Structuring for Safe AI

Generative AI and internal knowledge tools (such as LLMs and RAG systems) can support operational efficiency, but they require a robust, clean data environment. Attempting to deploy these tools on scattered, low-quality data carries significant operational risk.

Disclaimer

AI outputs require constant human review. While we assist in technical planning, our work does not replace formal legal, security, or compliance review. We do not provide certification for UK GDPR or the Data Protection Act 2018.

Our Assessment Criteria

  • Business Problem Definition: Clearly defining what the AI should solve rather than adopting tech for its own sake.
  • Data Quality & Privacy: Ensuring sensitive information is flagged and the underlying training data is accurate.
  • Access Permissions: Verifying that an internal LLM cannot retrieve documents the user does not have clearance to view.
  • Hallucination & Quality Control: Establishing protocols for human review to catch and correct inevitable AI errors.

Use-Case Prioritisation & Pilot Planning

We work with operations managers to map potential workflows where AI might save time. This involves grading each use case on technical feasibility and business value. Once a high-value, low-risk use case is identified, we design a strict pilot plan to test its efficacy in a controlled environment before a wider rollout.