How eDocify uses AI

eDocify is an AI-powered document operations platform built by Bitlogika. The product helps finance, accounting, verification, and compliance teams receive business documents, extract structured data, verify critical fields, route approvals, export to ERP systems, and preserve an audit-ready archive.

AI is not an add-on feature. It is part of the core document workflow: document understanding, extraction, confidence scoring, verification prioritization, provider benchmarking, and learning from human corrections.

The AI layer is intentionally multi-provider and hybrid. eDocify is not locked to a single external model or OCR service: it can combine Azure Document Intelligence, Mistral OCR, local Tesseract/RapidOCR/PaddleOCR routes, deterministic extraction rules, human verification feedback, Bitlogika's own AI Learning engine, and .NET-based machine learning components for ranking, validation, and continuous improvement.

Bitlogika is also building a proprietary AI Learning engine inside eDocify. The purpose of this engine is to turn verified human corrections into reusable product knowledge: improved extraction rules, better candidate ranking, tenant-specific field behavior, OCR provider comparison, confidence calibration, and future routing decisions.

This allows different processing combinations depending on customer needs:

• cloud-first route for speed and broad document understanding;
• local-first route for sensitive or cost-controlled processing;
• hybrid cloud + local OCR route for comparison and fallback;
• local OCR + local LLM route where a PDF/JPG invoice is processed by RapidOCR or PaddleOCR, converted to OCR text, structured by an Ollama-hosted Qwen3 model, and validated against a strict JSON schema;
• rules + AI route for accounting fields that need deterministic validation;
• AI + human verification route for high-risk fields;
• correction learning route where verifier changes are captured by the eDocify AI Learning engine and improve future extraction;
• provider benchmark route where the same document is tested against several OCR/AI engines.

AI capabilities

Capability	Product role	Current status
Document classification	Detect invoice, receipt, contract, archive record, and other supported document families.	Implemented / expanding
OCR provider orchestration	Route documents through Azure Document Intelligence, Mistral OCR, local OCR, rules, or hybrid pipelines.	Implemented
Mistral OCR route	Use Mistral OCR as an additional AI/OCR provider for document extraction experiments and provider comparison.	Implemented / expanding
Local OCR routes	Use local Tesseract, RapidOCR, and PaddleOCR-style processing for sensitive documents, offline experiments, and cost-controlled benchmark scenarios.	Implemented / expanding
Local OCR + local LLM JSON route	Test a local extraction path: PDF/JPG invoice -> RapidOCR or PaddleOCR -> OCR text -> Ollama qwen3:8b or qwen3:30b -> strict JSON schema.	Planned R&D
Hybrid extraction pipelines	Combine cloud OCR, local OCR, deterministic rules, AI validation, and human correction feedback in different routes per tenant or document type.	Implemented / expanding
Proprietary AI Learning engine	Capture verifier corrections and convert them into reusable learning signals for extraction rules, ranking, confidence calibration, provider routing, and tenant-specific behavior.	Implemented / expanding
.NET machine learning layer	Use .NET-based ranking and learning components to compare candidates, learn from verifier corrections, and support provider selection.	In development
Invoice field extraction	Extract supplier, buyer, invoice number, dates, totals, VAT, currency, and payment fields.	Implemented
Line item extraction	Extract invoice rows, quantities, prices, VAT categories, and accounting-relevant values.	Implemented / improving
Supplier and customer matching	Match extracted fields against client reference lists and accounting profiles.	Implemented / expanding
Confidence scoring	Prioritize documents and fields that need human review.	In development
Human verification guidance	Show verifier teams the fields, pages, and regions that require attention.	Implemented
Region OCR	Re-run OCR against selected document zones when a specific field needs correction.	Implemented
Anomaly detection	Detect suspicious totals, duplicate values, missing fields, and export blockers.	In development
Learning from corrections	Capture verifier corrections and use them to improve field rules, provider routing, and future extraction quality.	In development
Provider benchmarking	Compare OCR/AI providers on field-level accuracy, speed, and cost.	In development
Release gates	Promote OCR/AI changes only when they pass a golden dataset and quality thresholds.	Planned R&D / partially implemented

Why this matters

Traditional OCR tools focus on text extraction. eDocify focuses on the full document operation:

flowchart LR
  A["Document intake"] --> B["OCR / AI extraction"]
  B --> C["Confidence and validation"]
  C --> D["Human verification"]
  D --> E["Approval workflow"]
  E --> F["ERP export"]
  F --> G["Audit-ready archive"]
  D --> H["Correction learning"]
  H --> B

The result is not only a recognized document. The result is a controlled business process with measurable quality, human oversight, ERP integration, and audit evidence.

Microsoft AI alignment

Microsoft for Startups highlights access to AI services, Azure credits, technical resources, and enterprise-grade security as startup benefits. eDocify is a strong fit because it can use Azure services across the actual product workflow:

• Azure Document Intelligence for invoice, receipt, layout, and document extraction.
• Mistral OCR for additional document extraction and provider benchmarking.
• Azure OpenAI or compatible AI providers for interpretation, validation, and verifier assistance.
• Local OCR engines such as Tesseract, RapidOCR, and PaddleOCR for private, offline, or cost-sensitive processing routes.
• Ollama-hosted Qwen3 experiments for local OCR text-to-JSON structuring.
• .NET machine learning components for candidate ranking, correction learning, and extraction quality improvement.
• Azure Storage for documents, OCR outputs, and archive objects.
• Azure Service Bus or equivalent queues for asynchronous OCR and export jobs.
• Azure monitoring and logs for reliability, SLA, and operational insight.
• Key Vault for API keys, ERP credentials, and AI provider secrets.

Reference: Microsoft for Startups and Azure Document Intelligence.

Product evidence

eDocify already contains product surfaces and engineering modules that show AI is part of the product, not a marketing claim:

• OCR / AI pipeline and provider routing.
• Mistral OCR integration.
• Local OCR routes through Tesseract, RapidOCR, and PaddleOCR-style processing.
• Experimental local JSON extraction route using RapidOCR/PaddleOCR plus Ollama qwen3:8b or qwen3:30b.
• Hybrid processing routes that combine cloud OCR, local OCR, rules, AI validation, and human verification.
• Proprietary AI Learning engine that learns from verifier corrections and feeds future extraction, ranking, and quality decisions.
• .NET machine learning and ranking components for OCR candidate evaluation.
• Verification Workbench with field-level review.
• Region OCR and document preview tools.
• AI Learning and Accuracy Studio.
• Quality Engine and field-level metrics.
• Golden dataset and provider benchmark concepts.
• ERP export validation and repair-oriented workflows.

Near-term AI roadmap

Milestone	Outcome
Pilot golden dataset	500-1,000 real invoices with verified ground truth.
Critical field SLA	Field-level quality reporting for supplier, invoice number, dates, totals, VAT, and currency.
Provider bake-off	Compare Azure Document Intelligence, Mistral OCR, local OCR, and hybrid extraction by accuracy, cost, and speed.
Correction learning loop	Use human corrections to improve extraction rules and provider selection.
Release governance	Block OCR/AI changes that fail field-level quality gates.

Funding-ready message

eDocify is not a consulting project. It is a proprietary AI SaaS product for document operations. Cloud credits and startup support would be used to run AI extraction, document storage, queue processing, monitoring, secure pilot environments, and quality benchmarking for real customer pilots.