Most businesses end up hiring separately for the chatbot, the CRM integration and the reporting dashboard, then discover nobody is accountable once the three stop talking to each other. We build the whole stack ourselves — workflows, AI agents, integrations and the plain code in between — so one team understands how it fits together. We use AI where it genuinely earns its place, reading and deciding on messy information, and ordinary deterministic automation everywhere else, because it is cheaper and more predictable.
Most businesses end up hiring separately for the chatbot, the CRM integration and the reporting dashboard — three different freelancers, three half-finished systems that don't talk to each other, and nobody who understands the whole picture once something breaks. On top of that, "AI" gets bolted onto every sales pitch going right now, and a lot of what is sold as an AI agent turns out to be a chatbot wrapper with no real engineering behind it. We build the whole stack ourselves — workflows, agents, integrations and the plain code in between — so there is one team who understands how it all fits together.
New enquiry arrives by form, email or WhatsApp → an AI agent reads it, checks your CRM, and either replies or flags it for a human
Nightly data pipeline → pulls figures from several sources, runs them through your scoring or prediction logic, and refreshes a dashboard before anyone is awake
Public or partner websites → monitored on a schedule, with an alert the moment something relevant changes
CRM record updated → triggers whatever needs to happen elsewhere: a Slack message, a WhatsApp confirmation, an invoice, a row in a sheet
Multi-tenant platform → each customer's workflows, permissions and messaging kept separate but running on one shared system underneath
Recurring manual report → replaced with a scheduled job that pulls the numbers and sends itself
Three different sectors — field-service dispatch, predictive racing analytics, property planning data — each running on a system we designed, built and still support end to end. Some of it is classic automation, some of it (Punthub's prediction models) is genuinely AI; we use whichever one actually solves the problem, not whichever sounds better in a pitch.
3
production systems shipped, three different industries
How we count — what this number measures, and what we have not finished checking.
It means software that can look at unstructured information — an email, a message, a scraped web page — decide what it means, and take an action, rather than just following a fixed if-this-then-that rule. In practice that's usually a language model doing the reading and deciding, wired into ordinary code and APIs that do the actual work: updating a CRM, sending a message, writing to a database. It's less mysterious than the marketing suggests, and more useful once you see it running on your own data.
We build with a human in the loop by default — the agent handles the routine cases and hands over anything it isn't confident about, with the full context attached, rather than guessing. For anything touching money, contracts or a customer commitment, we add explicit checks and an approval step rather than letting the AI act unsupervised. You decide where that line sits; we build to it.
Whole-system work is a custom build, from £3,000, fixed and agreed before we start rather than billed hourly. Builds spanning several systems (CRM, WhatsApp, dashboards, data pipelines) are quoted as one number after a scoping call, and multi-agent stacks are from £8,000. The £1,500 Process Audit sizes it first. Credited in full against any custom build (£3,000+) if you proceed within 60 days. If what you need is one workflow rather than a system, that is from £500 and does not need an audit at all.
Mainly that one team builds and understands the whole thing. When your chatbot, your CRM integration and your reporting dashboard are each built by a different freelancer, nobody is accountable when they stop talking to each other. We design them as one system from the start, which is also why it usually costs less than three separate projects.
Both, and usually mostly the latter. AI is genuinely good at reading and understanding messy, unstructured information; deterministic automation is faster, cheaper and more predictable for everything else. We use AI where it earns its place and skip it everywhere it would just add cost and unpredictability for no real benefit.
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