Applied AI and Data Science

Applied generative AI refers to the industrialization of models like GPT, Claude, or Mistral on company proprietary data to automate business tasks. Diametral deploys tailor-made RAG systems, autonomous agents and co-pilots, with human supervision, governance and measurement of the business value generated.

A Retrieval-Augmented Generation (RAG) system combines a language model with a proprietary knowledge base to generate responses that are embedded in your documents. Diametral deploys RAGs on contracts, procedures, product catalogs, or support knowledge bases, which eliminates the hallucinations typical of generalist LLMs and guarantees the traceability of sources.

Industrializing an AI agent involves taking it out of demo mode to make it a supervised, measured and governed system. Diametral supervises each agent with safeguards (content filters, human validation on critical actions, complete logs) and measures the value generated monthly to ensure that they remain an asset and not a black box.

A POC demonstrates feasibility on a small perimeter, an industrialized AI works in production with SLA, monitoring and integration into business tools. Diametral avoids the eternal POC syndrome by framing the criteria for transition to production from the start: target volume, IS integration, governance and expected ROI.

Avoiding hallucinations is based on four practices: anchoring responses in your documents via a RAG, validating outputs through business rules, setting up human supervision on sensitive cases, and tracking each response for audit. Diametral integrates these layers by default into every enterprise generative AI deployment.