Specialized Models

Deploy document-specific intelligence with a mix of pretrained processors, trainable custom processors, and workflow-level validation.

What This Means in M3 Forge

M3 Forge does not force every use case through one generic model. It gives you a model stack that can be specialized by task, layout, and document type:

Prebuilt processors for common document classes
Custom extractors for field-level data capture
Custom classifiers for routing and categorization
Custom splitters for multi-document packet handling
Custom layout models for vendor or template variation
Summarizers for downstream review and decision support

This lets teams choose the right model surface for the document they actually operate on.

Where Specialization Pays Off

Problem	Specialized model approach
Same business field, many layouts	Use layout + extractor models tuned to vendor variation
Large packet with mixed document types	Split, classify, then route to dedicated extractors
Domain-specific forms	Train a custom processor with your schema and examples
Short review turnaround	Add summarization and confidence-based review routing

Typical Processor Stack

Example: Accounts Payable

A finance team can build a specialized invoice pipeline like this:

Classifier distinguishes invoices, receipts, and supporting correspondence.
Layout model identifies supplier-specific invoice variations.
Extractor captures invoice number, service date, line items, tax, and total.
Guardrail checks schema validity and policy constraints.
HITL resolves only low-confidence totals or missing tax fields.

This is materially different from sending every PDF to a single prompt and hoping the output is stable.

Example Schema Definition


{
  "fields": [
    { "name": "invoice_number", "type": "PLAIN_TEXT", "occurrence": "REQUIRED_ONCE" },
    { "name": "invoice_date", "type": "DATETIME", "occurrence": "REQUIRED_ONCE" },
    { "name": "vendor_name", "type": "PLAIN_TEXT", "occurrence": "REQUIRED_ONCE" },
    { "name": "line_items", "type": "PLAIN_TEXT", "occurrence": "OPTIONAL_MULTIPLE" },
    { "name": "total_amount", "type": "CURRENCY", "occurrence": "REQUIRED_ONCE" }
  ]
}

Why This Closes the “Generic Model” Gap

Specialized models matter when:

the same field appears differently across layouts
document quality ranges from clean PDFs to handwriting and scans
operations care about repeatable accuracy, not a nice one-off demo
model improvements need to come from labeled business feedback

M3 Forge supports a practical progression: start with foundation and zero-shot extraction, then train a specialized processor only when the workflow economics justify it.