AI Architecture
Designing an AI-Ready Data Platform
AI initiatives stall on data, access and integration foundations far more often than on model capability.
6 min read
What 'AI-ready' means in architecture terms
An AI-ready platform is one where relevant data can be located, trusted, accessed under policy, and served to an application with acceptable latency. Each of those is an architectural property, and each is testable before a model is selected.
- Discoverability: catalogue coverage and meaningful business definitions
- Trust: quality checks, lineage and known ownership
- Access: policy-based controls that apply equally to people and services
- Serving: APIs, vector or semantic layers, and predictable latency
- Traceability: logging of prompts, retrieved context and outputs
The semantic layer question
Retrieval-based approaches need more than raw documents. A semantic or knowledge layer — consistent business definitions, entity resolution and curated content sets — usually determines answer quality more than the choice of model.
Design for change
Model choice will change repeatedly. Isolate providers behind an internal interface, keep prompts and retrieval configuration versioned, and avoid embedding provider specifics in business applications.
Working through this in your own environment?
We help organisations make these decisions with the constraints they actually have.