Automation & Process Intelligence

We run a process portfolio analysis — classifying every process by automation ROI (volume × handling time × error rate) against automation complexity. High-volume, rules-based, structured-input processes are automated first. Low-volume, high-judgment processes stay human-in-the-loop.

RPA mimics user actions on a screen — fragile, maintenance-heavy, breaks when the UI changes. AI agents reason about inputs and select actions dynamically — more resilient, able to handle variation and exceptions. Most organisations are migrating from RPA to AI agents for core processes and keeping RPA only where API access doesn't exist.

Every automation we build has an exception handling framework: classify the exception, attempt resolution, escalate to human if unresolvable, log for pattern analysis. Over time, recurring exceptions become new automation rules. The goal is to shrink the exception rate continuously, not just automate the happy path.

Make (formerly Integromat), n8n, and Zapier for workflow orchestration. Custom Python or TypeScript for complex logic. API-first integrations wherever possible — we avoid UI-scraping RPA unless there's no API available. Platform choice depends on your existing tech stack and internal maintenance capability.

We document every automation fully, train your team on monitoring and basic maintenance, and offer ongoing support retainers for complex workflows. For AI agent-based automations, we set up monitoring dashboards so you can see exception rates, success rates, and processing volumes in real time.