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AI Automation & Agents

WhatsApp and website assistants, invoice and document reading, and automated workflows — built around a real task in your business, with a person still in charge of anything that matters.

There is a great deal of noise about AI at the moment, and most of it is not useful to a business with twelve staff. What is useful is narrow and unglamorous: the enquiry that arrives on WhatsApp at eleven at night and gets answered, the stack of supplier invoices that no longer has to be typed into Tally by hand, the weekly report that assembles itself.

We build those. Each one starts from a task somebody in your office is doing repeatedly, and finishes when that task takes less time than it did before. Anything with money or a commitment attached still goes past a person for approval — the automation prepares the work, your staff approve it.

What we build

  • WhatsApp and website assistants

    Answer the questions you get twenty times a week, at any hour, and hand the serious ones to a human with the conversation attached.

  • Invoice and document reading

    Supplier invoices, purchase orders and delivery notes read automatically and posted into Tally or your system, with the figures shown for approval first.

  • Enquiry handling

    Incoming enquiries sorted, summarised and routed, so nothing sits unanswered in a shared inbox.

  • Drafting assistants

    Quotations, follow-up emails and proposals drafted from your own past documents, for your staff to check and send.

  • Internal knowledge assistant

    Ask a question and get the answer out of your own manuals, price lists and past projects, with the source shown.

  • Automated reporting

    Daily or weekly summaries pulled from your data and sent to WhatsApp or email without anyone assembling them.

How an automation project runs

  1. Find the repeated task

    We look for something being done many times a week by hand. If we cannot find one, we say so rather than inventing a use for AI.

  2. Measure it first

    How long it takes today and how often it happens. Without that number there is no way to tell afterwards whether this helped.

  3. Decide what it may not do

    Written down before building: what the automation handles alone, and what always goes to a person.

  4. Build a narrow version

    One task, done well, running on your real data within a few weeks.

  5. Run alongside your staff

    For a period the automation and your team both do the work, and we compare the results.

  6. Hand over with the controls

    A switch to turn it off, a log of everything it did, and training for whoever supervises it.

Common questions

Can it get things wrong?

Yes. Any system built on a language model can produce a confident answer that is incorrect. That is exactly why we put approval steps on anything involving money, commitments or customer promises, and why every automation keeps a log you can check.

Where does our data go?

We tell you before we build which provider processes what, and what is retained. Where data must not leave your premises, we use a model that runs on your own machine, which costs more and is slower but keeps everything in-house.

What does it cost to run?

Most of these carry a monthly usage cost from the AI provider on top of the build. We estimate it at quotation from your actual volumes, so it is not a surprise later.

Tell us what you want to build

Send a few lines about the problem. You get back an approach, a price and a realistic timeline within one working day.