We design artificial intelligence systems tailored to your data, your processes and your business. Document RAG, autonomous agents, specialized assistants. Deployed in production, GDPR-compliant, hosted in Europe.
A custom AI is an artificial intelligence system designed specifically for a company's data, processes and objectives. Unlike a generic AI (ChatGPT, Gemini in standard usage), a custom AI is tuned to understand your business, your internal documents and your workflows, with the guardrails required for professional use.
Most companies have tried generative AI at some point. They opened ChatGPT, asked a few questions, and found it impressive. Then they tried to turn it into something useful day-to-day, and reality set in: a generic AI doesn't know your product catalog, doesn't read your contracts, doesn't understand your customers. It answers confidently, sometimes off the mark.
A custom enterprise AI changes the game. We give it access to your data (with appropriate permissions). We tune it to answer in your business language. We add guardrails so it says 'I don't know' instead of making things up. The result: a tool that becomes useful instead of just impressive.
Here are the cases where custom AI delivers the most value, observed across our projects in e-commerce, industry, communication and services.
Query your PDFs, contracts, manuals, product sheets in natural language. With sources cited in every answer.
Customer support assistants connected to your CRM, catalog and internal policies. First-level requests are resolved without humans.
Structured extraction from your invoices, quotes, emails, reports. Classification, summarization, alerts on weak signals.
One copilot per function: sales (lead qualification), legal (contract analysis), production (predictive maintenance), HR (recruitment).
Beyond chat: agents that execute multi-step tasks (write an email, create a product sheet, call your business APIs).
Multilingual product sheets, SEO descriptions, translations following your guidelines. Aligned with your tone, vocabulary, regulations.
There are three families of techniques for building a custom AI. We pick during the audit, never before, because the right choice depends on your data, your budget and your governance constraints.
Default approach for most cases. Your documents are turned into embeddings and stored in a vector database (Pinecone, Qdrant, or self-hosted). For each question, the system retrieves relevant passages and an LLM (GPT, Claude, Gemini or open-source model) crafts the answer with sources.
Advantages: instant updates (adding a document means it's immediately usable), traceable answers, controlled cost. A RAG chatbot is often the right first AI project.
When a simple chat is no longer enough. An agent can decide on several actions, call tools (your APIs, your CRM, your catalog), chain steps. Tool calling architecture, programmatic guardrails to limit the risk of cascading errors.
Example: a sales agent that qualifies an incoming prospect, checks their history in the CRM, prepares a draft quote and notifies the sales rep with a summary.
Reserved for cases where RAG isn't enough: very specific tone of voice, rare technical language, massive volumes of repetitive tasks. We train a dedicated model on your data. More expensive, slower to update, but ultra-specialized.
Our advice: start with a RAG. We move to fine-tuning only if the needs justify it and the ROI is proven.
Measure, fix, stabilize. Three words that sum up the way we work, applied to every AI project.
Mapping of data, processes, tools. Identifying high-ROI use cases.
A functional MVP on your real data. You test it before any industrialization commitment.
Production deployment, guardrails, monitoring, team training. Documentation and code delivered.
No mystification, no gratuitous jargon. At every step, you see what we do, why, and where we are. If the prototype doesn't convince, we stop there and both sides have learned something, without committing an industrialization budget.
Every AI project is different. A document assistant for a law firm has nothing to do with a sales qualification agent for an e-commerce site. Rather than throwing price ranges that mean nothing before understanding your need, we set up a first contact that actually serves a purpose.
Synapse, our AI advisor, asks the right questions to scope your project: your business, your data, your constraints, what you have already tried, what you really want to change. No 20-field form. A real conversation, at your pace, taking ten to fifteen minutes.
At the end of the discussion, you receive a written summary of your project as Synapse understood it. You can correct it, complete it, share it internally. It's already a useful working document, even if you decide afterwards not to continue with us.
In parallel, the Synapse Up team receives a pre-chewed analysis of your need (likely use case, estimated complexity, technical points to watch). We get back to you within a few business days with a concrete proposal: a scoped audit, a targeted demo, or simply a deeper discussion, depending on what makes sense.
No express quote sent before we understand each other. No sales discovery session that drags on for three meetings. The conversation with Synapse sorts things out upfront, and you save time.
An enterprise AI is only valuable if you can use it with peace of mind. All our solutions are designed to comply with GDPR and anticipate the obligations of the EU AI Act. Not as an option, by default.
We keep up with European regulatory changes, including obligations coming into force for high-risk AI systems. If your case falls under a specific category (health, finance, HR, justice), we discuss it from the audit phase.
No fixed price. Every project is unique and a number thrown in the air helps no one. That's why we start with a conversation with Synapse, our AI advisor, to scope your need, then with a short audit of your processes. At the end of that audit, you get a firm quote based on the real scope, not a finger-in-the-wind estimate.
A generic AI answers general questions using its training knowledge. A custom AI works on your data, your processes, your business language. It understands your catalog, your contracts, your customers. It's the difference between a consultant who just arrived and an employee who knows the house.
We move fast. The prototype arrives quickly after the audit, and production deployment follows right after. The exact timing depends on project complexity and data availability, the audit will spell it out. Our principle: no sliding schedule dragging on, we frame things from the start to deliver.
Yes, provided it's designed with that constraint from the start. We host models and data in Europe, we never use your data to train third-party models, and we put in place the technical measures (encryption, isolation, audit) required by GDPR and the EU AI Act.
RAG (Retrieval-Augmented Generation) is an architecture where an AI model fetches information from your document base before answering. Concretely, your PDFs, contracts, product sheets and emails become a knowledge base queryable in natural language, without having to retrain the model, and with sources cited in every answer.
Yes. We host solutions on European infrastructure (Scaleway, OVH, Azure EU, AWS Europe depending on the project) and use models available in the EU zone. For strong sovereignty needs, we deploy on-premise or on Belgian private cloud.
In 90% of business cases, RAG is enough and preferable: cheaper, faster to update, citable sources. Fine-tuning only makes sense in very specific cases (tone of voice, rare business language, massive volumes). Our audit determines the best approach for your case.
The three main risks: hallucinations (confidently invented answers), data leaks (bad permission setup), vendor lock-in (technical or pricing). We address all three with programmatic guardrails, a secure data architecture, and a firm principle: you leave with your code.
Start with ten minutes with Synapse. He asks the right questions, you leave with a useful recap, and our team gets back to you with a real proposal.