MightyBot Transforms Lending with Industry's First Autonomous Agentic AI.
February 25, 2026
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AI Thinking
AI document processing for construction lending goes far beyond OCR. Construction draw packages are multi-document packets — 15 to 30 pages spanning lien waivers, inspection reports, AIA payment applications, insurance certificates, and change orders — each requiring classification, type-specific extraction, cross-document reconciliation, and policy evaluation. Generic document processing tools fail because they were built for single-page invoices, not the complexity of construction finance.
Construction lending generates some of the most complex document workflows in financial services. A single construction loan can produce dozens of draw requests over its lifetime, each accompanied by a packet of supporting documents from multiple parties — general contractors, subcontractors, inspectors, insurance providers, and title companies. Each document type has its own format, legal requirements, and validation logic.
The document processing challenge is not reading text from images. Modern OCR handles that adequately. The challenge is understanding what the text means in context, extracting structured data from variable formats, normalizing entities across documents, and validating the extracted data against lending policies — all with evidence trails that auditors can verify.
Intelligent Document Processing (IDP) platforms have matured significantly for common business documents — invoices, receipts, purchase orders. These platforms work well when documents follow standardized templates with predictable field locations.
Construction lending documents break every assumption IDP platforms make:
Multi-document packets, not single pages. A draw request is not a single document — it is a packet of 5-8 different document types bundled into one PDF. The system must classify each page independently before it can extract anything.
Variable formats with no standard template. A lien waiver from a plumbing subcontractor in Texas looks different from one in California. An inspection report from a third-party inspector uses a different format than the lender's internal inspection form. AIA forms come in multiple versions (G702, G703, G702/G703 combined). Generic IDP cannot handle this variation.
Cross-document relationships. The lien waiver amount must match the corresponding AIA line item. The insurance certificate must cover the contractor listed on the draw. The inspection report date must fall within the lender's required window. These cross-document validations require understanding relationships between documents, not just reading individual pages.
Legal and regulatory specificity. Lien waiver requirements vary by state. Insurance coverage thresholds vary by lender. Retainage calculations follow contractual formulas. Construction lending document processing requires domain-specific knowledge, not general-purpose extraction.
MightyBot's document intelligence pipeline was purpose-built for the complexity of construction lending. Each stage transforms raw documents into structured, evidence-linked data ready for policy evaluation.
Every page in a draw package is classified independently with confidence scores. A 25-page PDF might contain:
The classifier handles mixed-format documents, rotated pages, poor scan quality, and blank separator pages. When confidence is low, the page is flagged for human review rather than misclassified.
Each classified page routes to extraction logic tailored to its document type. This is where the pipeline diverges fundamentally from generic IDP — each document type has its own extraction schema and validation rules.
AIA G702/G703 forms: Extract the mathematical hierarchy — original contract value, approved change orders, work completed to date, stored materials, retainage, previous certificates for payment, and current payment due. Validate the mathematical relationships between these fields (they must balance).
Lien waivers: Identify the waiver type (conditional vs. unconditional — a critical legal distinction), the claimant name, the amount, the through-date, and the project reference. Flag if the waiver type does not match the payment stage (unconditional waivers should not appear for current-period payments).
Inspection reports: Extract the inspector name, inspection date, percentage-complete assessments by trade or building component, noted deficiencies, and recommendations. Handle both structured forms and narrative-style reports.
Insurance certificates: Extract policy numbers, coverage types (general liability, workers' compensation, professional liability), coverage limits, effective and expiration dates, and additional insured endorsements. Match against the contractor and project to verify applicability.
Construction documents reference the same entities with inconsistent names. The normalization layer resolves these to canonical records using MightyBot's Canonical Field Library:
Every normalization links back to its source: the specific page, paragraph, and character boundaries where the original text appears. This evidence linking is what enables auditors to verify the system's interpretation of ambiguous data.
With structured, normalized data from all documents, the pipeline performs cross-document validation:
Each reconciliation check produces a result (pass, fail, discrepancy) with evidence pointers linking the finding to the specific data in specific documents. A reviewer can click through from "lien waiver amount discrepancy for Metro Plumbing" to the exact numbers on the exact pages of both documents.
The pipeline's final output is a complete, structured record at three levels of granularity — the structure MightyBot calls L0/L1/L2:
L0 (document level): Full document metadata, raw text, classification, and processing status for each document in the draw package.
L1 (page/section level): Page-level data including extracted text, hierarchical structure, classification confidence, and evidence pointers linking extracted values to specific character positions.
L2 (entity level): Normalized entities (contractors, amounts, dates, coverage details) that span across documents, with evidence links to every source reference. This is where cross-document reconciliation results live.
This three-level structure makes the data immediately searchable and queryable for policy evaluation, human review, and compliance reporting.
Construction lending increasingly incorporates visual evidence alongside traditional documents. Site inspection photos, progress videos, and drone imagery provide additional data points for draw validation.
MightyBot's pipeline extends to visual evidence processing: site photos can be analyzed for construction progress, compared against draw completion percentages, and linked to inspection reports. This capability is emerging but represents the natural extension of document intelligence into multi-modal evidence processing.
The Built Technologies deployment demonstrates what purpose-built document processing delivers compared to generic alternatives:
| Metric | Generic IDP | MightyBot Pipeline |
|---|---|---|
| Document types handled | Single-template documents | Multi-document packets (15-30 pages, 5-8 types) |
| Cross-document validation | Not available | Full reconciliation with evidence links |
| Field normalization | Basic name matching | Canonical Field Library with entity resolution |
| Evidence trails | None or minimal | Page + character-level source linking |
| Policy evaluation | Not available | Deterministic pass/fail with audit trail |
| Accuracy in production | Variable | 99%+ |
Why does generic document processing fail for construction lending?
Construction draw packages are multi-document packets with 5-8 document types in variable formats, not single-page standardized forms. Processing requires page-by-page classification, type-specific extraction, cross-document reconciliation, and domain-specific policy evaluation — capabilities generic IDP platforms were not designed for.
What documents are in a construction lending draw package?
A typical draw package includes AIA G702/G703 payment applications, conditional and unconditional lien waivers from subcontractors, third-party inspection reports, insurance certificates, change order documentation, and sometimes title updates and permit records. A single draw can span 15-30 pages across 5-8 document types.
What accuracy does AI achieve on construction lending documents?
MightyBot's document intelligence pipeline achieves 99%+ accuracy in production across thousands of construction loan draw requests processed for Built Technologies. This exceeds human baseline accuracy, and the system detects 400% more risk issues because it checks every policy against every document without fatigue.
How does AI handle poor quality construction documents?
The pipeline assigns confidence scores at every stage — classification, extraction, and reconciliation. When document quality is poor (bad scans, rotated pages, handwriting), confidence scores drop and the system routes to human review rather than making low-confidence decisions. This ensures accuracy is maintained even with variable input quality.
