The reality
Loan applications arrive as 200-page PDFs. Tax returns scanned at odd angles. Bank statements in dozens of formats.
THE PLATFORM
Data Engine
MightyBot's Document Intelligence Pipeline classifies, extracts, and indexes financial documents with 99%+ accuracy. Messy inputs are the default.
Why MightyBot
MightyBot's Data Engine turns messy, inconsistent documents into structured, policy-ready data with 99%+ extraction accuracy. The Document Intelligence Pipeline classifies pages, extracts values at character-level precision, canonicalizes to a standard financial reporting structure, and links every data point back to its exact source location. Messy inputs are the default.
The Hard Truth About Document Processing
The hardest workflows start with documents. Loan applications arrive as 200-page PDFs. Tax returns scanned at odd angles. Bank statements in dozens of formats.
The problem
Other AI platforms demo on structured inputs. The moment production documents arrive - 150 DPI scans, phone photos, inconsistent layouts - they fail.
Our answer
MightyBot's Document Intelligence Pipeline classifies, extracts, and indexes financial documents with 99%+ accuracy. Messy inputs are the default.
The Document Intelligence Pipeline
Four stages. Each purpose-built for production document messiness.
Page-by-Page Classification
Every page classified independently. A 200-page loan package segmented into tax returns, bank statements, pay stubs, appraisal reports.
Data Extraction
Each page processed by models tuned for its document type. Character-level boundary detection for dollar amounts, dates, percentages, names, and addresses.
FRS Canonicalization
Extracted values mapped to a canonical Financial Reporting Structure so downstream policy evaluation works consistently regardless of source format.
Evidence Pointers
Every extracted value maintains a traceable link to its source - the specific page, the specific location, the specific document.
L0 / L1 / L2 Indexing
Three-tier indexing. Each level serves a different purpose.
Document Level
Metadata: type, source, upload date, page count. "Show me all appraisal reports for this borrower." Answered instantly.
Page & Section Level
Structural segmentation: pages, sections, tables, schedules. Agents pull the income section of a tax return without reprocessing the full document.
Entity Level
Individual values: dollar amounts, dates, ratios, identifiers. Each links to its L1 section and L0 document via evidence pointers.
Megastore: Unified Search
Semantic search across all processed documents. A query for "borrower liquidity" returns bank balances, investment statements, and cash reserves. Search understands financial terminology.
See the Document Intelligence Pipeline on your documents.
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Frequently Asked Questions
What document formats does the Data Engine process?
PDFs (native and scanned), images (JPEG, PNG, TIFF), phone photos, Office documents, spreadsheets, and multi-page forms - regardless of scan quality, rotation, or formatting. Messy inputs are the default.
How accurate is data extraction?
99%+ in production. Document-type-specific models and character-level boundary detection maintain precision even on low-quality scans. A production number, not a benchmark.
What is FRS canonicalization?
It maps extracted values from different formats to a standardized schema. "Net Operating Income" and "NOI" resolve to the same canonical field, so evaluation stays consistent regardless of source format.
How do evidence pointers work?
Every extracted value links to its exact source - page number, coordinates, and document in the original upload. Auditors can click through from any decision to the source data.
How does Megastore prevent data cross-contamination?
Per-workflow repositories scope access at the architectural level. Each loan file has its own repository. Not a permission setting. An architectural guarantee.
Does the pipeline handle documents in multiple languages?
It is optimized for English-language financial documents today. Additional language support is available for specific document types and deployment needs.