The Business Context Engine

The AI that learns from expert knowledge

Dixtra is the context layer between your data and AI. It learns alongside your experts and shares your real business knowledge across the whole organization. Essential for making AI reliable in the enterprise.

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What is dixtra?

Your company loses hours every day searching for what it already knows

Every question mobilizes all your layers of data and knowledge.

The problem

Plenty of AI. Plenty of data.
Very little certainty.

Companies lose hours every day searching for what they already know. And the problem isn't the data.

$3T

is the yearly cost of decisions based on wrong or context-less data. In the US alone.

80%

of experts' time goes into understanding and organizing data. Not deciding with it.

70%

of organizations recognize the problem but don't know how to tackle it.

Source: Grand View Research, Artificial Intelligence Market 2025-2033.

The diagnosis

Your current stack processes data.
But it doesn't understand your business.

The critical knowledge of your company is already there. In your data. In your documents. And in the people who truly know.

But it's scattered. An important question takes calls, emails, meetings, experts, multiple systems. Hours. Sometimes days. And the answer can arrive too late.

Data platforms store. BI visualizes. LLMs generate. But none of them keeps a living representation of what's happening in your business, why, and what it means.

That layer didn't exist. Until now.

How it works

Three steps.
Results in weeks.

01
Discovery
The semantic agent connects your data warehouse and your documentation, and builds the living context of your business. The model is enriched continuously.
02
Learning
The AI converses with your analysts and experts to extract the knowledge that only lives in people. Nothing is accepted without prior human supervision.
03
Shared intelligence
The context becomes available to the whole organization by role and permissions. And as a foundation for your AI agents. The system learns and improves with every interaction.

What no one else does

AI learns from your experts through a semantic agent.

Knowledge, trapped in people, stops being a bottleneck. The semantic agent extracts it, validates it with experts, and incorporates it into the system.

While other systems scan documents, Dixtra talks with the people who know the business. It generates a dynamic checklist of open questions and converses with your analysts and experts to capture the business logic that isn't written down anywhere.

Nothing is accepted without human supervision. The company's quality standard rises. Responsibility still rests with people.

Cases in production

Dixtra is already real.

Heavy industry and scientific research share the same pattern: critical knowledge lives in experts' heads. We capture it.

Energy & Oil

From days to minutes in expert research on wells

Strategic queries about drilling wells that used to mobilize entire teams are now resolved almost in real time with Dixtra.

Biotechnology

Shared scientific knowledge

Researchers query papers, protocols, and historical documentation in natural language. The organization's knowledge is accessible to the whole team. It's no longer trapped in people. And the information sources are verifiable.

Trusted by

Plausible vs. true

One question. Two different answers. The decision that changes your P&L.

A real case. A premium cosmetics packaging line.

Prompt
Premium cosmetics

An operator proposes raising the temperature on line L-04 by 5 °C to cut the cycle from 14 to 11 seconds and save €1.2M a year. Would you do it?

Assistant without
business context

🟢 It's technically viable.

Analysis: Melt kinetics follow Arrhenius; a +5 °C increase speeds up the process by 30% to 50%, consistent with the proposal.

Recommendation: Run a controlled pilot (2–3 shifts), 100% leak testing, and visual inspection of the seal.

Conclusion: Technically viable. Proceed.

Plausible·Articulate·And completely wrong

Assistant
with dixtra

🔴 Do not proceed.

Five facts from the client itself advise against it.

Internal precedent: Lyon 2023 — the same change caused a €280K incident.
Active contractual clause with zero tolerance for cosmetic defects (€18K/batch penalty).
Systematic sensor deviation (+1.8 °C) not propagated to the MES.
Procedure Q-204 requires requalification with client notification — 5 weeks.
Documented alternative: film REF-082-EVO with the same return and zero risk.

A generic AI assistant without context simply answers the question.

Dixtra answers with the context you need to make decisions with confidence.

See the full exercise

Dixtra + Industry 4.0

Every conversation activates all connected layers.

Dixtra draws on the layers of your industrial stack from three sources: the company's data warehouse, documentary datasets (procedures, manuals, lessons learned), and a semantic agent that talks with specialists.

On that basis, anyone can ask in natural language and get a verifiable answer. No hallucinations. Expert knowledge is captured and shared across the whole organization. A single version of the truth for every team.

Designed for corporate environments

An architecture built for companies that can't afford mistakes.

Data sovereignty and enterprise-grade security

Your data stays yours.

Flexible deployment on public or the client's private cloud.

EU storage for European companies.

Zero training

Your information never leaves your environment to train models.

Contractual and technical guarantee.

GDPR and AI Act compliance.

Human-in-the-loop

No knowledge is incorporated without human supervision.

The semantic agent requires expert confirmation before integrating and sharing any fact.

Role-based access

Granular Role-Based Access Control (RBAC).

Each user accesses only what corresponds to them per their permissions.

Every answer is fully traceable.

The number that matters to the CFO

Dixtra isn't traditional software. It's a multiplier of profitability.

Answers in seconds, not weeks.

Expert answers come almost instantly because Dixtra mobilizes every layer at once, in real time.

Decisions with your real business context.

Dixtra gives you all the knowledge you need to decide with the confidence that sources are traceable.

Less time searching. More time deciding.

Your most qualified people focus on adding value, not on hunting and gathering information from scattered systems.

Let's start by understanding you

30 minutes to understand your business context.

The Context Diagnosis is a private workshop where we identify where your organization's critical context lives and whether there's any leak.

No cost. No purchase commitment. With a deliverable map in 48h.

Without context, AI improvises. With Dixtra, it understands your business.

Request a diagnosis

Got questions?

Frequently asked questions

What is a Business Context Engine?

A Business Context Engine is a system that maintains the real knowledge of a business —what its data means, how it relates, and what rules apply— and makes it queryable by people and AI agents. Unlike a data warehouse or a RAG, it preserves the meaning and state of the business, not just the data. Dixtra is the first commercial implementation of this category.

How is a Business Context Engine different from an LLM or a general AI assistant (ChatGPT, Claude, Gemini)?

LLMs reason very well, but they don't know your business: they don't know what each metric means or what rules your company applies, and connecting them raw to your systems doesn't solve it (they see tables and columns, and give probabilistic answers). Dixtra doesn't compete with LLMs; it's the context layer that builds on them and gives them the validated meaning of your business, so the answer is reliable and deterministic.

Isn't Dixtra just a RAG?

No. A RAG retrieves text fragments to answer a specific question. Dixtra maintains a living model of the business —structured, expert-validated, and traceable— and uses retrieval as one of its mechanisms, not the only one. RAG is a technique; Dixtra is the platform that preserves the state of the business.

How does Dixtra avoid AI hallucinations?

Dixtra answers only from your business's validated context and delivers each answer with its verifiable source. By relying on expert-approved knowledge (human-in-the-loop) instead of generating text freely, it avoids plausible but false answers.

Does Dixtra retrain AI models with my data?

No. Dixtra applies a zero-training policy, contractual and technical: your data doesn't train any model, neither its own nor third parties'. It applies granular role-based access control and complies with GDPR and the AI Act, with deployment on private cloud or your own infrastructure if you need it.

Do I need a data warehouse to use Dixtra?

Yes. Dixtra doesn't connect to each system separately (ERP, CRM…), but to the data warehouse where that data is consolidated (Snowflake, BigQuery, Redshift, Databricks, or equivalent). If a company doesn't have an operational data warehouse, the prior step is data architecture, outside Dixtra's scope.

What happens to expert knowledge when a key person leaves?

Dixtra captures, validates, and maintains that knowledge in a layer accessible to the whole organization. When an expert leaves, their knowledge stays in the system, available to the rest of the team.

How do you get started with Dixtra?

With a Context Diagnosis: a private 30-minute workshop where we map your business's critical knowledge and where it's broken. No cost and no purchase commitment.