The busywork disappears.
Copying data between tools, sorting inboxes, and chasing status updates take time away from your team. We map those workflows and build automations with version control, monitoring, and error handling. When a step fails, an alert and a clear log help you find and resolve the problem.
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
Classify incoming email and support tickets, route them to the right person, and draft replies using your policies as reference.
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.
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.