AI agents that do real work — with receipts.
Most 'AI automation' is a chatbot bolted onto a website. We build agents that do actual operational work: processing documents, monitoring systems, reconciling data, drafting reports — and logging evidence for every action they take.
Humans stay in control. Agents propose, people approve, and every decision has a receipt. That's how you get automation your team trusts instead of a black box nobody dares to rely on.
The problems we get hired to solve
Repetitive knowledge work
Hours a week spent copying data between systems, triaging inboxes, chasing status updates, and assembling the same reports.
SaaS sprawl
Five subscriptions doing one job badly, with your data fragmented across all of them and nobody sure which tool is the source of truth.
AI pilots that stall
A ChatGPT experiment impressed everyone in the demo and then never made it into the actual workflow, because nobody trusted it unsupervised.
What we build
- Document ingestion agents (invoices, BOLs, contracts, forms)
- Monitoring agents that watch systems and flag exceptions with evidence
- Report and analysis agents grounded in your live data
- Workflow automations with human approval gates
- Internal tools that replace 5+ point-solution subscriptions
- Claude and LLM integrations inside the software you already run
Every build follows the same four steps — Discover, Design, Build, Iterate — see how we work, or see it in production.
Common questions
What can AI agents reliably do today?
Read and extract from documents, cross-check data between systems, monitor for exceptions, draft communications and reports, and execute multi-step workflows — reliably, when they're built with verification steps and human approval gates. We're honest about the failure modes because we run agents in production ourselves.
What does 'agents with receipts' mean?
Every action an agent takes is logged with the evidence behind it — what it saw, what it decided, and why. When an agent flags an exception or files a report, you can trace exactly where that conclusion came from. No black boxes.
Will an agent take actions without approval?
Only where you've explicitly decided it should. We design approval gates into every workflow: agents handle the repetitive 95%, and anything consequential is queued for a human decision with the context attached.
Do we need our systems to be 'AI-ready' first?
No. Most clients start with messy, spreadsheet-heavy operations — that's normal. Part of the build is putting clean data plumbing in place, and the agent work often funds itself by fixing that plumbing along the way.