AI solutions designed around real work.
We examine the workflow, define observable quality, and connect agents to the data, tools and people needed to complete the task safely.
Start with the bottleneck, not the model.
A useful AI system addresses a specific operational constraint. These paths show where we commonly begin and how the scope differs.
AI automation for SMEs
Assess and prioritise repeatable work with explicit success and stop conditions.
02Custom AI agents
Coordinate knowledge, rules and limited tool use with dependable human escalation.
03Back office & documents
Classify inputs, extract information, prepare cases and route exceptions.
04Customer service
Understand requests, use approved knowledge and hand over complex cases with context.
05Sales & leads
Structure enquiries, consolidate research and prepare informed next steps.
06AI product development
Turn a user need into an integrated, testable AI capability and operating model.
The workflow determines the architecture.
An agent is not an isolated chat box. It needs approved knowledge, reliable data, constrained actions and a defined route for uncertainty. We document inputs, decisions, handovers and exceptions before selecting the technical pattern.
Quality is specified before development.
We agree what correct handling means: which sources are valid, when a person must take over, and which actions are prohibited. A representative evaluation set turns those rules into repeatable checks.
Adoption is part of engineering.
A solution only helps when ownership, monitoring, feedback and incident routes are clear. We design these operating elements with the system, including logging, approvals and controlled change.
Our AI consulting and implementation work connects workflow selection, technical feasibility and a practical delivery plan.
Which workflow should come first?
Bring one concrete process. We will look at volume, variation, systems and risk, then give you a grounded view of whether AI is a sensible next step.