We help mid-sized organisations answer that honestly — working out what is feasible with the data and systems you already have, and saying so plainly when the answer is “not yet”. Then we build the parts worth building.
What we do
Most AI projects fail before a line of code is written, on a use case that was never going to work. We start by checking.
An honest assessment before you commit budget.
We look at the data, systems and people you actually have, and map where AI would genuinely help — and where it would not. You get a short list of viable use cases, what each would take, and a recommendation you can act on, including “not yet”.
Working software, run in production.
We build the ones worth building and keep them running: language-model pipelines, agents and the unglamorous integration work around them. Evaluated before launch, monitored after, with a human in the loop wherever a wrong answer would matter.
Something we built
Sports registrations open months ahead, on the organiser's own page, and close in hours. EventAlert watches for you: it finds the official registration window for each event and tells you the moment it opens.
It is a small product, but a real one — running in production, with the same evaluation, review and monitoring we would put around anything we build for you.
Open EventAlertResearched, not scraped
A language model reads the organiser’s own registration page and quotes the sentence the dates came from.
A human approves every change
Nothing reaches a subscriber automatically. Proposed dates wait for review, with the source link attached.
Runs on its own
Monitors the catalogue on a schedule, alerts the moment a window opens, and backs itself up nightly.
Contact
Tell us what your organisation does and where the work is slow, manual or error-prone. We will tell you honestly whether AI would help there, and what it would take. We read every message and reply personally, usually within a day.