AI Readiness & Use-Case Mapping

A Cautious Approach to Generative Models

Generative AI and Large Language Models (LLMs) can be useful for internal document search and pattern detection. However, they are prone to hallucinations, require expensive infrastructure, and introduce security concerns if misconfigured. We help UK organizations map use cases with realistic technical expectations.

Core Readiness Pillars

1. Business Problem Definition

We evaluate if your problem actually requires generative AI, or if a traditional relational database query is faster, more accurate, and more cost-effective.

2. Data Quality & Normalization

Retrieval systems (like RAG) rely entirely on source-document accuracy. We assess folder structures, PDF text quality, and duplicate records beforehand.

3. Access Permissions & Privacy

Ensure customer-facing interfaces never have direct access to restricted backend systems. Aligned with UK GDPR principles, internal databases must be strictly partitioned.

4. Hallucination Control & Human Review

AI outputs must not go directly to clients without human-in-the-loop validation. We design interfaces that force reviewer approval steps.

Pilot Planning & Prioritisation

We work with operations managers to select one low-risk, high-context pilot program (e.g., searching internal training manuals) before attempting larger automations. This test phase helps determine actual license costs, error rates, and team adoption hurdles.