What we do
AI automation, from design to deployment.
Every engagement follows the same arc: find the work worth automating, design the automation, deploy it inside your systems, and hand it to your team. The categories below are where that work usually lives.
Intake
Work arrives understood
Email, documents, forms, and tickets are read the moment they land. The automation extracts what matters, files it where it belongs, and routes it to the right queue, so nobody starts their morning sorting.
- Inbound email triaged and answered or escalated
- Documents parsed into structured records (intelligent document processing, without the enterprise suite)
- Requests routed by content, not by whoever noticed first
Triage
The next step gets decided
Requests are classified and prioritized against your rules. Where a rule exists, the rule runs. Where judgment used to be the bottleneck, a model renders it, and the cases that deserve a person reach one.
- Leads scored and assigned as they arrive
- Exceptions separated from the routine flow
- Approvals requested only where the stakes warrant one
Drafting
First drafts stop costing hours
Responses, summaries, quotes, and reports are drafted from your data in your voice, for a person to approve rather than start from blank. The person stays the author; the model does the typing.
- Customer replies drafted with the account history in hand
- Meeting and thread summaries on schedule
- Proposals and quotes assembled from your catalog and terms
Reporting
What happened, without asking
Status pulled from the systems where the work happened and delivered where you read it. No status meetings to collect what the software already knows.
- Daily and weekly operational digests
- Exception reports when something needs a decision
- Numbers reconciled across systems before you see them
Questions
Asked before most engagements
- What is AI automation?
- Software that carries a piece of work from trigger to done, with a model handling the steps that used to need a person's judgment: reading, classifying, deciding, drafting. Traditional automation moves data between systems; AI automation also handles the parts that kept a human in the loop.
- How is this different from Zapier or RPA?
- Integration tools and RPA move data on fixed rules, and they are the right tool when the work is fully rule-shaped. They stop at steps that need reading or judgment. AI automation covers those steps with a model, so the whole piece of work runs instead of the mechanical half.
- Which AI models do you build on?
- Whichever fits the job. We build across commercial APIs and open-source models running on your own hardware, and we choose per task on cost, latency, privacy, and quality. Every automation is built so the model can be swapped without rebuilding the system.
- Can this run on our own hardware?
- Yes. When privacy, cost, or data residency calls for it, we deploy open-source models on hardware you control, and the automation runs entirely inside your walls.
- Does a person stay in control?
- Yes. Routine steps run and report what they did. Consequential steps wait for someone's approval before they happen. You decide which is which.
Tell us about the work you want carried.
A short email is enough to start.