AI Consulting, Agents, Generative AI, LLM & RAG
Identify where AI genuinely helps, then build governed assistants, agents, and retrieval systems on top of your real data.
The problem we usually meet
Most teams have a long list of AI ideas and little agreement on which are worth building. Pilots stall because the data is scattered, the outputs cannot be trusted, and nobody owns the guardrails once the demo is over.
Our approach
- 1
Run a discovery workshop to map candidate use cases against business value, data readiness, and risk.
- 2
Prototype the highest-value case first, grounded in your own content through retrieval (RAG) rather than model guesswork.
- 3
Define evaluation, human-in-the-loop checkpoints, and access controls before anything reaches users.
- 4
Harden the prototype into a production workflow with monitoring, cost controls, and documented decisions.
Deliverables
- Prioritised AI use-case assessment with effort and risk notes
- Reference architecture for LLM, RAG, or agent workloads
- Working prototype on your data, with evaluation results
- Guardrail and governance documentation, plus a handover walkthrough
Typical outcomes
- Prioritised use cases your stakeholders agree on
- Governed prototypes that are safe to put in front of users
- Production AI workflows integrated with existing systems
Talk through your ai consulting & agents work
A discovery call is the fastest way to test fit. We review your goals, constraints, and current stack, then outline an approach and the trade-offs behind it.