AI agents that take a job off your team.

An AI agent is software that can read, listen, look things up and write, and that uses those abilities to carry out a job a person would otherwise do by hand.

A chatbot answers questions. An agent does the work: it takes a request, finds what it needs in your systems, produces the result and hands it over for review.

These are jobs we have already built agents for.

Writing up reports

A doctor dictates and a structured report is ready to review. We built this for radiology practices.

• How the radiology dictation project works

Looking up codes

An assistant reads a clinical note and proposes the matching ICD codes, so nobody starts from a blank search box.

Finding records by voice

Doctors search patients and review labs and imaging by voice, including hands-free in the operating room.

Passing work between people

An orchestrator hands each request to the right specialised agent and brings back a single answer.

Keeping the books

Fin, the agent in our own product MyFuture Finance, reads bank exports and payslips, keeps the records and hands tax and forecasting questions to specialist agents. Every figure comes from a tested calculation, never from the model.

• Visit myfuture.finance

Most of the examples above come from healthcare, but the jobs are common to most businesses: writing something up, looking something up, finding a record, handing work on.

A job is a good candidate when it is repetitive, follows steps your team could explain, and ends in something a person can check, such as a report, a code, a record or an answer. It is a poor candidate when every case is different or nobody can say what a correct result looks like.

In your own cloud account, not ours. Your data stays inside your environment and under your access rules.

We deploy on AWS, Azure or Google Cloud, using each cloud's own agent service (Amazon Bedrock AgentCore, Microsoft Foundry or Vertex AI). When none of those fits the job, we build a custom setup instead.

With guardrails and evaluations, both following policies agreed with you.

Guardrails are rules the agent cannot talk its way around: which tools it may use, which data it may read, and what it must never send out. Evaluations are tests for behaviour. They run the agent against known cases before every release, so a change that makes it worse is caught before your team sees it.

A person does. In our radiology project the agent prepares the report and the doctor reviews it. In our coding project the assistant proposes codes and the clinician chooses.

The agent takes the typing and the searching. The decision stays with your team.

Yes. Every action is under code. The whole environment is written with AWS CDK or Terraform, so nothing is clicked together by hand. It can be reviewed, versioned and rebuilt from scratch.

The one that fits each step. An agent can use different models at different levels of effort: a small, fast model to sort a request, a stronger one for the step that needs careful reasoning.

That puts quality where it matters and keeps cost down everywhere else.

We monitor agents the FinOps way. You see what each job costs, how much the models were used and how often the result passed review.

Two runs of the same task can cost very different amounts, so a monthly bill alone tells you little. Cost per job, next to quality per job, tells you whether the agent is earning its keep.