Intelligent Process Automation
Automate repetitive back-office work with AI agents and RPA, end to end.
How we approach intelligent process automation
Most back-office work that eats hours is repetitive, rule-bound, and spread across systems that don't talk to each other: rekeying invoices, chasing approvals, moving data between tools, reconciling records by hand. We automate that work end to end, combining classic RPA for the deterministic steps with AI agents for the parts that need judgment, reading a document, or handling the exceptions a rigid script can't.
We start by mapping where time actually goes and which steps are worth automating, rather than automating a broken process faster. Then we build automations you can trust and maintain: instrumented so you can see what they're doing, with humans in the loop where the stakes demand it, and a clear fallback when something falls outside the rules. The goal is durable hours given back, not a brittle macro that breaks the first time a form changes.
In every engagement
Scope flexes to the problem, but these are the things you can count on us bringing.
- Process mapping and automation opportunity assessment
- RPA plus AI agents for rule-based and judgment steps
- System-to-system integration across the tools you run
- Human-in-the-loop checkpoints and exception handling
Questions buyers ask about intelligent process automation
What's the difference between RPA and AI process automation?
RPA handles steps that follow fixed rules: move data from field A to field B, click button C when condition D is true. It breaks when the process changes or the data is unexpected. AI agents handle the parts that require judgment: reading an unstructured document, deciding what category an exception falls into, dealing with a form that arrived in a format the RPA wasn't trained on. We combine both because most back-office processes have rule-based steps and judgment steps mixed together.
We've tried automation before and it broke every time a vendor changed their interface.
That's a brittle selector problem. Automation that depends on specific UI element positions breaks the moment a vendor updates their interface. We design integrations against APIs or stable data layers where they exist, and where we have to use UI automation, we build monitoring that catches breakage before the workflow fails silently and nobody notices for a week.
How do you handle exceptions without a human having to monitor the automation constantly?
Human-in-the-loop checkpoints. The automation handles what it can handle, and any exception that falls outside the rules routes to a person for a decision rather than failing or guessing. We define those boundaries upfront and instrument them so you can see how often exceptions occur and whether the rules need adjusting.
How do we know the automation is saving us time rather than creating new work?
We instrument from day one. The time saved per automated task is measurable; the maintenance overhead, the exception rate, and the error rate are all tracked. If the numbers show the automation is costing as much in oversight as it saves in labor, we adjust the scope or the design. Automation that creates a new full-time job to manage it isn't automation.
Industries we know well
The same service, sharpened by the regulations and realities of your sector.
See it on your own data.
Book a 30-minute discovery call and we'll walk through your use case.