03AI Solutions, RAG & Agents
Develop assistants and practical automation connected to the documents, data, and systems your organisation uses. Define a useful task, give the solution appropriate information and boundaries, and evaluate how well it supports the people doing the work.
When this helps
The work & its outputs
Develop retrieval-augmented generation (RAG), document-based assistants, AI chatbots, or enterprise search experiences for an agreed information collection. Examine source quality, structure, access, freshness, and retrieval needs. Make source references useful to the person reviewing an answer and define behaviour when relevant evidence is missing.
Support natural-language questions over suitable governed data sources. Connect the interface to agreed definitions, calculation boundaries, and access rules. Use conventional analytics, data engineering, SQL, or reporting where they support the task, and identify questions that require clarification before a result is interpreted.
Define tools, permissions, allowed actions, review points, and escalation before introducing an agent into a workflow. A solution may retrieve information, investigate a problem, recommend an action, or perform a specified action. Match each increase in autonomy to demonstrated capability and your organisation's chosen responsibilities.
Connect the solution to suitable existing applications, APIs, or collaboration tools within the agreed environment. Map how information enters and leaves the workflow, who can access it, and what happens when a dependency fails. Make integration and operational constraints visible during discovery.
Create an agreed set of user questions and tasks, including missing, outdated, ambiguous, and conflicting evidence. Examine retrieval relevance, support for answers, appropriate uncertainty, and task completion. Use these cases to compare changes and identify remaining limits, rather than relying on a handful of convincing demonstrations.
Plan deployment for the selected environment, introduce users to expected behaviour, and establish a way to report problems. Clarify monitoring, source updates, evaluation upkeep, and support responsibilities. Deliver the documentation and practical handover needed to maintain the agreed solution after the initial build.
A business sponsor defines the problem and acceptable boundaries. Your technical counterpart helps with environment constraints, information access, and integration decisions. Subject-matter experts contribute representative questions, expected evidence, difficult cases, and feedback. Their allocated time is essential to evaluating whether the solution is useful in the actual workflow.
A sensible first engagement
Choose a defined corpus, an existing assistant, or one workflow. Review representative user tasks, known problems, available information, and operating constraints. Agree how quality will be evaluated and identify a practical build or improvement increment.
If two procedures conflict, a knowledge assistant should expose the relevant sources and uncertainty instead of presenting one unsupported instruction as settled. Evaluation cases can check that behaviour and the agreed route to an expert.
A few useful answers
Yes. An assessment can examine information sources, retrieval, instructions, evaluation cases, and the user workflow within an agreed scope. Its findings inform specific changes and how to evaluate them.
Not necessarily. The business questions and missing context determine what is needed. Some solutions benefit from simpler definitions and mappings; others need more explicit relationships and rules.
Start by assessing the existing environment, access requirements, and constraints. The implementation approach follows from those findings and the task, rather than an assumed platform change.
A useful next step
Tell us what you are trying to improve, where your information lives, and what is getting in the way.