Codexlava builds AI into the systems UK businesses already run — reading documents into records, answering from your own data, sorting enquiries and drafting the routine work — with a person approving wherever it matters.
Built on the Anthropic API and the OpenAI API, in Python.
Not another chat window your team has to copy and paste into. A step in the process, on real records, that you can measure.
A supplier invoice arrives by email
Supplier, date, lines and VAT read from the document
Anything unclear goes to a person, not a guess
Posted to your accounts system, with a record of who approved it
The work that pays back is rarely glamorous: reading, sorting, drafting and looking things up, done the same way every time, with a person where it counts.
Invoices, purchase orders, forms and email attachments read into structured records in your own system. Fields the model is unsure of go to a person rather than being guessed.
An assistant that answers from your documents and records, shows the source, and respects who may see what.
Enquiries, tickets and leads sorted by what they actually say and sent to the right person or queue.
Replies, meeting notes and reports drafted inside the workflow, for a person to approve before they go out.
Multi-step tasks — look up a record, check a rule, update a system — using only the tools you allow, with approval for anything that cannot be undone.
The same four-stage process as every build, with one addition: the AI is tested on your own examples first, so you see real accuracy and running cost before the bigger spend.
Where the hours go, what a good output looks like, and how the work is checked today. One process with a measurable cost beats a broad “AI strategy”.
Whether the input exists, whether it is clean enough to work from, and whether it is allowed to go to a model provider at all.
A batch of your real examples, run through the model, so accuracy and running cost are measured rather than promised. If it does not work, this is the point to stop — before the build budget is spent.
Inside the software you already use, with the human checkpoint, logging and cost limits in place, on a staging URL you can try.
Errors reported in production, a sample of outputs reviewed over time, and prompts or models updated as the providers change them.
Large language models are now the most-used AI technology in UK business, yet only one adopter in ten calls its use extensive. The ONS names finding a use case and a lack of expertise among the commonest reasons firms stall — the two things this service exists to solve.
Source: ONS, Artificial intelligence in UK businesses: 2023 to 2026 (published July 2026, June 2026 data, businesses with 10 or more employees). Market context, not Codexlava results.
Every AI feature we build starts with one question: where does a person check the work? The answer is written into the scope, not added later.
The model is a small part of an AI feature. Most of the work is the software around it: queues, records, permissions, monitoring. See the full stack.
Chosen per task on output quality, speed and running cost — the cheapest model that does the job well, not the largest.
Python is where the AI tooling lives; FastAPI for lean services, Django when the feature sits in a larger system.
A model call is slow next to a database query, so AI steps run as background jobs with retries, and nobody waits on a spinner.
Where the extracted records, the audit trail and the approvals are kept.
The systems the work already lives in, so the AI acts where your team works rather than in a separate app.
Sized for the load, containerised so it runs the same on staging and in production.
Tested against real samples before launch; failed calls and errors reported in production.
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 process, what it costs today in time, what a good output looks like, and whether AI is the right tool for it.
What data goes where, to which provider, and the questions for whoever owns data protection in your business.
Measured accuracy and running cost on your own examples, and a clear build or do-not-build recommendation.
Inside your system with the human checkpoint, logging and cost limits, on a staging URL throughout.
How it works, the prompts and configuration, and how to change the model later.
What AI costs a UK small business in 2026: off-the-shelf to custom tiers, what the AI itself costs per month, and where the money really goes.
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.
A prototype, a proof of concept and an MVP answer three different questions. Which one you need first, and what each costs you.
Bring one process you suspect AI could help with. We will tell you on the first call whether it is a good candidate — including when it is not.
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.