Codexlava builds bespoke business software for UK startups, SMEs and healthcare organisations — process automation, internal tools, integrations and cloud applications, in Python and JavaScript.
When off-the-shelf nearly fits but not quite, and the workaround has become the process, that is usually the point to build. If a product does the job as sold, we will tell you to buy it.
Most of the cost in bespoke software is not the new thing, it is everything it has to talk to. Seven kinds of work cover almost every brief we see.
Replace the spreadsheets, re-keyed data and manual handoffs with software that applies your rules the same way every time — and runs the slow work in the background so nobody waits on it.
Internal tools, portals and admin systems built around how your team actually works.
Payment providers, CRMs, accounting platforms and internal tools, made to share one source of truth.
AI inside your own software, not beside it: documents read into records, answers drawn from your own data, enquiries triaged, drafts produced inside a workflow — with a person approving where it matters.
Sized for your actual load, with room to grow, rather than a bill sized for a team of twelve.
Years of accumulated data moved into something with rules, cleaned and reconciled rather than assumed to import.
iOS and Android from one codebase, sharing the back end and the logic of the web application.
Nothing exotic. Every tool below is widely used and well documented, so whoever maintains your system after us will already know it.
Python for back ends, automation, data and AI work; JavaScript and TypeScript for the interfaces people use and for Node.js services, with types where a codebase will grow.
Django when a system needs accounts, an admin and a data model from day one; FastAPI and Flask for lean APIs and services; Express.js on Node; React and Next.js for the web; React Native for iOS and Android.
PostgreSQL and MySQL for relational data with rules; MongoDB where the data is genuinely document-shaped.
Redis for caching and queues; Celery to run slow work — imports, reports, emails — off the request, so pages stay fast.
Chosen for the load and the budget. A VPS is often enough to start; AWS or Azure when the work needs managed services — Azure especially where a business already runs on Microsoft.
The platforms UK SMEs already pay for: sign-in and documents in Microsoft 365, payments in Stripe, accounts in Xero or QuickBooks, customers in HubSpot.
Applications are containerised with Docker, so they run the same way on a laptop, on staging and in production, and pipelines build and deploy them.
pytest for the back end, Playwright to drive the interface the way a user would, and Sentry to report errors in production before a customer has to.
Every line lives in a Git repository with its full history — which is what makes handing it over real.
Work is planned and tracked as tickets against the agreed scope.
AI coding agents for drafting, refactoring and tests — always under an engineer’s review. How that works is set out below.
The ONS finds AI use in UK businesses has nearly tripled since 2023, but the typical firm uses one or two tools that sit beside the work rather than inside it. Going deeper usually needs what custom software provides: data in one place, systems that talk to each other, and a process written down as rules.
Business figures: ONS, Artificial intelligence in UK businesses: 2023 to 2026 (July 2026). Developer figures: JetBrains Research, AI Coding Agents: Adoption Trends (August 2026, 15,000+ developers worldwide). Market context, not Codexlava results.
Most firms that use AI use a chat window next to the work. The value is in putting it inside the work: the system that already holds your data and runs your process.
Invoices, forms, PDFs and emails read into structured records in your own system, with low-confidence results sent to a person rather than guessed.
An assistant that answers from your own documents and records, cites where the answer came from, and respects who is allowed to see what.
Enquiries, tickets and leads sorted by what they actually say, and sent to the right person or queue.
Summaries, draft replies and reports produced as one step of an automated workflow, with human approval wherever the output leaves the building.
Which model API is used — such as Anthropic’s Claude or OpenAI — where your data goes, what is logged, and a running cost you can predict.
We build with Claude Code and Codex, the AI coding agents most developers now use at work. They make the routine work faster. They do not decide what gets built, and they do not ship anything on their own.
Every change has a named engineer who has read it, and every change is on a staging URL you can see before it goes live.
Both tools run on business terms under which your code is not used to train models.
The engineer turns a piece of the approved scope into a ticket with acceptance criteria. AI can help draft it; it does not decide what gets built.
The agent drafts the implementation, the tests and any refactoring in a branch — the routine work that used to take most of the hours.
The engineer who owns the code reviews every change, rewrites what is wrong and rejects what does not belong. Nothing is merged unread.
The change is built in Docker and checked in the pipeline before it moves on.
You see the change running, not a status report, and it goes live only once you accept it.
You always know which stage you are in and what comes out of it.
We map what already exists: the systems you run, the data you hold, who needs access and what the constraints are. We use AI where it speeds the groundwork up, but the scope is read, questioned and agreed with you — never generated and handed over.
You approve the written scope before any design starts.
Wireframes first, then a prototype you can click through and react to. This is the cheapest point at which to change your mind, so it is where we ask you to.
Nothing is built until you sign off the prototype. Changing a wireframe costs minutes; changing built software costs days.
Delivered in increments you can see working, not one hand-off at the end. We use AI where it is genuinely faster, and every line is reviewed by the engineer who owns it. Nothing reaches you that a human has not read.
Working software on a staging URL throughout, not a status report.
Automated and manual testing, then deployment. The documentation ships with the build, so your own team can run it — and ongoing support is there if you would rather we did.
You accept the build before it goes live, and you own what you paid for.
If you are in the second column, we will say so on the first call rather than sell you something you do not need.
You approve each stage before the next one starts, and you own everything you paid for.
The systems you run, the data you hold, who needs access and what the constraints are.
Read, questioned and agreed with you. If off-the-shelf would serve you better, this is where we say so.
So you see how the system works before anything is built.
On a staging URL throughout, reviewed by the engineer who owns the code.
Software requirement specifications, API documentation, and user manuals and guides.
UK web application costs in 2026 by type, from internal tools to SaaS, with developer day rates and the running costs most quotes leave out.
Django or FastAPI for a Python backend: what each includes, where each fits, and why many products end up using both.
When off-the-shelf software is the right answer, when a bespoke build earns its cost, and the hybrid most UK businesses end up with.
Launch is where most of a system’s life begins. Keep us on to run it, or take it in-house with the documentation — either way you own it.
Not sure it needs building in full yet? Start smaller with an MVP — same team, same foundations, scoped to settle the question first.
One call, no obligation. We will tell you what the work involves, roughly what it costs, and whether we are the right people to do it.