Custom AI Agents
Support agents, document processing, lead qualification and multi-step workflows — built on retrieval over your own content, with evaluation and escalation designed in.
Stop paying people to move data between tabs.
The recurring manual steps between your tools — copying records, chasing approvals, rebuilding the same report — rebuilt as reliable automations with error handling and logs.
What changes
The problem
Most teams do not have an automation problem. They have twelve small ones: a record that gets re-typed into a second system, a weekly report assembled by hand, an onboarding checklist that lives in someone's memory.
Each is too small to prioritise, and together they consume a day a week. They also fail silently — nobody notices a skipped step until a customer does.
Technologies involved
Chosen per engagement. We will explain any choice you want to question.
Our approach
Four principles that shape every engagement of this kind.
Not the documented version. We sit with whoever does the work, record every step, system and exception, and count how long it takes.
The predictable majority of cases gets automated; genuine exceptions are routed to a person with the context already attached.
Retries, idempotency, alerting and a readable run log — so a failed automation is visible and replayable rather than invisible.
Workflows live in your own n8n instance or repository, documented, so a small change does not need us.
Delivered by
Most engagements of this type combine two or three of them.
Support agents, document processing, lead qualification and multi-step workflows — built on retrieval over your own content, with evaluation and escalation designed in.
FastAPI services, data-validation layers and automated test suites — Python backends built to be read, extended and trusted by whoever maintains them next.
Custom Model Context Protocol servers that expose your data and actions as typed tools — with authentication, scoped permissions and audit logging built in from the start.
Delivery
Whatever the solution, the engagement runs through the same stages — so you always know what should exist by now.
We learn how the business actually works — the process, the constraints, the systems already in place, and what has been tried before. Mostly listening and asking awkward questions.
Scope, sequence and trade-offs get decided and written down: what is in, what is deliberately out, what it costs and roughly how long it takes.
For interfaces, the layouts and states. For systems, the data model, API contracts and integration points. Either way, the structure is agreed before code gets expensive to change.
Built in vertical slices so each milestone is working software you can use, not a percentage. You see progress in the client portal and in demos, not in status reports.
Automated tests for behaviour, manual passes for the things tests miss, plus accessibility, responsive and performance checks. For AI work, a graded evaluation run.
Deployment, environment configuration, redirects where URLs change, monitoring switched on, and a post-launch verification pass before anyone declares it done.
A handover session with your team, documentation they can act on, and an agreed support arrangement — so the system stays maintainable with or without us.
Questions
Including the ones where the honest answer is “you may not need this”.
Often you should not. Zapier is excellent for simple, low-volume connections. We move clients to n8n or custom services when per-task pricing gets expensive at volume, when logic outgrows linear steps, or when data cannot leave your own infrastructure.
It alerts, and the run log shows which step failed with what input. Steps are built to be idempotent where possible, so a replay does not duplicate work. We also agree up front which failures should stop the process and which should continue and flag.
Frequency times time saved, divided by implementation effort — with a bias towards the process whose failure costs the most. The map we produce in discovery makes that ranking obvious, and it is yours whether or not we build anything.
Other solutions
Internal tools, dashboards, APIs and platforms for the parts of your business no off-the-shelf product fits — typed, tested and delivered in your own repository.
02Triage, extraction, drafting and research steps handled by grounded AI inside your existing processes — scoped narrowly, evaluated honestly, and escalated to people by design.
03Organic and paid demand built on tracking that reconciles — technical SEO, content, Meta Ads and landing-page work, reported against cost per qualified lead.
04Support automation grounded in your own help content, with confidence thresholds, source citations and a handover that arrives with context already attached.
Describe the process or the goal. We will come back with what it would take, what it would cost, and what we would do first.