AI agents that run the work, with people where it matters.
Most AI projects stall somewhere between a convincing demo and something a business can rely on. We build the second kind: agents with clear inputs, measurable outputs, monitoring, and a person deciding wherever a mistake would be expensive. Sometimes that gives a team its time back. Sometimes an agent runs an entire function, from intake to reporting, and people handle only the exceptions.
It works wherever work follows rules. In operations: documents, records, reconciliations and reports. In engineering: test cases written in plain language and run as automated tests on web and mobile, tickets that move themselves, code reviewed on every change, releases deployed without anyone pushing each step.
Who it's for.
- Operations, finance and support teams with a lot of repetitive, rules-based work
- Leaders who want an entire function to run on AI, with people handling exceptions rather than every case
- Engineering and product teams who want QA, ticket flow, code review and deployments to run without hand-holding
- Companies that outsource work and still have to check it line by line
- Teams with valuable data locked in documents, inboxes and spreadsheets
What you get.
Opportunity review
We map your workflows and pick the ones where automation pays back. Where it won't, we say so.
Document intake
Agents that read PDFs, scans and photos in several languages, extract the fields you need, validate them, and file and enter them into your systems.
Review and reconciliation
Automated checks against your own rules and history: matching payments to invoices, or reviewing every booking before you approve it.
Whole functions, run by agents
Intake, checks, booking, reconciliation and reporting as one unattended cycle, with a person on the exceptions. Accounts payable, for example, can run this way in near-real time with nobody touching it.
Codeless test automation
Test cases written in plain language and turned into automated tests for web, mobile and any environment you run, kept in step with the product and re-run on every release.
Engineering workflow automation
Jira workflows, code review, build and deployment steps run by agents: tickets move, every change is reviewed, releases ship on schedule, and a person is asked only when something needs a decision.
Confidence scoring, not alerts
Every finding shows what it is based on and how far to trust it. Weak signals become notes, so the queue stays short and people keep reading it.
Guardrails and audit trail
Review queues for anything ambiguous, a log of every action, evaluation of the model's output over time, and privacy rules that keep sensitive data out of shared storage.
We work with
- LLM APIs
- Python
- TypeScript
- Test automation for web and mobile
- Jira
- GitHub and GitLab
- CI/CD
- ERP and accounting systems
- Google Workspace
- Slack
How an engagement runs.
Map
We look at how the work is done today and where the time actually goes.
Pilot
One workflow, automated end to end, and measured against how it's done now.
Harden
Evaluation, guardrails, monitoring, and the human checkpoints the process needs.
Run
We operate and improve it with you, extend it to the next workflow, or hand it to your team with documentation.
Related work.
AI automation, finance operations
Autonomous accounts payable
An AI agent that turns a shared folder of invoices and bank statements into a maintained payables ledger, a live dashboard and a payment tracker, with no manual data entry.
Read the case studyAI automation, finance operations
Bookkeeping review automation
An agent that reviews every ledger posting against years of audited bookings, the invoice register and the source documents, and returns its feedback as a short, evidence-scored queue.
Read the case study
Common questions.
Something else on your mind? Email us.
How is this different from off-the-shelf AI tools?
A tool automates a step. We automate the process around it: the intake, the checks, the exceptions, the reporting and the connection to your systems, with the evidence for every decision kept. That is the difference between a demo and something a business relies on.
How do you keep AI output reliable?
Every finding is scored on the evidence behind it, not on how confident the model sounds, and anything ambiguous goes to a person. Systems ship with logging, evaluation and guardrails. If a workflow can't be made reliable enough, we'll tell you before you've paid for it.
Can you automate our QA testing?
Yes, without anyone writing test code. You describe what to check in plain language; the agent turns it into automated tests for web, mobile or whatever environment you run, keeps them in step with the product, and runs them on every release. A failure comes back with the evidence, not just a red cross.
Which engineering workflows can you automate?
Ticket triage and movement in Jira, code review on every pull request, release notes, build and deployment steps, and the checks in between. The rule is the same as in operations: the agent does the routine work and a person makes the calls that matter.
How long does it take to build?
A first working version of a well-defined workflow usually takes days or a few weeks, not months. What takes longer is agreeing the rules: how you want things done, and which exceptions matter.
Can it work with the tools we already have?
Yes. We integrate with what you already run: your ERP or accounting system, shared drives, spreadsheets, Jira, Slack and internal systems. The aim is to remove work, not to add another tool.
Other ways we can help.
AI products & systems
Web, mobile and API products and internal platforms designed around AI from the first version, grown into the complete system, and run in production.
Data & decision intelligence
Large volumes of operational and financial data turned into dashboards, analysis and forecasts, with AI writing the read-out, so decisions get made on evidence, in minutes rather than weeks.
Executive & board advisory
An independent sounding board for C-level leaders and board members, and a mentor for the teams below them: AI strategy, pricing and growth, product and mobile UX, data, and organization and change across regions.
Tell us what you're working on.
A 30-minute conversation about your business, and a clear view of what we would do first. No obligation.