[ service 01 — automate ]
The busywork disappears.
Most teams lose hours every week to work a machine should be doing — copying data between tools, triaging inboxes, chasing status updates. We build automations as real software: version-controlled, monitored, and able to fail loudly instead of silently. If a step breaks at 3 a.m., you find out from an alert, not from an angry customer.
[ what we build ]
Workflow automation
Multi-step business processes — approvals, onboarding, reporting — turned into pipelines that run themselves and log every action.
Document intelligence
Invoices, contracts, and forms parsed into structured data with LLMs, with confidence scores and a human-review lane for the edge cases.
Inbox & ticket triage
Incoming email and support tickets classified, routed, and drafted for reply — grounded in your policies, never free-styling.
Data pipelines & sync
CRMs, spreadsheets, and internal tools kept in sync through scheduled jobs with retries, dead-letter queues, and dashboards.
Monitoring from day one
Every automation ships with health checks, alerting, and a runbook — so it keeps earning its keep after we hand it over.
[ typical stack ]
[ timeline ]
Typical engagement: 2–5 weeks from scoping to a monitored production deploy.
[ straight answers ]
How is this different from Zapier or Make?
No-code chains are great until they break silently or hit a case the template never imagined. We write real code with tests, retries, and alerting — and you own the repository, so you're never locked into a per-task pricing model.
What if our process is messy and undocumented?
That's the normal starting point. Week one is spent mapping how the work actually flows — including the exceptions people handle by instinct — before anything is automated.
Do we need AI for this at all?
Sometimes no — some problems need a cron job, not a model. Part of the engagement is telling you honestly where AI belongs and where plain software is cheaper and more reliable.
Have a ai automation problem in mind? Let's scope it.