The reality
Highly regulated industries deal with large, complex document packages. Tax returns scanned at odd angles. Bank statements in dozens of formats.
PLATFORM
AI Document Intelligence for Regulated Workflows
Messy documents become decision-ready data: classified, extracted at character
precision, and linked to evidence your auditors can follow.
What is an AI Document Intelligence Platform?
An AI document intelligence platform turns unstructured documents into data that downstream decisions can rely on. MightyBot's Data Engine classifies pages, extracts values at character-level precision, canonicalizes them into policy-ready structures, and attaches an evidence pointer linking every value back to its exact source location.
The Hard Truth About AI Document Processing
The hardest workflows start with documents. Document processing and financial spreading workflows can begin with 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 AI Document Intelligence Pipeline classifies, extracts, canonicalizes, and indexes documents with 99%+ accuracy. Production-ready from day one.
How MightyBot's AI Document Intelligence Pipeline Works
Four stages. Each purpose-built for production document messiness.
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Classify pages and document types
Every page classified independently. A 200-page loan package segmented into tax returns, bank statements, pay stubs, appraisal reports.
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Extract values at character-level precision
Each page processed by models tuned for its document type. Character-level boundary detection for dollar amounts, dates, percentages, names, and addresses.
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Canonicalize into policy-ready structures
Extracted values mapped to a canonical Financial Reporting Structure so downstream policy evaluation works consistently regardless of source format.
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Index for retrieval (L0/L1/L2)
Documents, pages, sections, and entities are indexed for retrieval across the L0, L1, and L2 levels.
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Attach evidence pointers to source coordinates
Every extracted value maintains a traceable link to its source: the specific page, the specific location, the specific document.
Supported input types
- PDFs (native and scanned)
- Images (JPEG, PNG, TIFF)
- Phone photos
- Office documents
- Spreadsheets
- Multi-page forms
- Video
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 for Processed Documents
MightyBot's Data Engine turns messy documents into policy-ready data with classification, extraction, canonicalization, Megastore search, and source evidence pointers.
Semantic search across all processed documents. A query for "borrower liquidity" returns bank balances, investment statements, and cash reserves. Search understands financial terminology.
Evidence Pointers
Example: a spread shows Net Operating Income of $1,284,000 for FY2025. The evidence pointer stores the source document, page, and the coordinates of the line it came from, so a reviewer clicks the number and sees the exact statement line that produced it. Disagreements get resolved at the source in seconds, and the trail survives into the audit record.
Beyond Documents: Photos and Video as Evidence
Regulated work does not stop at PDFs. The same pipeline applies: ingest, detect, extract, verify.
Photo Evidence
Inspection photos analyzed alongside the written report. The platform verifies the image shows what the report claims and flags what is missing.
Video Evidence
Claims footage and site video processed as first-class inputs, linked into the same decision trail as every document.
Visual-to-Text Alignment
When the picture and the paperwork disagree, the discrepancy is detected and escalated with both sources.
Cross-Document Reconciliation
When two documents disagree about the same fact, the conflict is detected and escalated with both values and both sources.
See AI document intelligence on your documents.
Request a demoExplore the stack
The rest of the Decision Execution Stack.
Agent CompilerFrom plain English to working agent. No canvas, no flowcharts: describe the work and the platform compiles the execution plan.ControlPolicy EnginePlain-English policies compiled into deterministic execution paths.RuntimeCompiled Agent ExecutionParallel execution plans for regulated workflows instead of sequential prompt chains.SystemsEnterprise IntegrationsConnect systems of record to systems of action without rip-and-replace.AuditCompliance InfrastructureWhy-trails, review gates, evidence records, and examiner-ready exports.SecuritySecure AI Agent PlatformSOC 2 Type II controls, tenant isolation, encryption, and access governance.Measured evidenceAgent Evaluation Cost StudyMeasured token costs of single-pass, agentic, and voting configurations on the same workload.
FAQ
Frequently Asked Questions
What is an AI document intelligence platform?
Software that reads business documents (statements, applications, contracts, forms) and produces structured, verified data: classification, extraction, normalization, and evidence linking, so downstream systems and policies can act on the result without re-keying or blind trust.
What is an evidence pointer in document extraction?
A stored reference from every extracted value back to its exact source location: document, page, and position. It is what lets a reviewer or examiner verify a number in one click instead of hunting through a 300-page file.
What document formats does MightyBot's AI Document Intelligence 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 canonicalization in AI document intelligence?
Canonicalization maps extracted values from different formats to a standardized schema. "Net Operating Income" and "NOI" resolve to the same canonical field, so policy 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.
Can MightyBot use photos and video as evidence?
Yes. Images and video are first-class inputs processed with the same pattern as documents: ingest, detect, extract, verify. The platform checks that a photo shows what the accompanying report claims and links visual evidence into the same decision trail.
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.
Last updated: August 6, 2026