Service · Data
Data & AI Infrastructure
The data foundation AI can actually use — curated, permissioned, observable, and aligned to how the business actually operates.
The problem
Why this work exists.
Most AI initiatives stall at the data layer. Either the data is locked in disconnected systems, or it is connected but under-permissioned, or it is permissioned but unreviewed and wrong. Models then either hallucinate, leak, or both.
The fix is not another data lake. It is a curated, permissioned, observable knowledge layer designed for AI from the start.
Why it matters
What is at stake.
AI is only as good as the knowledge it is allowed to read. And it is only as safe as the boundaries on what it is allowed to read. Data infrastructure is therefore both an enablement problem and a security problem at the same time.
Without it, AI quality plateaus and AI risk compounds.
How MXP helps
What we do in this engagement.
- Design the AI knowledge layer — sources, curation, permissions, freshness
- Establish patterns for retrieval, semantic search, and structured access
- Define what AI is allowed to use — and just as importantly, what it is not
- Build observability into prompts, retrievals, and agent data access
- Align data classification, sensitivity, and AI access policies
- Plan the AI data team and the operating cadence around it
Typical deliverables
What you walk away with.
- AI knowledge architecture covering retrieval, structured data, and tools
- Source-of-truth and curation policy for AI-accessible content
- Sensitivity and permissioning model integrated with identity and access
- AI data observability spec — what was retrieved, by whom, for which agent
- Operating model and team design for AI data engineering
- Phased delivery plan for the highest-value, lowest-risk knowledge surfaces
Engagement approach
How it runs.
Engagements typically run 8–14 weeks and pair with data, platform, and security leadership. We deliver an architecture and an actionable plan that puts AI on a defensible knowledge foundation — and keeps it there.
The knowledge layer is built to evolve as AI capability evolves.
Ready to make this real?
Most enterprises start with a focused diagnostic engagement. We'll show you the gaps and the path.