When exceptions are the rule, agents step in.
We built an agentic finance platform for global enterprises — automating AP/AR, invoice reconciliation and vendor payments from document intake through to payment release.
Rules-based RPA had covered the easy 30% of workflows. We were brought in to handle the hard 70% — the exceptions, edge cases and cross-currency mismatches that required human judgement.
RPA handled the easy cases. Exceptions went back to humans — every time.
Legacy automation broke on mismatched line items, missing POs and currency discrepancies. These weren't edge cases — they were the majority of real-world invoices. Every exception triggered a manual review queue that grew faster than the team could clear it.
The business needed automation that could reason through ambiguity, not just match patterns — and it needed a quality gate to ensure no hallucinated output ever reached the books.
Three agents, one pipeline, nothing left unverified.
Research Agent
Gathers full context from the organization's knowledge base before any reconciliation step begins — pulling relevant contracts, POs and historical transactions so the Finance Agent has everything it needs.
Finance Agent
Reconciles invoices against the gathered context, handling mismatched line items, missing references and currency discrepancies without routing back to a human queue.
Editor Agent
Formats the reconciled output for downstream systems — ensuring every record lands in the exact structure the ERP or accounting platform expects.
LLM-as-Judge Quality Gate
Every agent output is scored by a separate judge model before it moves to the next stage. Hallucinations and low-confidence outputs are caught and flagged before they reach the books.
Voice-Initiated Workflows
Phone-initiated finance requests feed into the same agentic pipeline — so voice, email and system-triggered workflows are all processed identically.
Finance runs itself. From document intake to payment release.
End-to-End Automation
Every stage — from document intake through reconciliation to payment — handled by agents, without manual touch.
Outputs Quality-Checked
The LLM-as-judge layer scores every single agent output before it moves forward. Nothing unverified enters the books.
Manual Exception Queues
The hard 70% that used to bounce back to humans now resolves within the pipeline — without human intervention.