Technology leaders
You need a governed AI architecture with grounding, evaluation, access controls and accountable oversight.
Service
AI Agents & Automation
We build governed AI agents and retrieval-grounded assistants for enquiry handling, qualification, summaries and internal knowledge — with evaluation, oversight and stated limits, so AI earns its place instead of adding risk.
Give AI a bounded job and a measurable standard
AI can help with language-heavy work such as finding information, summarising a conversation or preparing a response. It should not be treated as a reliable substitute for explicit business rules. We begin with a defined task, an authorised knowledge source and a clear account of what the assistant may and may not do.
The service covers retrieval-grounded assistants and agents connected to approved workflows. Discovery determines whether AI is appropriate at all: a conventional search, integration or rules-based workflow may solve the problem more predictably.
Who this service is for
Operations leaders
You need to reduce repetitive knowledge work while keeping exceptions and decisions visible to people.
Customer-facing leaders
You need faster, consistent assistance without allowing an agent to operate beyond approved limits.
Problems this solves
AI hype without a safe plan
Pressure to adopt AI with no governance, evaluation or clear use case.
Slow, repetitive knowledge work
Enquiries and internal questions handled slowly and inconsistently.
Hallucination risk
Ungrounded AI that answers confidently but wrongly.
What is included
AI agent engineering
Governed agents for enquiry handling, qualification and internal assistance, with guardrails and evaluation.
RAG / knowledge assistants
Retrieval-grounded assistants over trusted knowledge, with citations and quality evaluation.
AI governance & evaluation
Oversight, evaluation and guardrails so AI is safe and measurable in operations.
Private / local LLM (where needed)
Private or local deployment where data residency or privacy require it.
Example: an internal assistant with a source to check
A staff member asks a question about an approved procedure. The assistant retrieves relevant material within that user's access boundary and prepares an answer with source references. If the material does not support an answer, it explains the gap or directs the question to a person.
An action such as changing a customer record is a separate permissioned step. The system should validate the intended change and apply the agreed approval rule before execution.
How the engagement works
Assess the task and information
Identify users, source material, confidentiality, acceptable failures and the decisions that require human control.
Establish an evaluation set
Collect representative questions and difficult cases, including missing evidence and conflicting documents. Agree what a useful answer looks like.
Build access and oversight
Connect authorised sources, constrain tools and record reviewable outcomes. Define escalation and correction processes.
Validate before expanding
Evaluate output quality and failure modes on the agreed cases. Document limits, operating costs and the conditions for changing models or sources.
Data boundaries come before model choice
Public, hosted, private and local model options have different implications for deployment, cost and data handling. The choice depends on the sensitivity of the task and the organisation's operating requirements. A local model is not automatically accurate or secure.
Source permissions, retention, logging and tool access need explicit decisions. Human oversight is especially important where an incorrect answer or action could affect an individual. We describe the agreed controls and their limits without presenting a prototype as a validated production system.
How we evaluate success
Grounded answers
Review whether responses are supported by the authorised material and whether references help the user verify them.
Appropriate escalation
Test whether the assistant stops or asks for help when evidence, permission or confidence is insufficient.
Useful assistance
Measure the review effort and task completion for the intended users, including corrections and rejected outputs.
Questions to resolve before you start
Can an agent act without approval?
Only within the permissions and risk boundaries agreed for the task. We distinguish drafting, recommending and executing actions, and retain approval where the consequence requires it.
Will retrieval eliminate incorrect answers?
No. Retrieval improves access to evidence but cannot eliminate incorrect interpretation or missing context. Evaluation, source maintenance and escalation remain necessary.
Can we use confidential internal documents?
That requires an agreed data boundary, source permissions and deployment approach. Start with a description of the material, not an upload of sensitive documents into an enquiry form.
What if AI is not the right solution?
We can recommend a simpler search, workflow or integration. The objective is a supportable operational improvement, not the use of a particular model.
Discuss ai agents & automation
Bring your current workflow, systems and the result you want to achieve so we can define the next step.