Line Item Extraction
Any format — PDFs, scans, tax returns, internally prepared. Tables, line items, subtotals identified with character-level precision. Multi-page, multi-year — all in one pass.
Use Cases
Financial Statement Spreading
MightyBot executes financial spreading from any format. Line items extracted, canonicalized, structured. No schema drift. No transposition errors. No manual mapping.
Why MightyBot
MightyBot executes financial statement spreading — extracting line items from any format, canonicalizing to a consistent schema through FRS, and producing structured output with evidence pointers to source. No schema drift. No transposition errors. 99%+ accuracy. The bottleneck in every credit decision, eliminated.
The Problem
Financial spreading is the foundation of credit analysis — and the bottleneck.
Until the spread is complete, no ratio calculated, no credit decision made.
Every firm presents statements differently. Manual spreading means an analyst maps
each line item and types each value. Transposition errors are common, mapping
inconsistent across analysts. At portfolio scale, these compound into unreliable
analytics.
Format chaos
Every firm, every borrower, every year looks different
Schema drift
Inconsistent categorization compounds across the portfolio
Manual data entry
Transposition errors on every spread
Multi-entity complexity
Consolidation, fiscal year changes, method changes
Downstream dependency
Everything in credit analysis waits on the spread
How MightyBot Executes
FRS Canonicalization
Where schema drift dies. The Field Resolution System maps items to the Canonical Field Library. "Cost of Goods Sold," "Cost of Revenue," and "Direct Costs" resolve to the same item. No analyst interpretation. Deterministic. Every time.
Structured Output with Evidence Pointers
Every value linked to source — page, table, cell. Analysts verify any figure with a click. Auditors trace ratios to source documents.
Multi-Period and Multi-Entity Normalization
Multiple years, entities, fiscal year changes — all normalized into consistent time-series. Comparable datasets ready for trend analysis.
"We automated what no one else could."
Schema drift is structural. FRS canonicalization is the structural solution.
95%
Time reduction in production Built Technologies — Production Deployment
Before vs After
The bottleneck in every credit decision. Eliminated.
Request a demoFAQ
Frequently Asked Questions
How does MightyBot handle non-standard line item names?
FRS maps thousands of variations to standardized categories. New variations are resolved using context - position in the statement, relationship to subtotals, and neighboring values. The library grows. Schema drift doesn't.
Can MightyBot spread tax returns?
Personal and business returns - 1040s, 1120s, 1120-S, and 1065s. Line items are mapped to canonical categories so tax and financial statement analysis can be combined for the same borrower.
What happens when extracted data doesn't balance?
Consistency is validated automatically - assets equal liabilities plus equity, and subtotals match their components. Discrepancies are flagged with evidence pointers so the analyst can resolve the source quickly.
Does MightyBot support different accounting bases?
GAAP, tax basis, and cash basis presentations are all supported. FRS maps them according to context and identifies basis from headers, footnotes, and statement structure.
How does spreading integrate with downstream credit analysis?
Spread output feeds directly into ratio calculations, covenant monitoring, and credit memos. When the spread changes, the downstream analysis updates with it. The spread is the foundation.
Can we customize the canonical schema?
Yes. The Canonical Field Library is the base layer, and your institution-specific categories can sit on top of it. Output can map into your current template and chart of accounts.