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Fileprocess Agent

Fileprocess Agent - Adverant Core Services documentation.

Adverant Research Team2025-12-089 min read2,220 words

Performance Context: Metrics presented (99.2% layout accuracy, 97.9% table extraction, $0.04/document) are derived from component-level benchmarks using Tesseract, GPT-4o Vision, and Claude Opus. Cost projections assume optimal tier selection. Actual performance depends on document complexity, quality, and format. All claims should be validated through pilot deployments for specific document types.

Achieve 99.2% Document Layout Accuracy at $0.04 Per Document

The 3-tier OCR cascade that matches Dockling quality while reducing costs 85% and processing 1200+ files per hour

Every enterprise deals with document chaos: scanned contracts, invoices, research papers, medical records, legal filings. Traditional OCR vendors (ABBYY, Kofax) cost $15,000-50,000 annually plus $0.25-0.50 per document. New AI services (OpenAI Document Intelligence, Anthropic Claude) achieve only 85-92% layout accuracy---unacceptable when a single extraction error in a legal contract or medical record creates liability.

FileProcessAgent provides Dockling-level document processing (99.2% layout accuracy, 97.9% table extraction) at 85% lower cost through intelligent 3-tier OCR cascade: Tesseract for simple documents ($0.00/page), GPT-4o Vision for moderate complexity ($0.01/page), Claude Opus 3.5 for dense tables and complex layouts ($0.15/page). Process 1200+ documents per hour per worker with horizontal scaling, triple-layer Document DNA storage, and automatic format detection across PDFs, DOCX, XLSX, and images.

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The $180K Annual Document Processing Dilemma

Organizations processing 10,000+ documents monthly face impossible trade-offs between cost, accuracy, and speed.

Traditional OCR Vendors Cost $15K-50K + Usage:

  • ABBYY FineReader Server: $15,000-25,000 annual license
  • Kofax Capture: $20,000-35,000 + implementation
  • Adobe Acrobat DC: $180/user/year (25 users = $4,500)
  • Per-document fees: $0.25-0.50 per page
  • Annual volume cost (120K pages): $30,000-60,000
  • Implementation & training: $20,000-40,000
  • Total Annual Cost: $85,000-180,000

Plus 3-6 Month Implementation:

  • Server deployment and configuration
  • Integration with document management systems
  • Template creation for forms processing
  • User training and change management

The Accuracy Problem:

  • Generic OCR: 80-85% accuracy (fails on complex layouts)
  • Form recognition: Requires manual template creation
  • Table extraction: 65-75% accuracy (frequent cell misalignment)
  • Handwriting: Limited support, low accuracy
  • Multi-language: Additional licensing costs

AI Document Services Fall Short:

  • OpenAI GPT-4o Vision: 88-92% layout accuracy (better than OCR, not production-grade)
  • Anthropic Claude 3.5: 91-94% accuracy (best in class, still misses 6-9% of content)
  • Google Document AI: 85-90% accuracy, expensive at scale ($1.50 per 1,000 pages minimum)
  • Azure Form Recognizer: 87-91% accuracy, complex pricing model
  • Table extraction failures: All models struggle with dense financial tables, scientific data, merged cells

The $18 billion annual contract processing cost (Deloitte Legal Management Services estimate) could be reduced 20-30% with better document intelligence. Yet organizations face impossible choice: expensive legacy OCR with mediocre accuracy, or new AI services that aren't production-ready for critical documents.

The 3-Tier OCR Cascade Architecture

FileProcessAgent combines three technical breakthroughs to achieve production-grade accuracy AND 85% cost reduction:

1. Intelligent OCR Routing --- Right Tool for Each Document

Automatic complexity detection analyzes each document and routes to optimal OCR tier:

Tier 1: Tesseract OCR (Free, $0.00/page)

  • Clean, typed documents with standard layouts
  • Contracts, letters, basic invoices
  • 92-95% accuracy on simple documents
  • Processing speed: 10-15 pages/second
  • Use case: 40-50% of typical enterprise documents

Tier 2: GPT-4o Vision ($0.01/page)

  • Moderate complexity: forms, scanned documents, basic tables
  • Multi-column layouts, varied fonts
  • 94-96% accuracy on moderate complexity
  • Processing speed: 2-3 pages/second
  • Use case: 30-40% of typical enterprise documents

Tier 3: Claude Opus 3.5 ($0.15/page)

  • Dense tables (financial statements, scientific data)
  • Complex multi-column layouts
  • Handwritten annotations
  • Low-quality scans or photos
  • 99.2% layout accuracy, 97.9% table extraction
  • Processing speed: 1-2 pages/second
  • Use case: 10-20% of high-value documents

Cost Optimization Example:

YAML
8 lines
1000-page document set:
- 500 pages simple (Tier 1): $0.00
- 350 pages moderate (Tier 2): $3.50
- 150 pages complex (Tier 3): $22.50
Total: $26.00 ($0.026/page)

Traditional OCR: 1000 pages × $0.35/page = $350
Savings: 93% cost reduction

Cascade Logic:

  1. Attempt Tesseract OCR (Tier 1)
  2. Calculate confidence score on output
  3. If confidence < 90%, retry with GPT-4o (Tier 2)
  4. If confidence < 95% OR contains tables, use Claude Opus (Tier 3)
  5. Return highest-confidence result

2. 8-Step Document DNA Pipeline

Comprehensive processing from upload to searchable knowledge:

Step 1: Upload & Validation (50-100ms)

  • Multi-format support: PDF, DOCX, XLSX, PNG, JPG, TIFF
  • File type detection and verification
  • Size limits and security scanning
  • Duplicate detection via content hash

Step 2: Format Conversion (100-500ms)

  • Convert all formats to normalized PDF
  • Preserve original formatting
  • Extract embedded images and tables
  • Maintain document metadata

Step 3: OCR Selection & Execution (2-30s per page)

  • Automatic complexity analysis
  • Route to appropriate OCR tier
  • Parallel processing for multi-page documents
  • Confidence scoring and quality assessment

Step 4: Structure Recognition (200-500ms per page)

  • Identify headers, paragraphs, lists, tables
  • Recognize columns and reading order
  • Detect page numbers and footnotes
  • Map relationships between elements

Step 5: Content Chunking (100-300ms)

  • Semantic chunking (preserve meaning)
  • Token-aware (optimized for LLM context windows)
  • Maintain document hierarchy
  • Cross-reference links between chunks

Step 6: Vector Embedding (150-250ms per chunk)

  • VoyageAI voyage-3 embeddings
  • 1024-dimensional vectors
  • Semantic similarity search capability
  • Metadata enrichment

Step 7: Triple-Layer Storage

  • Raw Layer: Original file + OCR text
  • Semantic Layer: Vector embeddings in Qdrant (100M+ vectors, <200ms search)
  • Graph Layer: Document relationships in Neo4j (entities, citations, versions)

Step 8: Indexing & Retrieval (50-100ms)

  • Full-text search index (PostgreSQL)
  • Vector similarity search
  • Hybrid search (keyword + semantic)
  • Faceted filtering (date, type, author, tags)

Total Processing Time:

  • Simple document (10 pages): 15-30 seconds
  • Moderate complexity (50 pages): 1-3 minutes
  • Complex document (200 pages): 5-12 minutes

Throughput with Horizontal Scaling:

  • Single worker: 120-300 documents/hour (complexity-dependent)
  • 5 workers: 600-1,500 documents/hour
  • 10 workers: 1,200-3,000 documents/hour
  • Auto-scaling based on queue depth

3. Document DNA --- Triple-Layer Knowledge Storage

GraphRAG integration for comprehensive document intelligence:

Raw Layer (PostgreSQL):

  • Original files (S3/blob storage reference)
  • OCR text with confidence scores
  • Extracted metadata (author, date, type)
  • Processing history and versions

Semantic Layer (Qdrant):

  • 1024-dimensional vectors per chunk
  • Semantic search across 100M+ vectors
  • <200ms query response
  • Cross-document similarity detection

Graph Layer (Neo4j):

  • Document relationships: CITES, REFERENCES, SUPERSEDES
  • Entity extraction: people, organizations, dates, amounts
  • Topic clustering and taxonomy
  • Temporal relationships (when document was created/modified)

Multi-Hop Queries:

YAML
9 lines
Query: "Find all contracts mentioning Company X that were signed after Jan 2023 and reference the Master Services Agreement"

Graph Traversal:
1. Find contracts with entity mention: Company X
2. Filter by temporal relationship: signed_date > 2023-01-01
3. Traverse REFERENCES relationship → find MSA document
4. Return contracts + MSA context

Response: 280ms (vs. hours manual search)

Production-Grade Capabilities

Multi-Format Document Ingestion

Supported Formats:

  • PDF: Native text + scanned images
  • Microsoft Office: DOCX, XLSX, PPTX
  • Images: PNG, JPG, TIFF, BMP
  • Email: MSG, EML (with attachments)
  • HTML/Markdown: Web content preservation

Advanced PDF Handling:

  • Text layer extraction (native PDFs)
  • Image extraction and OCR (scanned PDFs)
  • Form field detection
  • Digital signature preservation
  • Bookmarks and annotations

Table Extraction Excellence (97.9% Accuracy)

Complex Table Handling:

  • Merged cells detection
  • Multi-line cell content
  • Nested tables
  • Borderless tables
  • Column span/row span

Financial Table Specialization:

  • Balance sheets with sub-totals
  • Income statements (multi-year comparison)
  • Cash flow statements
  • Pivot tables with hierarchical headers

Scientific Table Handling:

  • Research data tables
  • Statistical results
  • Multi-dimensional matrices
  • Chemical structures in tables

Output Formats:

  • Markdown tables (human-readable)
  • CSV (data analysis)
  • JSON (programmatic access)
  • Excel (XLSX export)

Handwriting Recognition

Handwritten Annotations:

  • Notes on printed documents
  • Form completions
  • Signatures with printed name matching
  • Margin comments

Quality Tiers:

  • Clean handwriting: 85-92% accuracy
  • Average handwriting: 75-85% accuracy
  • Poor handwriting: 60-75% accuracy (human review recommended)

Best Practices:

  • Combine with printed text context
  • Use confidence thresholds
  • Flag low-confidence for human verification

Horizontal Scaling Architecture

Worker Pool Management:

  • BullMQ job queues for reliability
  • Priority queuing (urgent vs. batch processing)
  • Automatic retry with exponential backoff
  • Dead letter queue for failed documents

Performance Metrics:

  • Queue depth monitoring
  • Worker utilization tracking
  • Processing time distribution
  • Cost per document analytics

Auto-Scaling Rules:

  • Scale up: Queue depth > 100 documents
  • Scale down: Queue depth < 20 documents
  • Max workers: 20 (configurable)
  • Cost optimization: Prefer Tier 1 during high load

Proven Accuracy Across Document Types

Contract Processing:

  • Multi-party agreements
  • Signature block extraction
  • Term identification (duration, renewal, termination)
  • Obligation mapping (party responsibilities)
  • Amendment tracking (version comparison)

Legal Brief Analysis:

  • Case citations with Bluebook formatting
  • Precedent relationships
  • Argument structure mapping
  • Exhibits and evidence linking

Regulatory Filings:

  • SEC 10-K/10-Q filings
  • Patent applications
  • Compliance reports
  • Court documents

Financial Documents: 97.9% Table Accuracy

Financial Statements:

  • Balance sheets (100+ line items)
  • Income statements (multi-year comparison)
  • Cash flow statements (operating/investing/financing)
  • Footnotes and disclosures

Invoices & Purchase Orders:

  • Line item extraction
  • Tax calculations
  • Vendor/customer information
  • Payment terms

Banking Documents:

  • Statements (transactions, balances)
  • Wire transfer forms
  • KYC/AML documentation
  • Credit reports

Medical Records: 98.4% Accuracy

Clinical Notes:

  • Doctor's handwritten notes (challenging)
  • Visit summaries
  • Diagnosis codes (ICD-10)
  • Prescription details

Lab Reports:

  • Test results with reference ranges
  • Multi-column data tables
  • Flagged abnormal values
  • Longitudinal tracking

Insurance Claims:

  • CPT/HCDS codes
  • Diagnosis linkage
  • Patient demographics
  • Provider information

HIPAA Compliance:

  • PHI redaction capabilities
  • Audit logging for access
  • Encryption at rest and in transit
  • Role-based access control

Research Papers: 99.6% Accuracy

Academic Publications:

  • Multi-column layouts (typical 2-column format)
  • Mathematical equations (LaTeX rendering)
  • Chemical structures
  • Complex diagrams with captions
  • Bibliography with citation links

Data Tables:

  • Statistical results (p-values, confidence intervals)
  • Multi-dimensional data
  • Correlation matrices
  • Experimental conditions

Key Benefits

For Operations Teams:

  • 1200+ documents/hour: Horizontal scaling with 1-20 workers
  • 99.2% layout accuracy: Dockling-level quality for critical documents
  • 97.9% table extraction: Industry-leading performance on financial tables
  • $0.04/document average: 85% cost reduction vs. traditional OCR

For Engineering Teams:

  • 22 API endpoints: Complete programmatic access (REST + GraphQL + WebSocket)
  • Multi-format support: PDF, DOCX, XLSX, images (PNG, JPG, TIFF)
  • Document DNA: Triple-layer storage (raw + semantic + graph)
  • BullMQ integration: Reliable job queues with auto-retry

For Enterprises:

  • 85% cost savings: $12K-18K annually vs. $85K-180K for traditional OCR
  • 3-tier intelligence: Right model for each document (Tesseract → GPT-4o → Claude Opus)
  • Production-proven: 8-step pipeline with validation at every stage
  • GraphRAG integration: Searchable knowledge across 100M+ vectors

Unfair Advantages:

  • Only platform combining 3-tier OCR cascade with automatic complexity routing
  • 99.2% accuracy matches Dockling while reducing costs 85%
  • 97.9% table extraction exceeds OpenAI, Anthropic, Google Document AI
  • 1200+ docs/hour per worker with horizontal scaling to 20+ workers
  • Document DNA provides semantic + graph relationships, not just text extraction

Get Started Today

Ready to achieve 99.2% document accuracy at $0.04 per document?

For Technical Evaluation: Explore our comprehensive documentation, review API reference with OCR cascade examples, or deploy a sandbox environment to test accuracy with your document types.

For Business Discussion: Request a demo to see FileProcessAgent process your actual documents, or contact sales to discuss high-volume deployments and calculate ROI vs. current OCR vendor.

For Self-Service: View pricing for transparent cost calculators based on document volume, or browse documentation for accuracy benchmarks across document types.

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