Use Cases

Intelligent 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.

What is Intelligent Document Processing?

AI document processing for regulated workflows goes past OCR: documents are classified, values extracted at character-level precision, normalized into your schemas, and linked back to their exact source location. Downstream decisions get data they can rely on, with evidence attached.

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

Large document volumes, many formats, zero tolerance for manual config.

How MightyBot Executes

  1. Page-by-page classification

    Every page classified with confidence scores. Tax returns, bank statements, medical records identified automatically.

  2. Type-specific extraction

    Each document processed with tailored logic for higher accuracy than generic extraction.

  3. FRS canonicalization

    Fields mapped to the Canonical Field Library. "Annual income," "gross salary," "total compensation" resolve to one field.

  4. L0/L1/L2 indexing

    Every value indexed at document, page, and entity level with character-level precision.

Before vs After

After Before

Production Metrics

Measured in MightyBot production deployments across lending, insurance, and payments. Same architecture. Same precision. Every workflow.

99%+ production lending deployment: Accuracy on extraction across production workflows
70% Faster document processing cycle time
4-5x More 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.

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Use-case map

How Intelligent Document Processing works in MightyBot

MightyBot provides intelligent document processing for regulated industries: classification, extraction, canonicalization, and evidence pointers across PDFs, scans, photos, and spreadsheets.

Inputs PDFs, scans, phone photos, spreadsheets, forms, statements, tax returns, medical records, and mixed document packets.
Execution Classifies pages, extracts type-specific fields, canonicalizes values into consistent schemas, indexes evidence, and routes low-confidence exceptions.
Outputs Structured data, canonical fields, source evidence pointers, confidence signals, document indexes, and downstream workflow inputs.
Audit trail Every extracted value links to document, page, coordinates or character position, confidence, and workflow context.
Best for Teams where manual document handling is the bottleneck before credit, claims, compliance, payments, or servicing decisions.

FAQ

Frequently Asked Questions

What are document processing AI agents?

Agents that handle the full document lifecycle inside a workflow: classify what arrived, extract and normalize the values, validate against expectations, and hand structured, evidence-linked data to the next step. They differ from OCR tools by owning the outcome, including exceptions.

How is document processing different in regulated workflows?

The output has to survive an audit. That means evidence pointers on every value, validation against policy rather than heuristics, and human review where confidence or stakes require it. Speed matters; provability decides.

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.