An AI Chatbot That Truly Understands Your Customers
Custom-built, powered by your data, integrated with your business tools: deploy a conversational assistant that responds accurately 24/7 and frees your teams from repetitive tasks.
What is a custom AI chatbot ?
An AI chatbot is a conversational assistant powered by large language models (LLMs) that understands natural language questions, accesses your business data, and responds as relevantly as one of your best employees would.
Unlike the rigidly scripted chatbots of the 2010s, modern AI chatbots rely on techniques like RAG (Retrieval-Augmented Generation) to draw from your knowledge base, CRM, or business APIs, and provide contextualized, traceable, and reliable answers.
When do you need an AI chatbot ?
Signs that the time is right :
Your customer support is overwhelmed by repetitive questions
Your users are searching for information in a dense knowledge base
You want to offer 24/7 service without continuous hiring
Your sales teams are wasting time qualifying leads
Your employees need quick access to internal procedures
You want to automate business actions (booking, quotes, tickets)
What an AI chatbot can do for you
Automated Customer Support
Dynamic FAQ, resolution of simple incidents, order tracking, intelligent escalation to a human when necessary.
Document Assistant (RAG)
Conversational search in your knowledge base (PDF, wiki). Ideal for reducing level-1 IT tickets or assisting your employees with HR processes by citing its sources.
Lead Qualification
Guided conversation on your website, needs collection, automatic scoring, transfer to the right sales rep with context.
Transactional Agent
The chatbot executes actions : creating a quote, making an appointment, opening a ticket, updating a CRM via your APIs.
From idea to production in 4 steps
Scoping & Workshops
We identify your priority use cases, your data sources, and your technical and security constraints. Deliverable: a shared functional specification document.
1 à 2 weeksPrototype (POC)
Development of a functional prototype on a limited scope to validate business relevance and answer quality before industrialization.
3 à 5 weeksIndustrialization
Integration with tools (CRM, ERP, website, Teams, Slack), scaling, security, end-to-end testing, and team training.
6 à 10 weeksMeasurement & Improvement
Tracking KPIs (resolution rate, satisfaction, cost per interaction), continuous enrichment of the knowledge base, prompt adjustment.
ContinuousThe right chatbot level for your needs
We adapt the technical complexity and functional scope to your actual challenges, not the other way around.
How we deployed an AI chatbot in 8 weeks
Context
B2B services company, 50,000 active customers. Support receives 1,200 tickets/month, 70% of which are recurring questions (billing, contracts, order status).
Solution
AI chatbot based on GPT-4o with RAG architecture across 3 sources: internal document base, Salesforce CRM, and ERP. Deployed on the website and customer portal.
Security & Compliance
European hosting, anonymization of sensitive data, auditable logs, automatic escalation to a human on sensitive topics.
Adoption
Support team training, integration with existing dashboards, weekly feedback loop to enrich the knowledge base.
Résults
Questions about the AI chatbot
What is the difference between a classic chatbot and an AI chatbot ?
A classic chatbot follows pre-written scenarios (“if the user clicks X, answer Y”). It quickly reaches its limits as soon as a question goes off-script. An AI chatbot relies on an LLM (GPT-4o, Claude, Mistral, etc.) capable of understanding natural language, rephrasing, and reasoning over your data to produce a relevant answer, even for unanticipated questions.
Does my data remain confidential ?
Yes. We prioritize models hosted in Europe (Mistral, Azure OpenAI EU, or self-hosted open-source models) and guarantee that no customer data is used to train the model. The architecture systematically includes anonymization, auditable logs, and GDPR compliance.
Can the chatbot hallucinate or invent answers ?
This is the main risk with LLMs, and it’s exactly why we use RAG. The chatbot no longer “generates” answers from its training memory: it retrieves information from your sources and cites its references. In case of uncertainty, it escalates to a human rather than making things up.
How long does it take to get a first prototype ?
Between 3 and 5 weeks for a demonstrable POC on a targeted use case. This phase helps validate the business relevance before committing to an industrialization budget.
On which channels can the chatbot be deployed ?
Website, mobile app, customer portal, Microsoft Teams, Slack, WhatsApp Business, or integrated directly into your business software via an API. We choose the channels based on your target users.
Our other expertise in AI & automation
Generative AI
Content generation, assisted writing, image creation, intelligent summaries integrated into your business workflows.
Process Automation
Intelligent workflows, multi-system orchestration, AI-augmented RPA to automate your repetitive tasks.
AI Data Analysis
Anomaly detection, prediction, scoring, automatic classification. Turn your data into decisions.
AI Chatbot
LLM-powered conversational assistants integrated with your business data for truly intelligent 24/7 support.
