Service

AI Automation

Automate the repetitive decisions, not just the repetitive tasks.

The problem

Rules-based automation breaks on the messy, judgment-heavy steps — so those stay manual and slow.

What we do

We build automation with AI decision points where rules fall short, keeping a human-in-the-loop fallback for the edge cases.

How it works

Map the workflow and its manual decision points
Automate the deterministic steps first
Add AI reasoning where rules can't cope
Route exceptions to a human with full context

Stack

Tech we use

PythonNode.jsExpressZapier / n8nMongoDB

Deliverables

What you get

A mapped workflow with the manual decision points automated
Integrations across the tools your team already uses
Human-in-the-loop fallback for edge cases
Monitoring so you can see what ran, when, and why

2–6 weeksTypical timeline — per automation, depending on integration complexity.

FAQs

Good to know

What should we automate first?

We start with the highest-volume, most repetitive decision or task — the one costing your team the most hours — rather than a vague enterprise-wide initiative.

What if the automation gets something wrong?

Every workflow ships with a human-in-the-loop fallback and logging, so exceptions are routed to a person instead of failing silently.

Keep exploring

Explore related

Automate your busywork

Send us a process that eats your team's time and we'll map the automation.

Schedule a Call