AI Readiness & Use-Case Mapping
Before integrating Large Language Models or attempting RAG implementations, organisations must evaluate their data quality, access permissions, and hallucination risks.
Critical Notice on AI Outputs
AI-generated outputs require human review and should not be treated as legal, security or compliance advice. Implementation requires proper data quality checks and should be evaluated case by case.
Defining Business Problems First
AI is a tool, not a strategy. We help UK businesses prioritise use cases by identifying specific, measurable operational problems. From retrieving internal policy documents faster to drafting standard service replies, we focus on pragmatic applications rather than revolutionary claims.
Assessing Data Quality
If your internal knowledge base is outdated, any AI applied to it will produce incorrect answers. We evaluate the cleanliness, accuracy, and format of the data you intend to feed into any pilot program.
Privacy & Sensitive Information
Connecting internal data to an AI model poses security risks. We map existing access permissions and identify sensitive information that must be excluded from indexing to comply with your internal governance.
Hallucination Risks
Generative AI will occasionally present false information confidently. We advise on establishing strict boundaries for the AI's knowledge retrieval and designing workflows where humans remain in the loop.
Pilot Planning
We advocate for starting small. By designing a tightly scoped pilot project, you can test a specific use case—such as internal documentation querying—measure the results, and refine the process safely.
LLM and RAG Planning
For organisations interested in Retrieval-Augmented Generation (RAG)—a method of securely querying your own private documents—we map the necessary infrastructure requirements. This includes document standardisation, chunking strategies, and evaluating potential vendor platforms without committing to specific software upfront.