ML pipeline architecture
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Case Study
AI-Powered Accounting System: Fortune 500 Manufacturing
Enterprise AI accounting with automated document recognition, intelligent tax processing, and real-time insights across 23 subsidiaries.
99.8%Processing accuracy
6xSpeed improvement
$850K+Annual savings
50K+Documents/month
99.8%
Processing accuracy
6x
Speed improvement
$850K+
Annual savings
Year: 2022Industry: Manufacturing / EnterpriseTimeline: 10 months
Problem
A Fortune 500 manufacturer processed 50,000+ financial documents monthly across 23 subsidiaries. Manual data entry had 15% error rate, tax compliance was reactive, and month-end close took 12 days. They needed automation without losing accuracy.
Constraints
- •23 subsidiaries with different chart of accounts
- •15+ document types (invoices, receipts, contracts)
- •Multi-currency and multi-language support
- •SOX compliance requirements
Solution
Deployed a custom ML pipeline: OCR extracts text, NLP classifies documents, and trained models extract structured data. Confidence scoring triggers human review only for edge cases. Real-time dashboards give CFO visibility across all subsidiaries.
Deliverables
- Custom OCR pipeline for financial docs
- Document classification ML model
- Data extraction with confidence scoring
- Multi-subsidiary consolidation engine
- Tax compliance automation
- Real-time CFO dashboard
- Audit trail and SOX reporting
- ERP integration layer
Artifacts
Documents and deliverables from the project
Verification report
Release gate
Verification / Quality gates
6-phase checklist before release
01Build
02ML model accuracy validation
03OCR accuracy benchmarks
04SOX compliance audit
05ERP integration tests
06Performance testing (50K docs)
All gates passed
6/6
Tech stack
PythonTensorFlowDjangoPostgreSQLOCRAWSKubernetes
Outcome
99.8% accuracy (up from 85% manual). 6x faster processing. $850K+ annual savings from reduced manual work. Month-end close reduced from 12 days to 3. Full SOX compliance maintained with automated audit trails. Now available as AccountingAI Professional SaaS product.
Hard parts we solved
Document Variety Handling
15+ document types, hundreds of vendor formats. We trained specialized models for each category and built a routing layer that picks the right model.
Confidence-based Human Loop
Not all documents can be auto-processed. Built a confidence threshold system where low-confidence extractions go to human review without blocking the pipeline.
Multi-subsidiary Consolidation
23 subsidiaries with different accounting standards. Built a normalization layer that maps all charts of accounts to corporate standard for consolidated reporting.
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