IT systems & LLM building blocks
Enterprise LLM integration: connect AI to your real tools without breaking your IT stack.
Using ChatGPT in a browser is consumption. Connecting GPT-4 or Claude to your CRM, your ERP, and your contracts is integration: the AI sees your data in context, acts inside your tools, and triggers workflows — no copy-pasting.
4–8 weeks · Fixed fee · EU-hosted by default
What LLM integration is, and why you probably need it
Using ChatGPT from a browser is consumption. Connecting GPT-4 or Claude to your HubSpot CRM, your Sage ERP, and your Notion contracts is integration. The difference? With real LLM integration, your AI sees your data in context, acts inside your tools, and triggers business workflows — no copy-pasting, no human middleman.
This is the technical foundation of any serious AI agent. Without integration, your AI stays a generic assistant. With it, it becomes a digital colleague who knows your business and your customers.
Three integration modes: picking the right one for your case
From the simplest to the most sophisticated — depending on data sensitivity, volume, and expected real-time performance.
Direct APIs (simple, fast to deploy)
The LLM calls your tools' APIs directly (HubSpot, Stripe, Slack, etc.) via function calls. Suited to limited integrations (5–10 tools max). Timeline: 2–4 weeks.
RAG — Retrieval-Augmented Generation
Your internal documents are indexed in a vector engine (Pinecone, Qdrant). The LLM retrieves the relevant passages before answering. Essential for internal document search. Timeline: 3–6 weeks.
MCP — Model Context Protocol (open standard from Anthropic, 2024)
Modern architecture: each internal tool (CRM, ERP, Drive) exposes an MCP server that the LLM consumes. Scalable, maintainable, our recommended choice for long-term integrations. Timeline: 4–8 weeks.
Which LLM to choose: our decision matrix
No single LLM wins every use case. We choose based on three criteria: reasoning quality, sovereignty constraints, and marginal cost per request.
Katalyx's 2026 LLM selection matrix:
- GPTGPT-4o / GPT-4.1 (OpenAI via Azure EU): robust, mature ecosystem, low latency. Default choice for 70% of cases.
- ClaudeClaude Sonnet 4 (Anthropic): long, nuanced reasoning and strong writing quality. Excellent for legal, advisory, and content work.
- MistralMistral Large 2 (self-hosted): maximum data sovereignty, native French. For healthcare, defense, sensitive legal work.
- OSSLlama 3.3 / Qwen 2.5 (open-source, self-hosted): full control, zero data leakage. Hosting cost to factor in.
- GeminiGemini 2 Flash (Google): excellent cost / speed ratio for high-volume processing (< €0.002 per request).
Typical LLM integrations we deliver
- LLM ↔ CRM integration (HubSpot, Salesforce, Pipedrive): automatic enrichment, scoring, personalised email drafting.
- LLM ↔ ERP integration (Sage, SAP, Cegid): quote generation, variance analysis, delay predictions.
- LLM ↔ document base integration (Drive, SharePoint, Notion, Confluence): multilingual semantic search.
- LLM ↔ messaging integration (Outlook, Gmail, Slack, Teams): automatic triage, reply suggestions, escalation.
- LLM ↔ ticketing integration (Zendesk, Intercom, Freshdesk): classification, summarisation, reply suggestions.
Data sovereignty, GDPR, AI Act: our guarantees
We have been integrating LLMs into regulated environments since 2023. Here are our default commitments:
- Guaranteed EU hosting: Azure EU, AWS Paris, OVHcloud, Scaleway. Data never transits outside the EU.
- No reuse: signed DPA contracts prohibiting reuse of your data for model training.
- End-to-end encryption: TLS 1.3 in transit, AES-256 at rest, key management via KMS.
- Logs and audit trail: every LLM request is traced (who, when, what). 12-month retention, exportable.
- AI Act compliance: classification of your use case (limited / high risk), documented risk assessment.
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. »
Six questions on the technical side
The topics a CTO or IT director wants settled before signing off on a budget: MCP, RAG, API costs, legacy systems.
What is the difference between LLM integration and AI agent design?
LLM integration is the technical layer: connecting a model to your tools. AI agent design is the business layer: orchestrating the LLM to solve a use case. You can integrate an LLM without designing an agent (useful for your internal developers), but you cannot design a serious agent without integration.
Can you work with our existing APIs?
Yes, that is even the standard scenario. We read your API documentation, or write it if it is missing. REST/GraphQL APIs are native for us, and we also know how to connect to SOAP / EDI / legacy files.
What exactly is MCP?
The Model Context Protocol is an open standard created by Anthropic in 2024. It describes how an LLM accesses external tools in a uniform way. Rather than coding one integration per tool, you expose one MCP server per tool and the LLM consumes them. Maintainable and scalable. It is our default choice for long-term integrations.
How much does an LLM integration cost?
€8,000–€12,000 excl. VAT for a simple integration (1–3 tools, < 4 weeks). €15,000–€28,000 excl. VAT for a complex integration (RAG or MCP, multiple tools, advanced security). Always fixed-fee.
Can you self-host an open-source LLM for us?
Yes. Self-hosted Llama 3.3 or Mistral Large on OVHcloud or your own data center. Budget +4 weeks of setup time, +30–50% budget, and a monthly infrastructure cost (€3,000–€12,000/month depending on volume).
How do you manage LLM API costs?
Three levers: choosing the right model (Haiku / Flash for volume, Sonnet / GPT-4 for quality), semantic caching (typically 40–60% savings), and smart routing (lightweight model first, escalate if needed). Marginal cost becomes predictable.
Connect AI to your IT stack with no hidden debt.
Fixed fee announced before signature. EU hosting, DPA, request traceability. We scope the perimeter together with your infrastructure teams.
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