AI automation
Most companies don't have an AI problem. They have a process that was never written down, running on six tools and one person's memory. We map it, then automate the parts that should never have needed a human.
What you get
We find the workflows eating your team's week — intake, triage, quoting, reconciliation, reporting — and rebuild them as automated paths with humans on the calls that matter.
01A workflow map, before any code
One week of interviews and system traces produces a diagram of how work actually flows today — including the spreadsheet nobody mentions in the org chart. You keep it whether or not we build anything.
02Automation on your existing tools
We wire into the CRM, ERP, ticketing, and file stores you already pay for. Replacing your stack is a last resort, not an opening move.
03Human-in-the-loop by design
Every automated path has a defined confidence threshold and a person who owns exceptions. Nothing important gets decided by a model with no one watching.
04Observability from day one
Dashboards for throughput, exception rate, and model cost. When an automation drifts, you find out before your customers do.
05Runbooks and handoff
Written operating procedures, on-call notes, and a training session for the team who inherits it. You should be able to fire us and keep running.
Shapes this usually takes
Most engagements take one of these three shapes. Which one fits depends on how well-defined the problem already is — tell us the situation and we'll say which, and what it would take.
Automation sprint
One workflow, mapped and automated end to end. Best when you already know which process is bleeding.
Contact usAutomation program
Three to five connected workflows, shared infrastructure, and a rollout plan across teams.
Contact usOngoing operations
We keep the automations healthy, tune prompts and thresholds, and add new paths as you find them.
Contact usWhat we reach for
Boring where boring works, current where it matters. We pick for what your team can maintain after we leave — not for what looks good in a case study.
Where we've done this
Common questions
How do you decide what to automate first?
Volume × time × error cost, divided by how well-defined the rules are. The loudest process is rarely the right first target — we look for the one that pays for the next three.
What if our data is a mess?
It usually is. Part of the mapping week is finding out how bad, and whether the fix is a cleanup, a schema, or an automation that tolerates the mess. We'll tell you which before you commit budget.
Do we need a data science team to keep this running?
No. We build for an ops team, not a research team. Thresholds, prompts, and routing rules live in a config surface your staff can edit.
Is ai automation the right move for you?
Send us the situation. We’ll come back with a read on whether this is the right service, a rough range, and what we’d want to learn first.