Case Study / Data Reconciliation

50,000 records reconciled overnight, not in days.

We replaced a four-person, 8-to-10-day monthly reconciliation across three source systems with an autonomous multi-agent pipeline that runs nightly, with a full audit trail.

Mid-market financial services firm (anonymous)
Multi-agent data reconciliation pipeline
Runs nightly, results ready by morning
OvernightReconciliation cycle, down from 8-10 business days
50K+Records reconciled monthly across 3 source systems
4Person team freed for higher-value analysis
NightlyAutonomous runs, with a full audit trail on every match

The challenge

A mid-market financial services firm was manually reconciling 50,000 or more records every month across three separate source systems. The process required a dedicated team of four and took 8 to 10 business days per cycle.

It still produced errors that created downstream compliance issues. Every month was a fire drill.

What we built

A multi-agent reconciliation pipeline that runs the whole cycle nightly, with results ready by morning. People stay in the loop only where judgment is needed.

  • One extraction and normalization agent per source system
  • An orchestrator that matches records across all three systems
  • An exception handler that routes ambiguous cases to human reviewers with full context

The outcome

The reconciliation cycle dropped from 8 to 10 business days to overnight processing. The four-person team was reassigned to higher-value analysis work.

Exception rates fell as the system learned from human reviewer decisions over time, and audit compliance improved because every match decision now has a traceable log.

What was a monthly fire drill is now an autonomous capability that runs while the team sleeps.

Why it matters at the platform level

The firm owns the pipeline, the data, the models, and the IP. It is not a tool they rent; it is infrastructure that compounds. The more it runs, the fewer exceptions it raises, and the cleaner the audit trail becomes.

Frequently asked questions

What did the reconciliation process look like before?

A four-person team manually reconciled 50,000 or more records a month across three source systems, a cycle that took 8 to 10 business days and still produced errors that caused downstream compliance issues.

How does the AI pipeline work?

One agent per source system extracts and normalizes the data, an orchestrator matches records across systems, and an exception handler routes ambiguous cases to human reviewers with full context. It runs nightly with results by morning.

Are humans still involved?

Yes, for judgment. Routine matches run autonomously, while ambiguous cases are routed to human reviewers with full context, and the system learns from their decisions so exception rates fall over time.

What happened to the four-person team?

They were reassigned from manual reconciliation to higher-value analysis work. The pipeline runs the routine monthly cycle on its own.

Is the process auditable?

Yes. Every match decision is logged and traceable, which improved audit compliance compared with the old manual process.

Do we own the system?

Yes. You own the pipeline, the data, the models, and the IP. It is infrastructure your firm keeps, not a tool you rent.

Reconciling by hand every month?

Book an Operating Assessment. We map your highest-value processes and put a hard ROI estimate on them before any build.

Book an Operating Assessment