Enterprise AI Platform

The challenge

An AI platform company needed to serve 20+ enterprise customers with AI that answered from each customer's own business data — accurately, securely and explainably. Generic RAG was not enough: answers had to be grounded in governed business context, isolated per tenant, and defensible in front of enterprise security and compliance teams.

At the same time, delivery had to speed up. Product, Engineering and Data needed one architecture and one operating model that could turn enterprise requirements into configurable AI use cases — without every customer becoming a custom build.

What we did

• Architected the core AI platform: retrieval augmented generation, semantic layer, metadata architecture, vector search and context engineering
• Designed tenant isolation so every customer's data, context and AI behaviour stayed strictly separated
• Built governance into the platform: provenance on every answer, confidence scoring, permission-aware retrieval and full auditability
• Directed prompt architecture, prompt engineering and evaluation to make answer quality measurable and improvable
• Co-designed the agent architecture enabling configurable, low-code AI use cases for enterprise customers
• Ran executive discovery and onboarding workshops with CTOs, Heads of Data and Digital Transformation teams to turn business problems into production AI use cases

Why it matters

This is the difference between AI that demos well and AI that enterprises actually adopt. Grounding, governance and tenant isolation are not features — they are the architecture. The same patterns apply to any organisation that wants AI its leadership, customers and regulators can trust.