AI where it earns its keep.
We build AI into the tools and workflows your business already runs: it takes over the repetitive work, surfaces what matters and leaves the final call with your team.
Plain automations, with a person in the loop
Most of what we ship reads like this: a watcher, a few model steps, and a human review before anything leaves the building. It runs next to your stack and writes into the tools you already use.
Tell us your use case// runs next to your stack, not instead of it
flow "support-triage" {
watch inbox("support@")
classify labels: ["bug", "billing", "question"]
draft reply(context: docs + past_tickets)
review by: human // every time
handoff to: your_helpdesk
}Concrete uses, not a demo
We start from a real task or a real pile of data, then build the model into the system that owns it. These are the places we reach for it first.
Document and email processing
Invoices, contracts, tickets and forms read by a model instead of a person. Data gets extracted, classified and routed into the systems that need it, at whatever volume shows up.
Search over your own knowledge
Answers pulled from your documents, wikis and past work, with the source shown next to every answer.
Summaries and reports
Long threads, call notes and raw data condensed into short, readable reports on a schedule.
Assistants inside your product
Chat and copilots built into the product you already run, scoped to what your users actually need.
Queue triage and tagging
Incoming requests sorted, tagged and prioritized before anyone opens the queue.
Where AI helps, where it does not
We have shipped enough AI to know its limits. If your problem is better solved with a plain script or a database query, we will say so on the first call and build that instead.
Where it helps
- Repetitive language work at volume: sorting, drafting, extracting, tagging
- Data you already collect but nobody has time to read
- First drafts and suggestions that a person reviews before they ship
- Queues that need triage before a human opens them
Where it does not
- Final decisions with real consequences. A person keeps the last word.
- Problems with little or messy data. A model cannot fix missing input.
- Jobs a plain script or database query already does cheaper and more predictably.
- Anything that must be right every single time with no review step.
Wondering where AI fits in?
Describe the task or the data, rough is fine. You get an honest read on whether AI helps, and a plan for what building it would take.
Start a project