Enterprise AI Strategy · Paris & France

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

Everyone deploys AI chatbots. We build industrialized RAG systems that connect your conversational agents to all your data - e-commerce, application, and document data. Result: agents that understand your business, cite their sources, and continuously improve through data science. That's where we make the difference.

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The challenge

Why AI chatbots fail without a RAG and data strategy

Deploying an LLM without anchoring it in your business data is investing in a well-spoken parrot that makes up answers. The failures are predictable:

  1. 01 AI chatbots that fabricate answers (hallucinations) - no business data leveraged, no sources cited
  2. 02 Business data in silos: ERP, CRM, PIM, tickets, docs - never connected to your AI systems
  3. 03 POCs stuck at prototype stage: no industrialized data pipeline, no path to production
  4. 04 Generic AI agents that give the same answer to a customer, a sales rep, and a technician
  5. 05 Untapped e-commerce and application data: logs, transactions, behaviors - a dormant goldmine
  6. 06 No improvement loop: no data science on conversations, no continuous optimization
  7. 07 Uncontrolled inference costs: no semantic cache, no model routing, no AI FinOps strategy
  8. 08 Unmanaged risks: undetected hallucinations, uncorrected biases, GDPR non-compliant, no audit trail
Architecture

Technical overview

Recherche intelligente - Search & RAG catalogue

Fusion recherche catalogue et contenus guidés avec ranking intelligent

Entrée utilisateur
Recherche catalogue
RAG contenus
Sortie
Analytics recherche
Intention produitIntention contenu
Utilisateur
RequêteTexte libre
Détection d'intentionProduit vs contenu
Search catalogueIndex produits
IndexAlgolia, Elasticsearch
Résultats produits
RAG contenusFAQ, guides, fiches
Base contenusVectorielle + CMS
Réponse guidée + liens
Fusion / RankingScore combiné
Interface résultatsListe + guidage contextuel
Logs requêtes
CTRClick-through rate
0 résultatRequêtes sans réponse
ConversionPost-search
Source
Traitement
Stockage
Sortie
Couche

Solution comparison

What AI infrastructure for your RAG system?

The choice of model and infrastructure depends on your constraints: data confidentiality, inference volumes, budget, and desired level of control. We design the most suitable architecture - often hybrid.

OpenAI / GPT

Strengths
  • Top-performing models for content generation and reasoning (GPT-4o, o3)
  • Complete ecosystem: API, fine-tuning, assistants, function calling
  • Native multimodal (text, image, audio, vision) - ideal for document analysis
  • High-performance embeddings for RAG vector databases (text-embedding-3)
Limitations
  • High costs at scale (per-token pricing, inference + embeddings)
  • Data sent to OpenAI servers (unless enterprise opt-out or Azure OpenAI)
  • Strong vendor lock-in on proprietary APIs
  • Variable latency under load - problematic for real-time agents
Ideal for: Rapid prototyping, RAG on varied document corpora, general-purpose conversational agents, content generation

Claude / Anthropic

Strengths
  • Very large context window (200K tokens) - ideal for RAG with long contexts
  • Safety-first approach, reliable responses with low hallucination rate
  • Excellent at document analysis, complex reasoning, and structured synthesis
  • Claude Agent SDK for orchestrating multi-step AI agents in production
Limitations
  • Narrower integration ecosystem than OpenAI
  • No publicly accessible fine-tuning (prompt engineering only)
  • More recent enterprise adoption (but growing rapidly)
  • Third-party embeddings required (Cohere, Voyage AI, or open-source)
Ideal for: RAG on large documentation, complex business agents, compliance-critical, contract/report analysis

Open-Source (Mistral / LLaMA / Qwen)

Strengths
  • Full control over model and data - no data leaves your infrastructure
  • Fine-tuning possible on your business data for specialized performance
  • On-premise deployment, private VPC, or sovereign cloud
  • Predictable costs at scale (no per-token cost, fixed GPU)
Limitations
  • Requires ML/MLOps expertise and GPU infrastructure (A100, H100)
  • Lower performance than proprietary models on some general tasks
  • Complex and costly fine-tuning and RLHF (compute and annotated data)
  • Model maintenance and updates are your responsibility
Ideal for: Sensitive data (healthcare, finance, defense), high inference volumes, sovereignty required, business fine-tuning

Cloud AI (AWS Bedrock / GCP Vertex AI)

Strengths
  • Integrated with your existing cloud infrastructure and data pipelines
  • Managed services: scaling, monitoring, enterprise security, native audit trail
  • Certified compliance (SOC 2, HIPAA, GDPR) - essential for sensitive data
  • Multi-model access via unified API (Claude, Mistral, LLaMA, Titan, Gemini)
Limitations
  • Tight coupling with chosen cloud ecosystem (platform lock-in)
  • Inference costs sometimes higher than direct APIs
  • Available models lag behind direct releases
  • Complex initial configuration (IAM, VPC endpoints, KMS)
Ideal for: Companies already on AWS/GCP, strict compliance, multi-model strategy, integration with existing data lakes
No technology dogma. We recommend the solution best suited to your context, constraints and ambitions. Every choice is documented and justified.

Our methodology

How we deliver

Architects with twenty years behind them, and a delivery process instrumented by AI: scoping, code review, quality and security tooled at every phase. Each step produces concrete deliverables, and we stay through the run.

01
2 to 3 weeks

AI, Data & Maturity Audit

Assess your organization's AI maturity and especially your data quality. 80% of a RAG system's success depends on upstream data quality. We map everything: available data, silos, formats, volumes, and internal capabilities.
Deliverables
  • AI maturity diagnostic for the organization (levels 1-5 per domain)
  • Complete data source mapping (ERP, CRM, CMS, PIM, tickets, logs, document repositories)
  • Data quality audit: completeness, freshness, structure, duplicates, formats
  • Exploitable document assets evaluation for RAG (PDFs, docs, wikis, FAQs, procedures)
  • Business interviews to identify high-potential AI automation processes
  • Inter-system data flow analysis and silo identification
  • Industry benchmark: what are your competitors doing with AI and RAG?
  • Internal skills assessment (data engineering, ML, prompt engineering)
  • Diagnostic report with maturity matrix and data remediation plan
02
3 to 4 weeks

Data Engineering & Data Pipeline

Build the data foundation that will power your AI systems. Extraction, cleaning, transformation, and structuring of your business data. Without a robust data pipeline, no performant RAG. This is where data science comes into play.
Deliverables
  • Data pipeline architecture: ingestion, transformation, indexing (ETL/ELT)
  • Connectors to your data sources (APIs, SQL/NoSQL databases, files, controlled scraping)
  • Data cleaning and normalization pipeline (deduplication, enrichment, validation)
  • Document chunking strategy optimized by content type (semantic, recursive, by section)
  • Vector embedding generation and storage (OpenAI, Cohere, or open-source models)
  • Vector database setup (Pinecone, Weaviate, Qdrant, pgvector depending on context)
  • Incremental data update pipeline (real-time or batch depending on sources)
  • Data quality metrics: coverage, freshness, consistency, source traceability
  • Documented data catalog: schema, origins, refresh frequency, owners
03
4 to 6 weeks

RAG Architecture & AI Agent POC

Design and prototype the complete RAG system: multi-source retrieval, reranking, augmented generation, and contextualized conversational agent. The POC is tested with real business data and real users - not a PowerPoint demo.
Deliverables
  • Complete RAG architecture: query understanding > retrieval > reranking > generation > guardrails
  • Hybrid retrieval pipeline: vector search (semantic) + BM25 (lexical) + metadata filters
  • Reranking system to optimize result relevance (cross-encoder, Cohere Rerank)
  • Advanced prompt engineering: system prompts contextualized by business/project type
  • Conversational agent with conversation memory and persistent user context
  • Source citation mechanism: every response references the original documents
  • RAG evaluation framework: faithfulness, relevance, answer correctness, latency, cost
  • Testing with real business users on representative scenarios
  • Comparative LLM benchmark on your data (GPT-4o vs Claude vs Mistral)
  • Argued go/no-go with quality metrics and measured ROI
04
2 to 4 months

Industrialization & Business AI Agents

From POC to product: scalable production architecture, multi-agent orchestration, IS integrations, guardrails, monitoring. Deploy business-contextualized AI agents that leverage all your data - e-commerce, application, and document data.
Deliverables
  • Production architecture: API gateway, agent orchestrator, semantic cache, high availability
  • Specialized AI agents per business domain (support, sales, HR, finance, ops) with dedicated prompts
  • Multi-agent orchestration: intelligent routing of queries to the most relevant agent
  • Integration with existing systems (ERP, CRM, HRIS, business tools) via API/webhooks
  • Unified multi-source RAG: cross-leveraging e-commerce, application, and document data
  • Contextual response personalization by user profile, role, and history
  • Guardrails and quality control system: filtering, hallucination detection, audit trail
  • AI monitoring in production: response quality, P95/P99 latency, cost per query, drift
  • Dedicated ML/AI CI/CD pipeline (MLOps): prompt versioning, A/B testing, rollback
  • Business team training on AI agent usage and feedback
  • Technical and operational documentation and business user guide
05
Ongoing

Data Science, Optimization & AI Governance

Leverage data science to continuously optimize your AI agents' performance. Conversation analysis, fine-tuning, knowledge base enrichment, and sustainable AI governance. This is where the value loop closes: usage data feeds continuous improvement.
Deliverables
  • Data science analysis of conversations: query clustering, knowledge gap identification
  • Continuous RAG knowledge base enrichment from new business data
  • Model fine-tuning or distillation on your data for specialized performance
  • Retrieval optimization: chunking, embedding, and reranking strategy adjustments
  • AI dashboard: business KPIs (time saved, resolution rate, satisfaction) and technical metrics
  • AI governance policy (ethics, bias, transparency, accountability, GDPR compliance)
  • AI committee: composition, roles, frequency, decision-making and arbitration process
  • Technology watch and new model evaluation (assisted migration)
  • Continuous inference cost optimization (semantic cache, model routing, batching)
  • Progressive deployment of new use cases from the AI roadmap
  • Quarterly AI strategy review with ROI measured per use case

Business value

What you concretely gain

Expected results

Conversational agents that know your business

Complete exploitation of your data assets

Measurable productivity gains: 20 to 40%

  1. 01

    Conversational agents that know your business

    Thanks to RAG, your AI agents respond with contextualized data from your own systems - product catalog, internal documentation, customer history. Not generic answers, but precise and sourced business responses.

  2. 02

    Complete exploitation of your data assets

    E-commerce, application, document data - the data pipeline connects all your sources to power AI agents. Your dormant data becomes a strategic asset continuously leveraged.

  3. 03

    Measurable productivity gains: 20 to 40%

    Automation of repetitive tasks, writing assistance, document analysis, instant answers to business questions. Your teams focus on high-value tasks.

  4. 04

    Sourced and traceable answers - zero hallucination

    Every agent response cites its documentary sources. Guardrails, hallucination detection, and complete audit trail. Built-in GDPR compliance, structured AI governance.

  5. 05

    Proven ROI before major investment

    Every use case goes through a measured POC with your real data and real users. You invest only in what has proven its business value.

  6. 06

    Competitive advantage impossible to copy

    RAG-powered AI trained on your business data creates a lasting competitive moat. The more it's used, the more usage data improves it - it's an internal network effect your competitors cannot replicate.

Frequently asked questions

Your questions, our answers

01 What is RAG and why is it key to high-performing business AI?
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.
02 What types of data can be leveraged in a RAG system?
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.
03 How are AI agents contextualized by business domain or project type?
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.
04 What role does data science play in setting up a RAG system?
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.
05 What budget should you plan for a RAG AI project?
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.
06 How do you ensure the AI doesn't give bad answers?
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.
07 Our data is sensitive. How do you protect confidentiality?
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.
08 What's the difference between your RAG approach and a classic chatbot?
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.

Ready to transform your data into business AI agents?

Thirty minutes with an architect - the person who will scope your project, and ship it. We assess your data estate, your priority use cases, and estimate the ROI of a RAG system on your data - no commitment.

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