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Accounting

AI-Powered Financial Operations

A regional CPA firm (45 professionals, 300+ clients)

$330K annual savings with 80% reduction in manual bookkeeping

KEY RESULTS

$330K
Annual savings from reduced labor costs and improved utilization. Firm didn't replace 3 departing bookkeepers.
95%
Auto-categorization rate after 60-day learning period. Staff reviews exceptions only.
3-5 days
Month-end close, down from 15-20 days. Some clients now close weekly.
38%
Advisory revenue as percentage of total, up from 12%. Staff time freed for higher-value work.

Before & After

✗ Before

  • •Transaction categorization: 70% of staff time
  • •Month-end close: 15-20 days per client
  • •Manual entry error rate: 3-5%
  • •Client calls about late financials: 40/month
  • •Billable hours on bookkeeping: 65%
  • •Advisory revenue: 12% of total

✓ After

  • •Transaction categorization: 10% of staff time (exceptions only)
  • •Month-end close: 3-5 days per client
  • •AI entry error rate: 0.4%
  • •Client calls about late financials: 5/month
  • •Billable hours on bookkeeping: 25%
  • •Advisory revenue: 38% of total

The Challenge

This CPA firm was trapped in the bookkeeping treadmill. Partners wanted to shift to higher-margin advisory services, but staff accountants spent 70% of their time on transaction categorization and reconciliation. The firm was competing on price for commoditized work while higher-value advisory opportunities went unserved.

PAIN POINTS

  • ×300+ clients meant 50,000+ monthly transactions to categorize—the same vendors, same categories, every month
  • ×Month-end close took 15-20 days, frustrating clients who needed timely financials for decision-making
  • ×Error rates of 3-5% on manual entries required extensive review cycles and occasional client-facing corrections
  • ×Fee pressure from bookkeeping-only competitors and DIY software eroded margins
  • ×Top accounting talent left for firms with modern tech stacks and advisory practices

Technology Stack

  • →Bank feed aggregation via Plaid API for real-time transaction import
  • →ML categorization model trained per-client on historical patterns
  • →OCR for invoice data extraction (95%+ accuracy on structured invoices)
  • →Reconciliation engine with fuzzy matching for bank statement items
  • →Anomaly detection for unusual transactions or variance patterns

Implementation Approach

1

Phase 1: Bank Feed Automation (Weeks 1-2)

Connected all client bank accounts and credit cards via Plaid. Transactions flow in daily instead of waiting for monthly statements. Built standardization layer to normalize vendor names across institutions ('AMZN MKTP' = 'Amazon' = 'AMAZON.COM').

2

Phase 2: Intelligent Categorization (Weeks 3-5)

Trained ML models on each client's 12-24 months of categorized transactions. Models learn client-specific patterns: which vendors map to which accounts, how to handle split transactions, client-specific naming conventions. Achieved 95%+ auto-categorization rate within 60 days of learning.

3

Phase 3: Reconciliation Automation (Weeks 6-7)

Built reconciliation engine that matches bank transactions against GL entries. Handles timing differences, partial matches, and bank fees automatically. Surfaces only true discrepancies for human review. Reduced reconciliation time by 85%.

4

Phase 4: Invoice & AP Processing (Weeks 8-10)

Deployed OCR for incoming invoices. System extracts vendor, amount, due date, line items. Matches against POs where applicable. Routes for approval based on amount thresholds. Average invoice processing: 45 seconds vs. 8 minutes manually.

Results Breakdown

$330K

Annual savings from reduced labor costs and improved utilization. Firm didn't replace 3 departing bookkeepers.

95%

Auto-categorization rate after 60-day learning period. Staff reviews exceptions only.

3-5 days

Month-end close, down from 15-20 days. Some clients now close weekly.

38%

Advisory revenue as percentage of total, up from 12%. Staff time freed for higher-value work.

Key Learnings

  • 1.Per-client model training is worth the effort. Generic categorization gets 70% accuracy; client-specific models get 95%+.
  • 2.Clean historical data is the prerequisite. Spent first 2 weeks fixing categorization errors in training data.
  • 3.Month-end close acceleration is the killer feature for clients. CFOs care about timely data, not how it's produced.
  • 4.Staff retraining is essential. Bookkeepers became 'exception handlers' and 'client advisors'—different skills, better career paths.
  • 5.The real unlock is advisory services. Automation frees capacity for the work clients actually value (and pay premium rates for).

Industry Context

60% of accounting firms have adopted AI for automation. Industry benchmarks show 80-90% reduction in manual bookkeeping tasks and 25% improvement in forecasting accuracy. The shift isn't optional: clients expect real-time financials, and competing on hourly bookkeeping rates is a race to the bottom. Winners are firms that use automation to fund advisory practices.

TIMELINE

60 days for core automation

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