Page-by-page classification
Every page classified with confidence scores. Tax returns, bank statements, medical records identified automatically.
Use Cases
Document Processing
MightyBot's Document Intelligence Pipeline classifies, extracts, and canonicalizes data from any document -- PDFs, scans, photos. Evidence pointers to every source. Powers every use case.
Why MightyBot
MightyBot's Document Intelligence Pipeline executes the full document lifecycle for regulated industries. Pages classified. Data extracted from any format — PDFs, scans, photos, spreadsheets. Fields canonicalized to eliminate schema drift. Every value indexed with evidence pointers to source page and character offset. This powers every MightyBot use case.
The Problem
Basic OCR extracts text but misses context — it can't distinguish a borrower's income from a co-borrower's on the same return. Rule-based extraction breaks when new formats arrive. Template-matching requires manual configuration for every variation. Skilled professionals spend most of their time on information retrieval instead of analysis.
Format chaos
PDFs, scans, photos, spreadsheets from dozens of counterparties, all different.
Context required
Same field names mean different things across document types.
Schema drift
Same data point has different field names across sources.
Precision stakes
Every value must trace to source for regulatory audit.
Scale
Thousands of documents, hundreds of formats, zero tolerance for manual config.
How MightyBot Executes
Type-specific extraction
Each document processed with tailored logic for higher accuracy than generic extraction.
FRS canonicalization
Fields mapped to the Canonical Field Library. "Annual income," "gross salary," "total compensation" resolve to one field.
L0/L1/L2 indexing
Every value indexed at document, page, and entity level with character-level precision.
Before vs After
Production Metrics
Production deployments across lending, insurance, and payments. Same architecture. Same precision. Every workflow.
95%+ Accuracy on extraction across production workflows
70% Faster document processing cycle time
10x Throughput documents processed per analyst
Zero Schema drift with Canonical Field Library enforcement
Full Character-level evidence pointers to every source location
The document intelligence layer that was missing. Every use case starts here.
Request a demoFAQ
Frequently Asked Questions
What document formats does MightyBot process?
PDFs (native and scanned), images (JPEG, PNG, TIFF), mobile photos, spreadsheets (Excel, CSV), and multi-page mixed-format packages. Handles any image quality, orientation, or layout variation.
How does MightyBot handle documents it hasn't seen before?
Confidence scores. High-confidence classifications proceed automatically. Low-confidence flagged for review. New types added through configuration. No retraining. No code changes.
What is FRS canonicalization?
Maps extracted field names to the Canonical Field Library — a standardized schema. "Net income" vs. "bottom line" vs. "net profit" resolve to one field. Schema drift eliminated at the architecture level.
How do evidence pointers work?
Every value linked to its source: document, page number, bounding box coordinates, character offset. Any downstream system traces any data point to exactly where it appears in the original.
Does MightyBot replace our document management system?
No. Processes documents from your existing DMS, LOS, claims system, or storage. Extracted data flows back via APIs. The integration is the product.
How does MightyBot handle mixed documents in a single file?
Each page classified independently, then grouped into coherent documents. A loan package with interleaved tax returns, bank statements, and pay stubs? Automatically segmented and processed. No manual sorting.