AI & Agentic Data Foundations

AI is only as reliable as the data, context and controls behind it

MDA helps organisations assess, design and deliver AI use cases by putting in place the data, context, permissions, quality, lineage, retrieval and evaluation they depend on. Every engagement starts with a concrete business decision or workflow, so the technology and controls are designed for a defined purpose.

Where are you with AI?

Deployed

Our AI is live, but people do not trust it

Find out why answers are unreliable, controls fall short or adoption is low, and whether to repair, redesign, replace, retire or scale.

AI Reliability & Adoption Diagnostic

The foundations

What an AI system needs besides a model

  • Data

    Sources that arrive reliably, with known grain, history and meaning.

  • Context

    The definitions, documents and business rules the system must reason with.

  • Access

    Permissions that match who may see what, carried through to every answer.

  • Retrieval

    Structured queries, search or vector retrieval, chosen for the use case, not by default.

  • Quality and lineage

    Evidence of where an answer came from and whether its inputs can be trusted.

  • Evaluation and oversight

    Agreed tests, monitoring and human controls proportionate to the risk.

How MDA delivers AI work

Four levels, each with a clear boundary. Larger implementations are scoped only when the evidence and the delivery team are in place.

  1. Use-case and data readiness assessment

    One defined decision or workflow; an evidence-led go or no-go.

    Available now

  2. Reliability and adoption diagnostic

    One deployed solution; the root causes of unreliable answers, weak controls or low use.

    Available now

  3. Data, context and control architecture

    The information, retrieval, access, lineage, evaluation and human-control design.

    Available now

  4. Bounded RAG or agentic pilot

    A defined pilot with agreed evaluation criteria, led and built by Gaety, with specialist support where the scope requires it.

    Scoped on request

What MDA will not promise

  • A guaranteed level of model accuracy or adoption.
  • RAG, vector databases or agents as automatic ingredients. They are used where the use case justifies them.
  • A whole enterprise AI platform in one step. Larger programmes start from a proven, bounded use case.

Have an AI use case, or one that is not working?

Start with a focused conversation