P01 / Project dossier
RAG Onboarding
Onboarded the business to a retrieval-augmented generation framework, enabling LLMs to access and ground responses in business data while addressing data-access challenges early.
Challenge
Large language models needed a reliable way to access business data and ground their responses in enterprise context. Without an established retrieval path, data availability and integration constraints risked slowing future AI scenarios.
The opportunity was identified early, before LLM data-access challenges became delivery blockers.
Approach
Led the business onboarding to a retrieval-augmented generation framework and aligned data-access needs with the framework's capabilities. Surfaced likely access constraints early and shaped mitigations as part of onboarding rather than after implementation.
What changed
Established a reusable path for LLMs to retrieve business data through the RAG framework, creating a foundation for grounded AI experiences.
Early risk identification reduced downstream uncertainty and enabled data-access challenges to be addressed before broader feature delivery.
Why it matters