Data & AI Architecture
Data and AI that pass review.
AI pilots are easy to start and hard to put into production. The hard part is architecture: where the data comes from, who may see it, how answers are checked, and what happens when the model is wrong.
The problem we solve
Most AI initiatives stop at review. Security, data-protection and risk teams ask questions the pilot was never designed to answer: data lineage, access control, evaluation, audit.
What you get
You get a design your reviewers can approve, and a path from the first use case to the next ones without starting again.
- A data platform architecture: ingestion, storage, governance and access
- Retrieval and agent designs (RAG, tools, orchestration) with guardrails
- An evaluation approach: test sets, quality measures and monitoring in production
- Controls for security, privacy and model risk, documented for your reviewers
How an engagement runs
Enterprise AI readiness and implementation
A phased programme, or an accelerated 12-week engagement focused on the first production use case.
Standards we work with
- Retrieval-augmented generation (RAG)
- NIST AI Risk Management Framework
- EU AI Act requirements
- OWASP Top 10 for LLM applications
Questions we are often asked
Which models do you work with?
We are model-neutral. The architecture keeps the model replaceable, because the best choice will change.
Can AI work with our confidential data?
Often, with the right controls: data classification, access enforced at retrieval time, and deployment options that keep data where it must stay.
How do you know the answers are good enough?
We define evaluation before the build: test sets from your domain, quality thresholds, and monitoring once in production.
Related reading
Enterprise RAG Architecture Playbook
Retrieval, grounding, evaluation and guardrails for production.
ArticleRAG Architecture Patterns for the Enterprise
The patterns that survive security and model-risk review.
Point of viewWhy Enterprise AI Needs an Architecture, Not Just a Model
The decisions around the model matter more than the model.
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