Project Overview

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Vision

Develop an on-premise AI system that automates document processing for Swiss financial institutions while ensuring data sovereignty. The solution combines advanced OCR with large language models to transform manual document handling into automated workflows.

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Problem Statement

Current State

Swiss financial institutions process thousands of compliance documents annually:

Document Type	Typical Length	Current Processing
KYC Forms	20-50 pages	3-5 days manual
Regulatory Filings	50-100 pages	1-2 weeks manual
Compliance Questionnaires	30-80 pages	1 week manual
Annual Reports	100+ pages	2-3 weeks manual

Key Challenges

1. Accuracy Degradation
   • Current AI: 95%+ on single-page forms
   • Reality: 70-80% on multi-page documents
   • Gap: 15-25% accuracy loss on extended documents
2. Processing Time
   • Manual review: 2-3 weeks per 100-page document
   • Staff cost: High-skilled compliance officers
   • Bottleneck: Peak regulatory periods (quarterly, annually)
3. Data Sovereignty
   • Cloud AI prohibited for sensitive financial data
   • FINMA regulations require Swiss data residency
   • FADP/GDPR compliance mandatory
   • On-premise deployment essential
4. Schema Variability
   • 50+ different CRM systems across Swiss institutions
   • No standard document templates
   • Manual field mapping required for each client

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Solution Architecture

Three Integrated Capabilities

+------------------+     +------------------+     +------------------+
|   DOCUMENT       |     |   DYNAMIC        |     |   INTELLIGENT    |
|   DIGITIZATION   | --> |   FIELD MAPPING  | --> |   PRE-FILLING    |
+------------------+     +------------------+     +------------------+
| - OCR extraction |     | - Zero-shot      |     | - PDF population |
| - LLM processing |     |   schema mapping |     | - Excel filling  |
| - 50-100 pages   |     | - CRM integration|     | - Variable forms |
| - Scanned docs   |     | - F1 > 85%       |     | - Checkboxes     |
+------------------+     +------------------+     +------------------+

Component Details

1. Document Digitization (WP2 + WP3)

Feature	Description
OCR Engine	PyMuPDF + Tesseract + PaddleOCR ensemble
LLM Processing	7-13B parameter domain-adapted models
Document Types	PDF, scanned images, mixed layouts
Languages	German, French, Italian, English
Page Handling	Hierarchical attention for 50-100 pages

2. Dynamic Field Mapping (WP4)

Feature	Description
Schema Discovery	Automatic detection of target fields
Zero-Shot Mapping	No manual template configuration
CRM Integration	Direct API connection to client systems
Validation	Confidence scoring per field
Learning	Improves from corrections

3. Intelligent Pre-Filling (WP5)

Feature	Description
Output Formats	PDF, Excel, Word
Field Types	Text, checkboxes, dropdowns, tables
Variable Layouts	Adapts to document structure
Review Interface	Human-in-the-loop verification
Export	Direct to CRM or file download

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Technology Stack

AI/ML Components

Component	Technology	Purpose
Base Models	Llama 3, Mistral, Qwen	Foundation models (7-13B)
Fine-tuning	LoRA, QLoRA adapters	Domain adaptation
OCR	Tesseract, PaddleOCR, Docling	Text extraction
Embeddings	Sentence-BERT, E5	Semantic matching
Orchestration	LangChain, LlamaIndex	Pipeline management

Infrastructure

Component	Technology	Purpose
Compute	NVIDIA A100/RTX 4090	On-premise inference
Storage	Local NVMe + encrypted backup	Document storage
API	FastAPI, REST	Integration layer
Monitoring	Prometheus, Grafana	Performance tracking
Security	TLS 1.3, AES-256	Data protection

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Innovation Aspects

Scientific Innovation

1. Hierarchical Attention for Long Documents
   • Novel architecture for 50-100 page processing
   • Maintains context across document sections
   • Reduces information loss from chunking
2. Domain-Specific Hallucination Detection
   • Compliance-aware fact checking
   • Cross-reference validation against source
   • 40% reduction target vs. baseline
3. Zero-Shot Schema Mapping
   • Transfer learning across CRM systems
   • No per-schema training required
   • F1 > 85% without manual configuration

Technological Innovation

1. On-Premise LLM Deployment
   • 7-13B models on consumer/enterprise hardware
   • 60-70% cost savings vs. cloud
   • Full data sovereignty maintained
2. Multi-Format Document Handling
   • Scanned PDFs, native PDFs, images
   • Mixed layouts and languages
   • Table and form extraction

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Expected Impact

Economic Impact

Metric	Current	Target	Improvement
Processing time	2-3 weeks	1-2 hours	99% reduction
Error rate	20-30%	<10%	66% reduction
Staff efficiency	1 doc/week	10+ docs/day	50x increase
Cost per document	CHF 500-1000	CHF 50-100	90% reduction

Market Opportunity

Year	Clients	Revenue	Notes
Year 1	10-15	CHF 0.5M	Design partners
Year 2	15-25	CHF 0.8M	Beta customers
Year 3	40-50	CHF 1.5M	Market entry
Year 4	50-60	CHF 2M	Scale-up
Year 5	80-90	CHF 3M	Market leadership

Swiss Economic Benefit

• 15 Swiss-based FTE positions by Year 5
• CHF 11-22M annual cost savings for Swiss financial institutions
• Strengthened Swiss technology sovereignty
• Exportable to EU markets with similar regulations

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Risk Mitigation

Risk	Likelihood	Impact	Mitigation
Model accuracy insufficient	Medium	High	Ensemble methods, human review
Document quality issues	Medium	Medium	Quality filtering, preprocessing
Integration complexity	Low	Medium	API-first design, staging
Regulatory changes	Low	High	Modular architecture, rapid adaptation
Resource constraints	Low	Medium	Efficient fine-tuning (LoRA)

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Success Criteria

The project will be considered successful if:

1. Technical Goals Met
   • 90% accuracy on 50-100 page documents
   • < 2 hours processing time per document
   • TRL 7 achieved
2. Business Goals Met
   • 3-5 Swiss institution deployments
   • Positive ROI demonstrated
   • Commercial foundation established
3. Scientific Goals Met
   • Peer-reviewed publications
   • Open benchmark dataset released
   • Reproducible methodology documented

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