How long until a first usable result?

90 days on average from kick-off to production on a first use case. A working prototype is usable internally from week 3. We deliberately limit duration to avoid tunnel effect and keep teams motivated.

Our data is sensitive (quality, patents, supplier contracts). How do you guarantee confidentiality?

Security is built in from diagnosis step 1. Depending on sensitivity: Azure EU / AWS Paris hosting for standard cases, or a sovereign stack (on-premise Mistral, OVHcloud, Scaleway) for critical data. Controlled access, full logging, human validation on sensitive actions. No data leaves your perimeter.

Our IT landscape is complex (Sage / SAP ERP, CMMS, scattered documents). Can you connect to it?

Yes. That is our job. Connection via REST API, export files, legacy EDI, or proprietary systems. Every building block is documented and maintainable by your IT team. No black box.

We already tried an AI project that did not stick. What changes with your method?

Four critical factors: 1) limited scope from the start (one use case), not a large programme; 2) end users involved from design, not only at the end; 3) data validated and qualified before development; 4) architecture designed to last, not a throwaway prototype. Often one of these 4 points caused the previous project to fail.

How much does an industrial AI integration project with Katalyx cost?

Diagnosis: €4,900 excl. VAT (5 days). Design + development + deployment: €15,000 to €45,000 excl. VAT depending on use-case complexity. Monthly run (maintenance, monitoring, evolutions): from €1,200 excl. VAT/month. Firm pricing, never open time-and-materials.

Can you take over an AI project already started by another provider?

Yes. We start with an audit of the existing stack (code, architecture, data, documentation), identify quick wins and critical technical debt, and propose a takeover plan. Takeover audit fee: €1,800 to €3,800 excl. VAT depending on complexity.

When should we involve our DPO and CISO?

From diagnosis step 1. Security and compliance are framing topics, not end-of-project topics. We systematically work as a Business / IT / DPO-CISO triad to avoid later blockers.

Method · Industrial AI Agent4 steps · 90 d · Controlled scope

Katalyx Method · Industrial AI Agent

Put AI on your shop floor in 90 days. No throwaway prototype. No mega-programme. No broken ERP.

Our AI integration method for industrial companies runs on 4 clear steps. We start with one business use case, secure it technically from day one, and validate it with your users before expanding. No slide decks. No demos that impress without delivering. Field proof in 90 days.

Book my 30-min diagnosis Download the full method (PDF)

4 steps · 90 days · Controlled scope

Generic AI does not work on the shop floor

An impressive demo is not a solution that works.

In an industrial context, an AI solution cannot be treated as a generic tool. It must work with established business processes, sometimes heterogeneous data, confidentiality constraints, existing systems such as ERP, CMMS or document repositories — and above all users who expect a concrete operational gain.

So our approach starts on the ground: identify a precise business pain, check data availability, build a controlled scope, then deliver a first solution that is genuinely usable before considering an extension. Not the other way round.

Why does this inversion change everything? Because in 4 out of 5 industrial AI projects that fail, the problem is not the technology. It is framing. Teams wanted too much, too fast, without checking that data would follow or that users would adopt. Our method exists to neutralise that structural risk.

Start small, build solid

Large AI programmes fail. Well-run first use cases succeed.

We do not recommend launching a large cross-cutting AI programme from day one. That approach often creates complexity, delays decisions and dilutes ownership. We favour a first targeted integration on a high-potential use case, with measurable success criteria.

A limited initial scope — one department, one business flow, or one priority use case. Anything but the entire plant in sprint 1.
An architecture designed to be maintainable, secure and extensible — from the first prototype. No hidden technical debt to pay in 6 months.
Validation by end users — before any wider rollout. Your operators, technicians and buyers validate in real conditions, not a meeting-room demo.
Simple performance indicators — time saved, adoption rate, answer quality, error reduction, process acceleration. Not a dashboard with 40 KPIs.

From your field need to an AI agent in production

4 steps. Not one more. Not one less.

Step	Objective	Expected outcome
1. Diagnosis & framing	Understand business stakes, existing tools, data maturity and security constraints.	A prioritised, realistic, costed scope.
2. Functional & technical design	Define uses, business rules, data to mobilise, architecture and validation conditions.	A clear delivery plan, validated by stakeholders.
3. Development & integration	Build the AI solution, connect useful sources, set up access controls and traceability.	A working, integrated solution testable in real conditions.
4. Acceptance, deployment & improvement	Test with users, fix, train, measure, prepare extensions.	A solution that is used, documented and steered over time.

Step 1 / 4 · 1 to 2 weeks

Before you invest, you will know exactly what works — and what should wait.

This first step checks that the anticipated use case can genuinely create value. It avoids three classic traps: premature estimates sketched on a napkin, fragile technical promises, and AI projects built on insufficient data.

The business processes involved and low value-add tasks that can become an AI use case.
Available data sources: ERP, Excel, PDF, technical documents, history, CMMS, CRM, internal databases.
Data quality, accessibility, sensitivity and update frequency.
Security, confidentiality, traceability and compliance constraints (GDPR, AI Act, sector standards).
User profiles, daily friction points, and their capacity to adopt a new tool.

Step 2 / 4 · 2 to 3 weeks

You validate every choice before a single line of code. No mid-project surprises.

Design turns the business need into a concrete integration plan. It specifies what the solution must do — and especially what it must not. Which data it may use, which rights apply, how results will be validated. At the end of this step, your team knows exactly what it is buying. And we know exactly what we deliver.

Topic	Questions addressed
Business use	Who uses the solution? When? To produce what concrete result?
Data	Which sources are used? Are they reliable, up to date, accessible?
Business rules	Which constraints must be respected? Which human validations are mandatory?
Security	Which access is allowed? Which data must stay confidential? Which logs?
Validation	Which criteria mean the solution is ready for production?

Step 3 / 4 · 4 to 8 weeks

Even your first scope is built to last. No throwaway prototype.

Delivery is not designed as a throwaway prototype. Even when the first scope is narrow, the goal is clean foundations: documented architecture, user rights, exchange security, tests, documentation and room to evolve. The logic: if use case 1 works, you will want a second, then a third. We give you the foundations for that.

Set up a technical environment suited to data sensitivity (EU cloud, on-premise, hybrid).
Connect required sources: document repository, exports, ERP API, business tools, internal databases.
Build the AI assistant, agent or augmented document search engine.
Configure rights, roles, consultation rules and full interaction logging.
Technical tests, functional tests, adjustments before user acceptance.
User and technical documentation delivered with the code. Not after.

Step 4 / 4 · continuous follow-up after go-live

Once your teams have used the solution for 30 days, you will know exactly what it is worth.

An AI solution should not be judged on a demo alone. It must be assessed on its ability to help teams in daily work. Acceptance confronts the solution with real cases brought by your users, checks answer or action quality, identifies gaps and fixes them before wider rollout.

After go-live, we track simple indicators — chosen with you in step 1 — to objectify value created and identify extension paths.

Weekly and monthly active users.
Usage volume (queries, triggered actions, documents generated).
Time saved measured by survey and field sampling.
Human validation rate on sensitive actions.
Anomalies detected and fixed (hallucinations, bias, business errors).
Qualitative team feedback: internal NPS, friction identified, evolution requests.

Built in from step 1. Not bolted on at the end.

We do not add security afterwards. We start with it.

Security is built into framing, not added at the end of the project. Architecture level depends on data sensitivity, client constraints and expected integration depth. For highly sensitive data (patents, customer data, confidential contracts), we propose a sovereign stack (self-hosted Mistral Large, OVHcloud / Scaleway / on-premise). For standard cases, Azure EU Paris or AWS Paris hosting is enough.

Principle	Concrete application
Data control	Identify data used, location, sensitivity and associated rights. No data is processed without prior mapping.
Controlled access	User roles defined explicitly. Access limited to information based on operational need (least privilege).
Traceability	Logging of useful interactions to track usage, control errors and audit. Logs retained at least 12 months.
Secured exchanges	Data protection in transit (TLS 1.3) and at rest (AES-256). Key management via KMS.
Human validation	Always required when AI triggers a sensitive action (order, ERP change, external communication).

What we have already delivered · What we can deliver for you

5 industrial use cases that have passed the field test.

Domain	Use case	Value sought
Engineering office	Augmented document search across specifications, reports, drawings and project history.	Cut search time, reuse existing knowledge more easily, accelerate quoting.
Quality	Non-conformity analysis and recurring pattern detection.	Speed up root-cause identification, strengthen quality tracking, anticipate deviations.
Maintenance	Help analysing intervention history and preventive recommendations.	Anticipate failures better, prioritise actions, optimise spare-parts stock.
Purchasing / inventory	Gap detection, stockout alerts, replenishment proposals.	Reduce repetitive tasks, improve operational reactivity, secure the chain.
HR / admin	Internal assistant on procedures, contracts, documents and onboarding.	Smooth access to information, standardise internal answers, free HR time.

When our method really works

Our method is effective. But it needs 5 conditions on your side to deliver fully.

An AI project rarely succeeds because the technology is impressive. It succeeds when the use case is well chosen, data is usable, users are involved, and leadership steers the project with concrete goals. Here are the 5 success conditions we check together from diagnosis step 1.

A business sponsor who can validate use-case priority and decide quickly.
A technical or IT lead to facilitate access to data and existing systems.
End users involved from the design phase — not only consulted at the end.
A controlled initial scope, with explicit, measurable success criteria.
Simple governance: regular check-ins, deliverable validation, fast decisions.

Visibility, deliverables, measurement

Before, during, after. You always know where the project stands.

Before build	During build	After deployment
A prioritised use case, available-data analysis, clear costed scope. An informed Go/No-Go decision.	A solution built progressively, tested with the business, documented. Regular validation points at each milestone.	A usable, measured, maintained tool that can improve over time. Progressive skills transfer to your internal team.

At every step, the goal is visibility for the client: on scope, risks, technical choices, deliverables, timeline and success conditions. That transparency secures the decision before investing in a wider build — and keeps control throughout the project.

← See all our methodsExplore our other methods

Same principles. A method adapted to your context.

Google reviews

What our clients say

5.0 out of 5 · 13 reviews

Source: Google

Mohamed Seghir
il y a 2 mois
« Très belle collaboration avec l'équipe Katalyx sur le développement d'iZola. Projet exigeant techniquement (KYC biométrique, signature électronique, scoring), tenue des délais, qualité du code, et surtout un vrai dialogue produit du début à la fin. Un mot pour Victor, notre chef de projet : disponibilité, rigueur, écoute, et cette capacité rare à anticiper les sujets avant qu'ils deviennent des problèmes. Une vraie différence sur un projet de cette ampleur. Merci à toute l'équipe — on continue l'aventure ensemble. À recommander. »
Marie-Anne Falconet
il y a un mois
« Nous avons fait appel à Katalyx pour le développement d'une appli de reconnaissance d'images par IA pour identifier les jouets que nous reconditionnons. Tout le process a été hyper pro, super suivi, documentation détaillée, disponibilité de l'équipe, échanges très fluides. Je recommande vraiment pour tous vos projets, ce sont de vrais experts ! »
Enzo Gauben
il y a 2 mois
« La société Katalyx a permis à notre entreprise de développer et designer notre application iZola de 0. Nous recommandons grandement leur service et leur rigueur qui nous a permis d’avancer en toute sérénité sur le développement de cette dernier de A à Z. Merci à toute l’équipe Katalyx 🤝 Enzo »
Fernandes Denis
il y a 10 mois
« Katalyx nous a accompagné avec une approche très structurée pour développer de nouvelles solutions. Leur compréhension de nos enjeux stratégiques et leur capacité à transformer une idée en plan d’action concret font vraiment la différence. Je recommande vivement pour toute entreprise qui veut accélérer son développement »
Jérôme Staszak
il y a 10 mois
« J’ai eu l’occasion d’échanger à plusieurs reprises avec Enguerrand dans un cadre entrepreneurial. Ce qui m’a marqué, c’est sa capacité à penser “stratégie” avant d’agir, à chercher des solutions concrètes plutôt que des promesses. Son approche est jeune, mais déjà structurée et tournée vers le développement de solutions qui font sens pour les entrepreneurs. Un bel état d’esprit, beaucoup de clarté et une vraie orientation résultat. »

See all reviews on Google

FAQ · Industrial AI Method

Seven industrial objections.

Security, complex IT, prior failure, budget — the questions we hear before every diagnosis.

01
How long until a first usable result?
90 days on average from kick-off to production on a first use case. A working prototype is usable internally from week 3. We deliberately limit duration to avoid tunnel effect and keep teams motivated.
02
Our data is sensitive (quality, patents, supplier contracts). How do you guarantee confidentiality?
Security is built in from diagnosis step 1. Depending on sensitivity: Azure EU / AWS Paris hosting for standard cases, or a sovereign stack (on-premise Mistral, OVHcloud, Scaleway) for critical data. Controlled access, full logging, human validation on sensitive actions. No data leaves your perimeter.
03
Our IT landscape is complex (Sage / SAP ERP, CMMS, scattered documents). Can you connect to it?
Yes. That is our job. Connection via REST API, export files, legacy EDI, or proprietary systems. Every building block is documented and maintainable by your IT team. No black box.
04
We already tried an AI project that did not stick. What changes with your method?
Four critical factors: 1) limited scope from the start (one use case), not a large programme; 2) end users involved from design, not only at the end; 3) data validated and qualified before development; 4) architecture designed to last, not a throwaway prototype. Often one of these 4 points caused the previous project to fail.
05
How much does an industrial AI integration project with Katalyx cost?
Diagnosis: €4,900 excl. VAT (5 days). Design + development + deployment: €15,000 to €45,000 excl. VAT depending on use-case complexity. Monthly run (maintenance, monitoring, evolutions): from €1,200 excl. VAT/month. Firm pricing, never open time-and-materials.
06
Can you take over an AI project already started by another provider?
Yes. We start with an audit of the existing stack (code, architecture, data, documentation), identify quick wins and critical technical debt, and propose a takeover plan. Takeover audit fee: €1,800 to €3,800 excl. VAT depending on complexity.
07
When should we involve our DPO and CISO?
From diagnosis step 1. Security and compliance are framing topics, not end-of-project topics. We systematically work as a Business / IT / DPO-CISO triad to avoid later blockers.