AI that does a named job, with a person in charge

We engineer AI agents and automations that take the repetitive work off your team: reading, sorting, drafting, chasing and answering. Each one has a named job, limited access, and a person approving anything that matters.

Jobs an AI agent can take on

Examples of jobs agents do well. Yours will be specific to your business.

01

Answer the phone

Answer calls at any hour, book appointments and send the messages on. We engineered AnswerBug, which does this every day.

02

Sort the inbox

Read each enquiry, sort it, and draft a reply from your help pages for a person to send.

03

Read documents

Pull the dates, amounts and names out of contracts, invoices and forms, and flag what is missing.

04

Chase what is overdue

Spot unpaid invoices or fees and draft the reminder, ready for a person to approve.

05

Keep records up to date

Update the CRM after every call or email, so nobody types it twice.

06

Prepare the first draft

Quotes, reports, release notes and incident updates, drafted from your data for a person to check.

How we engineer agentic workflows

  1. 1

    Map the job

    Every step, every tool, and the points where a person must decide. We write down what "done well" looks like before we build anything.

  2. 2

    Give the agent tools, not keys to everything

    Agents reach your systems through their APIs or MCP servers, with only the access each step needs.

  3. 3

    Put a person where it matters

    Anything that spends money, leaves the building or cannot be undone waits for a person to approve it.

  4. 4

    Test on your real examples

    Before launch, the agent runs against real cases from your business, and we measure how often it gets them right.

  5. 5

    Run it, watch it, widen it

    Every action is logged. We watch quality and cost, and give the agent more to do only as it earns it.

spec

What our agents never do alone

  • Send money or change bank details
  • Make a price, a promise or an apology on your behalf
  • Delete data without a person approving
  • Pretend to be a person
  • Use your data to train someone else's model without a written agreement

These limits are written into every agent we engineer, and they are why our products ask a person before they act.

Questions people ask

What is the difference between an agent and an automation?

An automation follows fixed steps: when this happens, do that. An agent decides which step comes next, within limits we set. If fixed steps will do the job, we engineer the automation, because it is cheaper and easier to trust.

Which AI models do you use?

The one that fits the job, its data rules and its cost. We use models from the major providers, and keep your choice of provider open.

Where does our data go?

Only where the job needs it, under a written agreement that covers it. We tell you which providers see which data before anything is built.

Can AI work with the systems we already use?

Usually, yes. We connect to the tools you run today through their APIs or exports, give the AI one clear job, and keep a person approving anything that matters. Starting again from scratch is rarely needed.

How do we start?

Tell us about the job in Get started. If it is not clear yet which job to automate first, a short discovery finds the one worth doing.

Tell us about the job

Know what you need

Describe the work you want off your team's plate, and we reply with questions or a written quote.

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