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.
AI in the specific places it earns its cost.
Triage, extraction, drafting and research steps handled by grounded AI inside your existing processes — scoped narrowly, evaluated honestly, and escalated to people by design.
What changes
The problem
The question is rarely whether AI can do a task. It is whether it can do it reliably enough, cheaply enough, and with a sensible answer when it cannot.
Projects fail when AI is dropped across a whole process instead of the two or three steps where judgement is cheap to verify and the cost of being wrong is contained.
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.
We walk the process and identify where output is easy to verify and failure is recoverable. Those steps go first; the rest stay human.
Retrieval over your documents and data, with citations, so answers come from your material rather than the model's general training.
A graded test set from your real cases, with accuracy and failure modes published to you before anyone depends on it.
Confidence thresholds, review queues and clean escalation — so uncertainty becomes a handover rather than a confident error.
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.
System prompt architecture, structured output contracts and graded evaluation sets — so a prompt change is a measured improvement rather than a hopeful edit.
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.
FastAPI services, data-validation layers and automated test suites — Python backends built to be read, extended and trusted by whoever maintains them next.
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”.
You see the numbers. We build an evaluation set from your real cases, grade the system against it, and report accuracy and failure modes before rollout. If it is not good enough for a step, we say so and that step stays manual.
In the work we do, it usually removes the repetitive part of a role rather than the role. We will be straightforward about which tasks are affected during scoping, because planning for it works better than discovering it.
To the model providers you approve, through API tiers that do not train on submitted data by default, and nowhere else. Retrieval content stays in storage you control. We document exactly what is sent where so you can check it against your own policy.
Other solutions
The recurring manual steps between your tools — copying records, chasing approvals, rebuilding the same report — rebuilt as reliable automations with error handling and logs.
02Internal 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.
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.