The Challenge
A leading financial services provider was struggling with a massive library of legacy RPA bots. Over 50 bots had been deployed across the organization — each one brittle, requiring constant maintenance, and unable to handle even minor changes in UI or business logic. Weekly failures were the norm, not the exception.
The operational burden was immense: a dedicated team of 12 engineers spent most of their time patching broken scripts rather than building new capabilities. Leadership recognized this wasn’t sustainable.
Our Approach
We implemented a phased transition from RPA to Agentic AI using our Calibrate · Decode · Activate framework:
Calibrate — Assessment with BenchMark: Used BenchMark to analyze the underlying legacy systems and map every process handled by the RPA bots. This identified which bots were redundant, which processes could be consolidated, and which required full agent redesign.
Decode — Knowledge Extraction with DeepDive: Deployed DeepDive to reverse-engineer the business logic embedded in the RPA scripts and underlying systems, building a comprehensive knowledge graph of process dependencies and exception handling rules.
Activate — Operationalize with AI Agents: Built intelligent agents capable of reasoning through process exceptions and adapting to UI changes. Deployed as a managed service with real-time monitoring and performance optimization.
The Results
- 60% reduction in maintenance costs: AI agents adapted to changes that previously broke RPA scripts, dramatically reducing the engineering burden.
- 99.5% process success rate: Up from 82% with the legacy RPA layer. Agents handle exceptions intelligently instead of failing silently.
- 10x transaction throughput: The new architecture processed orders of magnitude more transactions without proportional infrastructure scaling.
- 4 months to full production: From initial assessment to complete production cutover in under 120 days.