministic (n8n, rules, pipelines) and what requires generative reasoning.
In practice: Only genuinely ambiguous steps are handed to a language model; everything else stays predictable and verifiable.
Reinforcement Learning
RL and feedback loops to align agent behavior with real business objectives, not generic proxies.
In practice: Agents that improve over time based on operator corrections, without continuous manual retraining.
Model Selection
Targeted selection of the most up-to-date, fit-for-purpose model — never the most powerful in absolute terms, but the most coherent with the agent's goal.
In practice: A different engine for each company agent, chosen by task, cost and compatibility with the already-structured internal wiki.
Training & Distillation
Distillation of general-purpose base models — often massive, with over 95% of their capacity unused relative to the real use case — into small, specialized models.
In practice: Compact models calibrated to the client's processes, deployable on-premise, with drastically lower cost and latency.
A mistake to avoid
Competitive advantage can't be rented from the cloud.
For SMEs and professional firms first, and for large enterprises too: artificial intelligence isn't built by chasing a hard-to-find hire, or by reskilling existing IT overnight. And it isn't bought as a cloud service.
The expert you can't find
The job market doesn't have enough AI specialists: waiting to hire one, or hoping to reskill internal IT in the short term, stalls the company while competitors move.
The cloud trains someone else's model
Every interaction with a generic cloud AI service feeds the provider's model, not your company's. The value you produce stays outside your perimeter.
It's not just about data
With an external service you don't just share company or critical client data: you share how your organization thinks and behaves — the hardest part to replicate, and therefore the most valuable.
That's why a solution like Mimētikós — or more broadly a knowledge layer built and hosted in-house, becoming your company's permanent wiki for any AI — isn't just a privacy choice: it's the condition for keeping a competitive advantage no external vendor can replicate.
The case study
Mimētikós: when theory becomes a digital worker.
Mimētikós is the cognitive operating system we built by applying every competency described above to a real case: a company that needed not another virtual assistant, but a digital colleague.
With Mimētikós we took the Knowledge Genome thesis out of the lab. The project is built on a Zero UI principle: no new interface to learn, no additional dashboard — the AI adapts to the company's existing processes, not the other way around.
Under the hood, Mimētikós orchestrates adaptive agents on a knowledge layer structured according to the principles on this page: retrieval anchored to verified sources, deterministic automation where predictability matters, models selected and distilled for each role. Mimētikós is today an independent product, with its own site and its own commercial roadmap: here we present it as proof of our research's applicability, not as a sales pitch.
How we work
From tacit know-how to an AI-ready asset.
A four-phase path, designed for enterprise organizations with knowledge scattered across people, documents and legacy systems.
Audit
Mapping the existing knowledge estate: documentation, procedures, systems, tacit know-how held by key people. We identify what is already structured and what lives only in the organization's memory.
Extraction
Collecting and normalizing knowledge from heterogeneous sources — wikis, PDFs, databases, interviews — into a single, coherent, verifiable representation.
Structuring
Building the Knowledge Genome: knowledge graphs, semantic indexing, traceability mechanisms and source versioning.
Agentic integration
Connecting the knowledge layer to models and agents selected for the purpose, with deterministic automation where predictability matters and generative automation where reasoning is needed.
Let's collaborate
Request an audit of your cognitive infrastructure.
We work with strategic partners, investors, SMEs and professional firms that see organizational knowledge as an asset to protect, not a problem to outsource to a single AI vendor.
We only respond to inquiries from companies, strategic partners and investors.