AI agents that leverage your business data for contextualized, sourced answers

RAG (Retrieval-Augmented Generation) enables an LLM to respond based on your actual business data — product catalog, internal documentation, knowledge base, customer tickets, procedures. Without RAG, the model invents answers (hallucinations). With RAG, every response is sourced, traceable, and contextualized. It's the difference between a generic chatbot and a true expert on your organization who cites their sources.

All your structured and unstructured data: product catalogs (PIM, ERP), technical documentation, internal knowledge base, FAQs, business procedures, support tickets, contracts, financial reports, CRM data, application logs, e-commerce data (orders, reviews, behaviors). The data pipeline connects to your existing sources via APIs, exports, or dedicated connectors. We structure and index each data type with the most suitable chunking strategy.

Each AI agent is configured with specialized system prompts per domain (support, sales, HR, finance, ops), filtered access to relevant data sources for its scope, and contextual personalization based on user profile and history. An orchestration system automatically routes queries to the most relevant agent. Result: a retail support agent doesn't respond like an HR agent or a technical agent — each has its own expertise and data.

Data science is involved at every stage: exploratory data analysis to identify patterns and gaps, embedding and chunking strategy optimization, systematic response quality evaluation (faithfulness, relevance), user query clustering to identify uncovered cases, and model fine-tuning or distillation for specialized performance. It's an iterative process: usage data feeds continuous improvement.

The audit and data pipeline range from EUR 25K to 50K (5-7 weeks). A complete RAG POC with conversational agent costs between EUR 20K and 40K. Industrialization with IS integrations and multi-domain agents represents EUR 80K to 250K depending on complexity. Running costs (inference, vector database, infrastructure) range from EUR 500 to 15,000/month depending on volumes. We always deliver a TCO estimate with expected ROI per use case.

Systematic guardrails architecture: source validation (the agent can only respond based on indexed documents), hallucination detection through cross-verification, confidence scoring on every response, human-in-the-loop for critical decisions, complete audit trail, and continuous quality monitoring in production. Every response cites its sources — if the agent doesn't find a reliable source, it says so rather than making things up.

Multiple architectures depending on your requirements: open-source models (Mistral, LLaMA) deployed in your private VPC — no data leaves; cloud APIs with DPA and contractual non-retention guarantees (Azure OpenAI, AWS Bedrock); self-hosted vector database with at-rest and in-transit encryption; or hybrid architecture combining cloud and on-premise. The strategy is defined during the audit phase based on your GDPR and industry constraints.

A classic chatbot responds with pre-scripted answers or an LLM without business context. Our RAG approach leverages your entire data estate — e-commerce, application, document data — via an industrialized data engineering pipeline. The agent understands business context, cites its sources, adapts to the user's profile, and continuously improves through the data science loop. It's an augmented business expert, not an upgraded FAQ.