Intelligent process automation pulls together several technologies — RPA, AI agents, document understanding, OCR, NLP, workflow orchestration — to handle work that previously needed humans. Done well it is one of the highest-ROI digital programmes available; done badly it is a pile of unmaintained bots.
Most automation programmes start strong and decay quickly because nobody owns the bots after they are deployed. We design the operating model — backlog, on-call, change management, SLOs — alongside the automation itself.
What we build
Orchestrated workflows
End-to-end workflows that combine human work, system actions and AI inference under a single orchestrator. Visibility, retries, SLAs and audit are first-class concerns — not afterthoughts.
Orchestration is what turns automation from a pile of point bots into a maintained system. We build it on durable workflow engines (Temporal, Camunda, AWS Step Functions, equivalents) chosen per workload.
AI agents inside the workflow
LLM-driven agents that handle nuance — drafting responses, classifying ambiguous inputs, summarising case files — bounded by explicit tool definitions and human-in-the-loop where the stakes warrant it.
Agents are the most powerful new ingredient in process automation in years. They are also the easiest to misuse. We build agents with clear tool contracts, audit trails and explicit fallback paths — they fail safely or they don't ship.
Document understanding
Extraction, classification and structuring of the documents most organisations still receive — invoices, claims, contracts, application forms, regulatory filings. Trained on real data, monitored for drift, retrained on a schedule.
Modern multimodal LLMs have changed the document-understanding economics meaningfully. Pipelines that used to need specialist tools now run cleanly on general-purpose models. We build for the current state of the art and we re-evaluate every six months.
RPA where it still fits
Where the underlying system genuinely lacks an API and there is no realistic short-term route to one. We treat RPA as a tactical bridge, not a strategy, and we are honest with clients about its operating cost over time.
RPA gets a bad reputation because it is often the only realistic answer for a workflow that the underlying vendor has neglected. We use it deliberately and document the technical-debt the bot represents, so the client can retire it when the underlying system catches up.
How we approach it
- Start with the process and the data, not the tool.
- Build for the long tail — most processes have edge cases that look small but break naive automation.
- Operate as a product, with on-call, change management and SLOs.
- Measure against the cost of the work the automation actually replaces, not the headline FTE estimate.
- Plan for retirement — bots are tactical fixes, not permanent infrastructure.