We build AI into the work your business already does: agents that take over the checks your team repeats, and AI for the hard steps, such as reading a document or writing the daily summary. Each finding comes with its evidence. Your people decide what happens next.
It gathers the records, checks them against your rules, writes up each difference with its evidence and sends it to the person responsible. When nobody acts, it follows up.
What it fixes: Hours of repeated checking, and problems that surface weeks after they happened.
Writing the daily finance summary. Reading contracts and photos without sending them to an outside service. Answering a manager's question from read-only data. Comparing a drawing with the office standard.
What it fixes: Retyping, reports nobody has time to write, and questions that wait days for an answer.
A check your team repeats, a clear rule, and the person who owns the result.
Plain rules clear the routine cases. AI looks only at what the rules cannot settle.
We build and test on synthetic data and copies of your records, not on your live system.
Before it runs, the agent is scored on cases with known answers and replayed on past days against what your people decided. If it falls short, it stays off.
A model from another provider reviews the work, and a separate pass must confirm each finding before it counts.
First silently, then beside your team, then site by site with your approval.
Piloted: an AI reviewer checked submitted inspection reports and photos against management's rules, logged each change under its own label and notified the inspector.
Ask for a report in plain words. The assistant writes and runs read-only queries, corrects its own errors and shows each query it ran.
Drafts the weekday briefing, tracks open commitments and risks, and prepares drafts that wait for the owner's approval.
One AI model drafts a software change, a model from another provider reviews it, and a person approves before anything goes live.
A daily AI-written finance summary, a question box and an on-request dashboard analyst, read-only by design. Ran inside a live platform from June to August 2026; now paused.
Runs on the office computer, flags money and stock issues for staff to confirm and answers management's questions. Personal data is filtered out.
Supplier contracts and shelf photos are read by text recognition on the device, so they are not uploaded to an outside service.
Checks sheets against office standards and compares revisions. Its AI step stays off until it is measured as reliable; judgement and sign-off stay with the engineer.
Numbers an assistant reports come from database queries you can inspect, not from the model's memory.
AI gets read-only, time-limited access to the data its task needs, and each query it runs is logged.
Work an AI prepares is recorded under its own label, so no person is credited with a check they did not make.
AI prepares and proposes. People approve, and a switch turns the AI off while daily work carries on.
Pay and personal details are removed before AI sees the data. Sensitive documents are read on your own computers.
Emails and documents are handled as data, not instructions. Text that tries to steer the AI is flagged for a person.
Data protection and the EU AI Act are covered in Law & data.
Software that completes a task in several steps: it gathers what it needs, checks it, writes up what it found and passes it to the right person. A chatbot answers questions. An agent finishes a job.
Applied AI does one step inside a process, such as reading an invoice or writing a summary. Agentic AI runs the whole recurring job around that step and hands the result to a person.
With one recurring job that costs your team hours each week and has a clear owner. We map it, build it on copies of your data, measure it on past days and switch it on step by step with your approval.
No. We work on top of the systems you already use and read the files they already produce.
Our agents prepare findings and proposals, and your people decide. If you choose to delegate a routine judgement, that is your decision, and each change the AI makes is logged under its own label.
Leading commercial models, chosen for each task, and local models where the data must stay on your premises. Where a fixed rule does the job, we use the rule: it costs less and is easier to audit.
Describe it in one sentence. A partner replies.
