Nine kinds of work that tend to overlap. Most clients need two or three of them, usually in this order, and almost never all of them at once.
What we do
Each of these can be a standalone piece of work or one stage of a larger one. If you are not sure which applies, start with process analysis.
Identify where AI can create measurable value, then integrate it into the workflows you already have. In practice that means things like AI assistants, internal copilots, document-processing systems, AI-enabled research, internal knowledge assistants and AI-enhanced customer workflows.
Understand how the business operates before recommending any technology: process mapping, bottleneck identification, repetitive-task analysis, information-flow and manual-data-transfer analysis, and a feasibility read on each automation opportunity.
Design and integrate reliable automation across existing systems: API integrations, webhooks, scheduled and event-driven workflows, data synchronisation, notification and reporting workflows, document processing. Platform choice follows the requirement — n8n, Make, Zapier, MCP integrations, or plain custom code.
Controlled agent architectures for processes that genuinely warrant them: research agents, internal assistants, knowledge retrieval, reporting, document analysis, workflow coordination. Always with permissions, human approval on consequential steps, tool boundaries, auditability, error handling and cost control.
Lightweight or full custom software where off-the-shelf automation is not enough: dashboards, internal web applications, data-processing tools, administrative interfaces, scripts, API services and AI-enabled applications. Always after a build-versus-buy check.
Turning fragmented information into something usable: data collection and consolidation, structured extraction, report generation, research pipelines, information normalisation, AI-assisted analysis, and searchable internal knowledge systems.
A written comparison of the models, tools, APIs, vendors and platforms available for your specific use case, judged on capability, reliability, integration requirements, pricing, scalability, security, vendor dependency and operational burden.
Problem, analysis, prototype, real-world validation, improvement, production — in that order. A small working prototype costs far less than discovering the same thing after a full implementation.
Where it is practical, AI and automation go into the systems you already use — email, CRM, spreadsheets, databases, websites, cloud applications, communication systems, internal business software — rather than forcing a platform replacement. The everyday IT and tooling work that surrounds those projects is part of the job.
After go-live
An AI implementation often needs refinement after deployment: workflow tuning, prompt and system refinement, cost optimisation, performance monitoring, failure analysis, and spotting the next use case. That work is offered where it is useful.
It is not assumed. Not every implementation needs an ongoing contract, and we will not pretend otherwise to keep a retainer alive.
How engagements usually run
The common entry point is the two-week AI & operations diagnostic: a fixed-scope, fixed-fee look at one part of your business, ending in a prioritised list of opportunities and an honest recommendation — which sometimes is “leave this alone”.
From there, work is sized to the problem. Delivery stages are dropped when they are not needed, and nothing goes to production without your approval.
That is a normal place to start. Describe the process that frustrates you most and we will work out together whether there is something here worth doing.
