Custom AI Integration
As a generative AI agency, we design and deploy operational AI solutions such as business chatbots, semantic search engines, and autonomous agents, deeply integrated into your IT system and aligned with your business challenges.
Can AI truly transform your daily business operations?
Our AI integration projects begin when AI is no longer an R&D topic, but a concrete operational lever to automate, secure, and leverage your data assets.
Your teams waste time on repetitive tasks
You want to automate document analysis, data entry, report writing, or meeting minutes to free up your talent for high-value missions.
Your internal data remains unexploited
You have an immense knowledge base (PDFs, wikis, contracts, meeting minutes, databases) but it's impossible to access it quickly to answer specific questions.
Your customer service is overwhelmed
You need an intelligent conversational agent capable of understanding context, consulting your knowledge base, and resolving queries 24/7 without human intervention on levels 1 and 2.
You want to accelerate content production
Writing product descriptions, sales emails, proposals, or meeting minutes takes too much time. AI can become a true co-pilot for your employees.
You are concerned about data security
Using public AI tools poses a risk to your intellectual property. You are looking for a sovereign solution, a private instance, or an on-premise deployment to guarantee total confidentiality.
You don't know which use case to start with
AI is evolving rapidly and you need a technical partner to identify the highest ROI opportunities and validate feasibility with a costed POC before making any commitments.
The AI solutions we design
From conversational chatbots to autonomous agents, we design operational AI integration projects connected to your IT system and aligned with your business needs.
AI Agents & Business Chatbots
Creation of intelligent assistants connected to your existing tools (CRM, ERP, ticketing) to automate customer support, sales qualification, or administrative management. Includes contextual memory and function calling.
LLM · OpenAI · AgentsSemantic Search Engine (RAG)
Implementation of a RAG (Retrieval-Augmented Generation) architecture allowing you to query your internal documents in natural language with sourced, traceable, and reliable answers.
Vector DB · Pinecone · LangChainProcess Automation (Agentic AI)
Orchestration of complex task chains where AI makes decisions, uses external tools via function calling or MCP, and validates results autonomously. Discover our dedicated offer for a broader approach to business process automation.
Python · Autonomous · APIsData Analysis & Classification
Automatic extraction of structured information, lead scoring, sentiment analysis, and classification of large volumes of textual or unstructured data (emails, documents, transcripts).
NLP · Data · PrécisionOur expertise in LLM integration
We combine foundational models, orchestration frameworks, and vector databases to deliver robust, scalable, and maintainable AI solutions.
Langchain development agency
Voir la page technoOur mastery of modern AI technologies (LLMs, RAG, vector databases, orchestration frameworks) allows us to select the open-source models or proprietary APIs and tools best suited to your use case. No default tech: every LLM integration project gets an optimal architecture, balanced between performance, inference cost, and confidentiality.
Concrete deliverables for a successful AI Integration
From the initial audit to production deployment, clear deliverables at every step. The source code belongs to you, with no hidden subscriptions or lock-in.
You own 100% of your AI solution.
We design AI integrations that respect your informational assets : no data exfiltration, no use of your prompts to train third-party models, no non-European dependency when it can be avoided.
Our architectures are auditable and built to last.
Feasibility Audit & AI Roadmap
Analysis of your available data, identification of the highest ROI use cases, and risk mapping (confidentiality, bias, AI Act compliance).
Functional Prototype (POC)
Testable initial version on a reduced scope with your real data, to validate the relevance of responses, measure quality, and estimate production costs.
Technical Architecture & Vector Database
Setup of secure infrastructure: vector databases (Pinecone, Weaviate, Qdrant), embedding pipelines, authenticated APIs, and monitoring.
Industrialized Solution & Interface
Production-ready application or API, integrated into your existing tools (CRM, ERP, EDM), with guardrails, kill switches, and granular user permissions.
Documentation & Source Code
Full delivery of proprietary source code, technical documentation, operational runbook, and a prompting guide for your teams.
Why trust us with your AI projects
No vague promises. Concrete, measurable, and contractual commitments.
From exploration to production in 4 steps
An iterative process to start small, validate value, and scale what works.
Exploration & Diagnosis
Understanding your business needs, auditing available data, defining success KPIs, and mapping constraints (confidentiality, compliance, budget).
1 - 2 weeksPrototyping (POC) & Stack
Selecting models (GPT, Mistral, Claude, Llama 3), designing the architecture (RAG, agents, fine-tuning), and developing an initial prototype testable on your data.
3 - 5 weeksAgile Development & Refinement
Rapid iterations to improve response accuracy, handle edge cases, optimize token costs, and harden guardrails (anti-hallucination, anti-injection).
4 - 10 weeksLaunch & Monitoring
Secure deployment, user training, real-time performance and cost monitoring, and continuous iteration based on usage feedback.
ContinuousReady to launch your AI integration project ?
Functional POC in 4 to 6 weeks, on your data, with measurable ROI.
The experts leading your AI projects
Senior profiles at the intersection of business, data, and software engineering.
Our other areas of expertise
Frequently Asked Questions about AI Integration
What is the difference between RAG and fine-tuning ?
RAG (Retrieval-Augmented Generation) allows AI to consult your documents in real-time to formulate its answers, without modifying the base model. Fine-tuning, on the other hand, retrains the model on your data.
RAG is generally faster to implement, more reliable (sourced answers), and more cost-effective. Fine-tuning remains relevant for highly specialized domains or specific response styles.
Is my corporate data secure ?
Yes. We use private instances (OpenAI Enterprise, Azure OpenAI), self-hosted open-source models (Mistral, Llama 3), or hybrid architectures based on your criticality level. Your prompts and data are never used to train public models.
How much does an AI integration project cost ?
A feasibility POC starts at around €10,000. A fully integrated and industrialized solution generally ranges between €30,000 and €100,000, depending on the complexity of the use case, data volume, and the level of integration into the existing IT system. Beyond the build, you must account for inference costs (API tokens) and hosting.
How do we prevent AI "hallucinations" ?
Several combinable techniques: RAG architecture with systematic sourcing of answers, defensive prompt engineering, guardrails, cross-validation by a second model, and a human validation loop for critical cases. No AI is flawless; we design workflows so that errors can be detected and recovered.
Can AI be integrated into my existing software ?
Absolutely. We systematically design our solutions as APIs that interface with your current tools (SaaS, ERP, CRM, EDM). AI seamlessly steps into your existing workflows without a redesign, via authenticated calls and dedicated monitoring.
Which models to choose : OpenAI or open source?
OpenAI (GPT-4, GPT-4o) offers the best raw performance and the best tool ecosystem. Open source (Mistral, Llama 3) guarantees total sovereignty, long-term controlled costs, and the possibility of self-hosting. We help you choose based on your confidentiality, budget, and performance constraints.
