80%
Faster Processing
3x
Data Volume Handled
0
Manual Interventions
47min
End-to-End Latency
Overview
FinTech Corp's risk analytics team was drowning in delayed data. Their legacy batch processing system — built a decade ago — ran overnight jobs that took 4+ hours, meaning traders started each day with stale risk calculations. As transaction volumes grew 3x in two years, the system was buckling. They needed a fundamental rearchitecture, not a patch.
Challenge
Legacy batch processing couldn't keep up with real-time trading data, causing 4-hour delays in risk calculations.
Solution
Deployed autonomous AI agents to orchestrate streaming data pipelines with intelligent routing and self-healing capabilities.
Result
Processing time reduced from 4 hours to 47 minutes. The system now handles 3x the data volume with zero manual intervention.
Implementation
Discovery & Architecture
Mapped all 23 data sources, identified bottlenecks in the legacy ETL chain, and designed a streaming-first architecture with intelligent agent orchestration.
Agent Development
Built three autonomous agents: a data router that classifies and prioritizes incoming streams, a pipeline monitor that detects anomalies in real-time, and a self-healing agent that automatically remediates failures.
Migration & Parallel Run
Ran the new streaming system in parallel with legacy batch for 2 weeks, validating data accuracy to 99.97% before cutover.
Optimization & Handoff
Fine-tuned agent decision thresholds, reduced infrastructure costs by 30% through intelligent scaling, and trained the internal team on operations.
Technology Stack
"Condor didn't just modernize our pipeline — they gave us a system that thinks for itself. Our risk team now has real-time data they actually trust."
James Liu
VP of Engineering, FinTech Corp
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