AI that speaks your business.

Why generic AI falls short for real work

ChatGPT, Claude, and Gemini are impressive general assistants. They also don't know your pricing, your contracts, your product catalog, your customer history, or your internal playbook. Ask them a specific question about your business and they'll either say they don't know — or worse, guess. Custom AI closes that gap: the model is grounded in your data, retrieval-augmented, and constrained to answer only from your sources with citations, so your team can trust it and your customers get real answers.

What we build

Custom AI covers a wider range than most people realise. On the retrieval-heavy end, we build private knowledge bases — a system your sales team asks for pricing precedents, a system your ops team asks for the correct SOP, a system your customers ask questions of on your website. On the workflow-heavy end, we build copilots — an AI that reads inbound emails and suggests responses, an AI that reviews contracts against your redline rules, an AI that watches a customer's support history and hands the agent a briefing. The common thread: yours, grounded, and evaluated.

• Private knowledge base with citation-backed answers (RAG)
• Sales, ops, and support copilots trained on your history
• Document review: contracts, invoices, expenses, RFPs
• Product-catalog Q&A grounded in your live inventory
• Multi-lingual over the same source data (FR / EN / IT / DE)
• Private LLM deployments where data cannot leave your infrastructure

The architecture, in plain terms

A custom AI system has four layers. Data ingestion pulls your sources — documents, database rows, API calls — into a shape a model can use. Retrieval finds the right chunks when a question comes in, ranked by relevance. The language model composes the answer, constrained to cite the retrieved sources. The evaluation harness scores each answer against a labelled set so we know quality is holding — and catches regressions before your users do. We pick the model (Claude, GPT, Gemini, Mistral, or an open-source model on private infra) based on where your data has to live and how much latency you can tolerate — not on which vendor a consultant is loyal to.

Security, compliance, and data residency

This is where Monaco-based buyers ask the sharpest questions, and rightly so. We design every custom AI system with three properties in mind. First: data residency — some businesses need the model itself to run on infrastructure they control, not on a US API. Second: retention — training data, embeddings, and logs all have retention rules that map to your obligations under Law n° 1.565 and the APDP. Third: audit trail — every answer the system gives can be traced back to the exact source chunks it retrieved and the exact prompt it saw, so you can defend any decision downstream of the AI.

• Model choice mapped to your data-residency requirements
• Retention rules aligned to Law n° 1.565 and APDP guidance
• Full audit trail: sources, prompts, and answers logged and reviewable
• Access controls: role-based, row-level where applicable
• Guardrails against prompt injection and data exfiltration