Python Development
FastAPI services, data-validation layers and automated test suites — Python backends built to be read, extended and trusted by whoever maintains them next.
Built for how your business actually works.
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.
What changes
The problem
There is usually one process at the centre of a business that no product on the market quite handles. So it runs on a spreadsheet with twelve tabs, a shared inbox, and one person who understands it.
That works until it does not: the spreadsheet corrupts, the person leaves, or the volume doubles. Custom software is worth building exactly there — and rarely anywhere else.
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.
Entities, states and transitions first. Interfaces that match a wrong model are expensive to unwind later.
One real workflow working completely, early, so the model is validated against reality rather than a document.
Where you already have systems that work, we connect to them through typed clients instead of rebuilding them.
Tests, documentation, migrations and a CI pipeline — so the next change is routine, by us or by anyone.
Delivered by
Most engagements of this type combine two or three of them.
FastAPI services, data-validation layers and automated test suites — Python backends built to be read, extended and trusted by whoever maintains them next.
Custom themes, editor-friendly block patterns, measurable performance work and technical SEO foundations — WordPress sites your marketing team can actually run.
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”.
Usually, yes — and we will tell you when. Building is worth it when the process is a genuine differentiator, when integration cost across several SaaS tools exceeds the build, or when your constraints are unusual enough that configuration never quite fits.
Vertical slices make this manageable: because each slice is working software, re-prioritising the next one costs little. What we protect against is changing the data model late, which is why modelling comes first.
You keep everything: repository, infrastructure, accounts, documentation and tests. There is no runtime dependency on Hirelyx, no proprietary layer, and no licence to renew.
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.
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.