Document Intelligence Suite
Extract, classify, and route millions of invoices, contracts, and customs forms across 28 document types and nine languages.
The problem
Your accounts-payable team keys in 11,000 invoices a month. Your trade-finance team rekeys 3,400 customs forms across four document layouts. Your KYC team reviews 900 onboarding files weekly with a 6% rework rate. Pure-OCR vendors handle the easy cases and break on the long-tail layouts that cause the most pain.
IDC's 2024 Document Automation Market report estimates that 71% of mid-market document-processing workflows in APAC remain manual or semi-manual, despite 12 years of OCR investment.[^1] The cost: an average of 2.4 FTE per 10,000 monthly documents, plus a 4-7% downstream error rate.
Our approach
Source: scanner / email inbox / SFTP drop / API webhook
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File ingest (Laravel job queue)
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OCR layer: AWS Textract (default) | Azure DI | Tesseract for air-gapped
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LLM extraction: Claude Sonnet 4.6 with structured-output prompts
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Schema validation (Zod-equivalent in PHP) + confidence scoring
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High-conf (≥ 0.9): auto-route to ERP/workflow
Med-conf (0.7-0.9): human review queue (Filament UI)
Low-conf (< 0.7): reject + retry with different OCR engine
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Audit log + observability: every page, every field, every override
Who it is for
- A regional trade-finance bank in Singapore processing 12,000+ letters of credit and customs documents monthly across English, Chinese, and Bahasa.
- A 600-person Japanese manufacturer with a paper-heavy AP function across multiple suppliers and a 14-day average invoice-to-pay cycle.
- A Vietnamese logistics provider processing customs paperwork and bills of lading across English, Vietnamese, and Chinese.
Tech stack
- OCR: AWS Textract (default), Azure Document Intelligence (Japan East deployments), Google Document AI (multi-region), Tesseract 5 + PaddleOCR for air-gapped Chinese script
- LLMs: Claude Sonnet 4.6 for structured extraction, Claude Haiku 4 for classification, GPT-4o for layout-heavy pages
- Validation: PHP Symfony Validator with custom rule sets per document type
- Human review UI: Filament 3 (Laravel admin) with side-by-side document and extracted fields
- Storage: S3 / Azure Blob / Alibaba OSS depending on cloud, with lifecycle policies for retention
- Backend: Laravel 12 with Horizon-managed queues, scaled on AWS ECS or Azure Container Apps
Integration list
SAP ECC, SAP S/4HANA, Oracle E-Business Suite, NetSuite, Workday Financials, Microsoft Dynamics 365 Finance, Sage Intacct, Yonyou, Kingdee, OBIC7, Concur, Coupa, Tradeshift, Tungsten Network, custom inbound APIs.
Deployment timeline
| Week | Activity |
|---|---|
| Week 1 | Document inventory; sample 200-500 documents per type; success metrics agreed |
| Week 2 | OCR engine selection; structured-output prompt design per document type |
| Week 3 | Validation rules; human review UI configured; ERP integration tested in staging |
| Week 4 | Shadow mode on first document type (extraction runs, humans still keying) |
| Week 5-6 | Cutover on first document type; expand to second |
| Week 7-8 | Add remaining document types; tune confidence thresholds |
Mini-ROI
A regional trade-finance bank in Singapore processed 9,400 letters of credit in the first 60 days post-launch with a 91% straight-through rate (no human touch). Annualised labour saving: US$680,000. Error rate dropped from 4.1% to 0.7%. The freed capacity was redeployed into customer-relationship work, not headcount reduction.
IDC estimates that document automation reaches payback in 5-9 months for mid-market enterprises processing >5,000 documents monthly.[^2] Across our last 14 deployments, median payback sat at 6.8 months.
Pricing tiers
| Tier | Setup (one-time) | Monthly run cost | Best for |
|---|---|---|---|
| Starter | US$24,000 - US$42,000 | From US$1,400/mo | 1-2 document types, single language, up to 5,000 docs/month. |
| Scale | US$55,000 - US$110,000 | From US$3,800/mo | 5-10 document types, 3-5 languages, up to 40,000 docs/month. |
| Strategic | US$130,000 - US$260,000 | From US$8,500/mo | 15+ document types, custom layouts, full multi-language, dedicated FinOps for OCR cost. |
All tiers include reprocessing of historical batches if needed for backfill.
Frequently asked questions
How does this handle handwritten Chinese, Japanese, or Korean script? We use a layered approach: AWS Textract or Azure DI for printed text, plus PaddleOCR fine-tuned on handwriting for the script-heavy fields, plus Claude Sonnet 4.6 to reconcile both. Accuracy on handwritten Japanese kanji invoices in our last benchmark sat at 96.4% on field-level extraction.
Can the system handle layouts we have not seen before? Yes, within bounds. Claude Sonnet 4.6 generalises well to layout variations within a document type. For genuinely new document types, we add 30-80 sample documents and retune the structured-output prompt. Typical cost per new document type: US$6,000-US$11,000.
What happens to documents we cannot extract automatically? They route to a human review queue in the Filament admin UI. The reviewer sees the original document side-by-side with the extracted fields and can correct or override. Corrections feed back into the evaluation harness for ongoing improvement.
How do you handle PII and bank-grade data? Documents stay in your cloud account, in your chosen region. We use Anthropic's no-training-on-customer-data API. For higher sensitivity, we deploy fully on-premise with open-weights models and Tesseract OCR. We have shipped under MAS-TRM and J-FSA controls.
Can you process bills of lading and customs forms in mixed languages? Yes. Claude Sonnet 4.6 handles mixed-language documents (e.g., Vietnamese + Chinese + English on a single shipping document). Field extraction confidence is reported per language as well as overall.
How do we audit the AI's decisions? Every extraction logs the OCR text, the LLM prompt, the LLM response, the validation result, the routing decision, and any human override. Logs are searchable, exportable, and retained per your data retention policy. Audit-ready out of the box.
Will the system handle peak-season volume? Yes. The queue auto-scales horizontally. We size the cluster to handle 3x average daily volume by default. Peak-season tuning is included in the Scale and Strategic tiers.
Can we connect this to a downstream RPA or workflow tool? Yes. Outputs route via webhook, message queue (RabbitMQ, SQS), or direct REST API. We have integrated with UiPath, Automation Anywhere, Power Automate, and n8n.
Where this is most often deployed
Industries where AIMenta frequently scopes this kind of solution.
Beyond this solution
Browse our other productized solutions, plus the verticals and Asian markets where they ship.
Other solutions
By industry
By Asian market
Frequently asked questions
What document types does the suite process?
The suite handles PDF (native and scanned), Word, Excel, PowerPoint, HTML, and image formats (TIFF, PNG, JPEG) via OCR. It processes contracts, invoices, regulatory filings, medical reports, engineering specs, and internal memos. Custom extractors can be trained for proprietary document layouts in under two weeks.
How accurate is data extraction compared to manual keying?
For structured documents (invoices, purchase orders) extraction accuracy consistently exceeds 98.5% on benchmark corpora. Semi-structured documents (contracts, regulatory submissions) achieve 94–97% field-level accuracy. All extractions include a confidence score; items below threshold are flagged for human review rather than silently passed through.
Can we connect the suite to our existing ERP or document management system?
Yes. Pre-built connectors exist for SAP S/4HANA, Oracle NetSuite, Microsoft SharePoint, Google Drive, and Salesforce. For systems without a native connector, a REST/webhook bridge is configured during implementation. Document metadata and extracted fields are written back to your system of record automatically.
What languages are supported for extraction and classification?
English, Traditional Chinese, Simplified Chinese, Japanese, Korean, Bahasa Malaysia, Bahasa Indonesia, Vietnamese, and Thai. Mixed-language documents (common in APAC trade finance) are handled by per-field language detection. Language scope can be expanded by training on additional corpora provided by the client.
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