AI Workflow Automation
The repetitive work, handed over
Most of the value in AI is not a new product; it is the twenty-step internal process nobody enjoys. We automate those: the intake that arrives by email, the document that gets rekeyed, the ticket that gets routed by hand, the follow-up nobody sends. Work moves through a pipeline you can watch, with exceptions pushed to a person instead of silently failing.
- Process map with volumes, handling time and the cost of each step today
- Automation pipelines with explicit exception paths
- Integrations with email, CRM, ERP, ticketing and storage
- Operator console: queue, exceptions, audit trail, manual override
- Before/after measurement on handling time and error rate
- 01
Measure the current process
Volume, time per case and error rate, so the result can be proven rather than claimed.
- 02
Automate the spine first
The high-volume happy path goes first; edge cases route to a human from day one.
- 03
Close the loop
Every exception a human resolves becomes a test case and, where it helps, training data.
Classic RPA follows fixed scripts against fixed screens and breaks when either changes. We automate against APIs where they exist, and use models for the judgement steps — reading a document, classifying an intent, drafting a reply — with rules and verification around them.
Yes. Where a client is standardised on Microsoft we build with Power Platform, Azure OpenAI and Copilot extensibility; elsewhere we use Temporal or n8n with our own services. The judgement layer is portable either way.