AI-Powered Receipt Ingestion at Enterprise Scale

Client:

PE-backed portfolio company

Time to First Value:

3 weeks

Users Enabled:

Finance + Operations teams

Our Problem

Problem

A PE-backed portfolio company processed a constant stream of receipts Thousands per week—from field operations and vendors. To enter the required fields into the ERP, they staffed a 10-person offshore data-entry team.

Slow: Batch turnaround often lagged by days, delaying close and cash visibility.
Error-prone: Human keying introduced inconsistencies that downstream teams had to reconcile.
Expensive: Labor and rework costs scaled with volume; peak periods required overtime or additional temps.
Unscalable: Growth meant linearly adding headcount, training, and QA.

Our Solution

Solution

We built and deployed an autonomous receipt-ingestion agent that converts raw images/PDFs into clean, structured records ready for the company’s database.

Vision + Language Stack: High-accuracy OCR and layout parsing feed an LLM extraction layer that understands vendor formats and line-item quirks.
Confidence-based QA: Each field carries a confidence score. Low-confidence edge cases route to a lightweight human check; routine receipts flow straight-through.
Schema-aware Validation: Business rules (dates, taxes, GL mappings, currency, totals vs. line-item sums) catch and correct common discrepancies before they hit the ERP.
Secure & Auditable: PII redaction, encryption in transit/at rest, and full audit logs for every record and correction.
Drop-in Integration: SFTP/watch-folder ingestion + API connector to the company’s database; no workflow disruption.

Our Results

Results

>90% faster and cheaper receipt processing, with materially fewer downstream corrections. The finance team closes faster, FP&A sees spend in near-real-time, and operations no longer staff to volume spikes.

Metric	Before (Manual)	After (Autonomous Agent)
Cycle time	Multi-day batches	Same-day straight-through for the vast majority
Unit cost	Labor-driven and rising with volume	>90% reduction via automation
Error rate	Frequent rework & exceptions	Materially lower; rule/validation catching errors upstream
Scalability	Linear with headcount	Elastic—handles surges without hiring
Visibility	Delayed spend insight	Near-real-time dashboards & exports

What this means for PE operators

Immediate EBITDA lift: Meaningful opex reduction without touching revenue.
Working-capital impact: Faster, cleaner data accelerates accruals, vendor reconciliation, and cash control.
Replicable playbook: One build, many beneficiaries—roll out to other portcos with similar document flows (AP, expense, proof-of-delivery, COIs).