OCR reads text, but misses context.
It can capture characters on a page, but it often misses the relationship between a label, a value, a table, a checkbox, and the surrounding business meaning.
Nashville extracts fields, tables, entities, handwriting, checkboxes, clauses, codes, and relationships from complex documents — so teams can move from manual review to JSON-ready outputs without brittle OCR templates.
Nashville is the extraction layer of OmniSuite™. Once Chicago has classified and separated the document stream, Nashville reads the content and pulls out the facts the business needs. That structured data can then feed Orion for grounded reasoning, Polaris for workflow automation, or any host system that needs clean, usable document data.
Nashville is the extraction layer. It sits between document classification and enterprise reasoning — the step that converts whatever Chicago hands over into structured data the rest of the platform (and your systems) can actually use.
Most document extraction systems were built for stable forms and predictable layouts. They read text with OCR, match fields against templates, and rely on rules that work until the format changes. Real enterprise documents do not behave that way.
Labels move. Tables change. Fields appear in different places. Scans are noisy. Handwriting shows up. A single packet may contain forms, notes, attachments, correspondence, and supporting evidence. When the system cannot understand the document, humans become the fallback.
It can capture characters on a page, but it often misses the relationship between a label, a value, a table, a checkbox, and the surrounding business meaning.
A new vendor form, revised claim packet, different government application, or slightly shifted table can create downstream extraction errors.
People end up correcting fields, checking tables, validating values, and rekeying data into downstream systems.
Nashville treats each document as a visual and semantic object, not a flat text file. It looks at the words, the layout, the surrounding labels, the field positions, the tables, the handwriting, and the structure of the page together. That makes extraction more resilient when formats vary and more useful when the document carries meaning through layout.
Detect fields, labels, tables, regions, checkboxes, signatures, and visual relationships.
Extract the facts that matter in context — names, dates, amounts, codes, policy numbers, clauses, entities, and line items.
Handle structured, semi-structured, and unstructured documents without rebuilding templates for every new format.
Output structured data that can feed case systems, workflow tools, analytics, RAG pipelines, and downstream automation.
Nashville manages the full extraction lifecycle: understand the page, map the structure, extract the facts, improve the model, and return data your systems can use.
Nashville evaluates the document visually and semantically to understand what it is looking at, how the page is organized, and which content relationships matter.
OmniSense™ identifies labels, values, tables, checkboxes, regions, and document structure so the system can extract data from the right places.
OmniParse™ converts the discovered structure into extraction instructions that can be used by Nashville’s domain-specific models.
Nashville uses the right model type for the document and use case — semantic, entity-based, visual, or high-speed visual extraction.
OmniTrain™ uses reviewed results, active learning, reinforcement learning, and preference feedback to improve model performance over time.
Outputs are returned as structured data for host systems, APIs, analytics, RAG, or downstream workflows.
Not every document needs the same extraction strategy. Nashville supports multiple model types so teams can start fast, handle different document formats, and scale to production without changing platforms.
Simple to moderate forms, semi-structured and unstructured content where speed and setup time matter.
Contracts, narratives, correspondence, regulatory documents — where meaning matters more than position.
Repeatable structured forms where layout, tables, and visual structure carry important meaning.
High-volume, cost-sensitive, edge, or near-edge environments that need fast visual understanding.
Many extraction projects stall because the demo model and the production model are not the same path. Nashville is designed so early extraction work becomes the foundation for production-grade automation.
Use lightweight models to begin extracting from real documents quickly, without months of labeling.
Capture fields, values, geometry, confidence scores, and extraction outputs from real business content.
Use guided curation to validate outputs and turn human review into better training data.
Use curated results to train higher-fidelity VLMs or MVLMs for production workloads.
Move from early extraction to production automation without re-platforming or rebuilding the workflow.
“The first model gets you moving. The reviewed outputs make the next model better.”
Processes text, visual structure, labels, tables, fields, handwriting, and layout together.
Understands where information sits on the page and how fields relate to nearby labels and regions.
Captures structured rows, columns, values, codes, quantities, and financial or clinical details.
Handles poor-quality scans, handwritten fields, stylized forms, and inconsistent document quality.
Lets teams review, correct, and improve extraction workflows without requiring a full data science team.
Returns JSON-ready data for enterprise systems, partner applications, RAG, reporting, and workflow automation.
Every new format means a new template, a new rule, or a new round of labeling before extraction can start.
People read, rekey, check, and correct document data because the system can’t be trusted to do it alone.
A vendor revises a form, a scanner shifts a margin — downstream extractions break and rework piles up.
Case systems, workflow tools, and analytics get half-structured exports that still need human cleanup.
Production use doesn’t make the system any smarter — corrections live in spreadsheets, not models.
The PoC model and the production model are two different rebuilds with different timelines and different teams.
Start extracting from new document types without building large template libraries or writing brittle rules.
Reduce the human effort spent reading, rekeying, checking, and correcting document data.
Handle layout variation, new document versions, vendor-specific formats, and inconsistent scans with less rework.
Turn unstructured and semi-structured documents into JSON-ready data downstream applications can use directly.
Reviewed results feed the model improvement loop — production use becomes a source of better training data.
Move from early demos to high-volume production workloads inside the same Nashville environment.
Extract claim numbers, policy details, loss descriptions, adjuster notes, line items, supporting evidence, and coverage-related facts.
Capture patient details, service codes, lab values, clinical evidence, plan criteria, and treatment documentation.
Extract borrower information, income data, loan values, KYC fields, underwriting facts, and supporting financial evidence.
Read structured forms, eligibility documents, permits, public-sector intake packets, and compliance evidence.
Extract line items, amounts, dates, vendor details, account numbers, tax data, and payment information.
Identify parties, dates, obligations, clauses, renewal terms, exceptions, and key entity relationships.
Nashville sits between document classification and enterprise reasoning. It gives downstream systems the structured data they need to answer questions, validate decisions, and trigger action.
Classifies and separates mixed document streams.
Extracts fields, tables, entities, handwriting, clauses, and relationships.
Reasons over structured data and retrieved enterprise content.
Routes, escalates, validates, and triggers workflow actions.
Chicago identifies the document. Nashville extracts the facts. Orion reasons from them. Polaris moves the work forward.
No. Nashville is a multi-modal document understanding layer. It can use text, but it also interprets layout, tables, labels, checkboxes, handwriting, and visual structure.
Forms, claims, applications, medical records, financial documents, invoices, government forms, contracts, letters, correspondence, and other structured or semi-structured business documents.
Nashville is designed to avoid brittle template dependence. It can learn document patterns and use visual and semantic understanding instead of relying only on fixed field positions.
Yes. Nashville is designed for documents that include handwriting, stylized text, poor scans, and irregular layouts.
Structured data that can be returned through APIs or used by downstream systems, including Orion for reasoning and Polaris for workflow automation.
Reviewed outputs can be curated and used to improve models, allowing extraction performance to strengthen as the system processes more real-world documents.
Nashville turns complex, messy, real-world documents into structured data your teams and systems can use.
We enable highly regulated organizations to build, govern, and operate domain-specific models within their own infrastructure and governance frameworks.
Nashville extracts fields, tables, entities, handwriting, checkboxes, clauses, codes, and relationships from complex documents — so teams can move from manual review to JSON-ready outputs without brittle OCR templates.
Nashville is the extraction layer of OmniSuite™. Once Chicago has classified and separated the document stream, Nashville reads the content and pulls out the facts the business needs. That structured data can then feed Orion for grounded reasoning, Polaris for workflow automation, or any host system that needs clean, usable document data.
Nashville is the extraction layer. It sits between document classification and enterprise reasoning — the step that converts whatever Chicago hands over into structured data the rest of the platform (and your systems) can actually use.
Most document extraction systems were built for stable forms and predictable layouts. They read text with OCR, match fields against templates, and rely on rules that work until the format changes. Real enterprise documents do not behave that way.
Labels move. Tables change. Fields appear in different places. Scans are noisy. Handwriting shows up. A single packet may contain forms, notes, attachments, correspondence, and supporting evidence. When the system cannot understand the document, humans become the fallback.
It can capture characters on a page, but it often misses the relationship between a label, a value, a table, a checkbox, and the surrounding business meaning.
A new vendor form, revised claim packet, different government application, or slightly shifted table can create downstream extraction errors.
People end up correcting fields, checking tables, validating values, and rekeying data into downstream systems.
Nashville treats each document as a visual and semantic object, not a flat text file. It looks at the words, the layout, the surrounding labels, the field positions, the tables, the handwriting, and the structure of the page together. That makes extraction more resilient when formats vary and more useful when the document carries meaning through layout.
Detect fields, labels, tables, regions, checkboxes, signatures, and visual relationships.
Extract the facts that matter in context — names, dates, amounts, codes, policy numbers, clauses, entities, and line items.
Handle structured, semi-structured, and unstructured documents without rebuilding templates for every new format.
Output structured data that can feed case systems, workflow tools, analytics, RAG pipelines, and downstream automation.
Nashville manages the full extraction lifecycle: understand the page, map the structure, extract the facts, improve the model, and return data your systems can use.
Nashville evaluates the document visually and semantically to understand what it is looking at, how the page is organized, and which content relationships matter.
OmniSense™ identifies labels, values, tables, checkboxes, regions, and document structure so the system can extract data from the right places.
OmniParse™ converts the discovered structure into extraction instructions that can be used by Nashville’s domain-specific models.
Nashville uses the right model type for the document and use case — semantic, entity-based, visual, or high-speed visual extraction.
OmniTrain™ uses reviewed results, active learning, reinforcement learning, and preference feedback to improve model performance over time.
Outputs are returned as structured data for host systems, APIs, analytics, RAG, or downstream workflows.
Not every document needs the same extraction strategy. Nashville supports multiple model types so teams can start fast, handle different document formats, and scale to production without changing platforms.
Simple to moderate forms, semi-structured and unstructured content where speed and setup time matter.
Contracts, narratives, correspondence, regulatory documents — where meaning matters more than position.
Repeatable structured forms where layout, tables, and visual structure carry important meaning.
High-volume, cost-sensitive, edge, or near-edge environments that need fast visual understanding.
Many extraction projects stall because the demo model and the production model are not the same path. Nashville is designed so early extraction work becomes the foundation for production-grade automation.
Use lightweight models to begin extracting from real documents quickly, without months of labeling.
Capture fields, values, geometry, confidence scores, and extraction outputs from real business content.
Use guided curation to validate outputs and turn human review into better training data.
Use curated results to train higher-fidelity VLMs or MVLMs for production workloads.
Move from early extraction to production automation without re-platforming or rebuilding the workflow.
“The first model gets you moving. The reviewed outputs make the next model better.”
Processes text, visual structure, labels, tables, fields, handwriting, and layout together.
Understands where information sits on the page and how fields relate to nearby labels and regions.
Captures structured rows, columns, values, codes, quantities, and financial or clinical details.
Handles poor-quality scans, handwritten fields, stylized forms, and inconsistent document quality.
Lets teams review, correct, and improve extraction workflows without requiring a full data science team.
Returns JSON-ready data for enterprise systems, partner applications, RAG, reporting, and workflow automation.
Every new format means a new template, a new rule, or a new round of labeling before extraction can start.
People read, rekey, check, and correct document data because the system can’t be trusted to do it alone.
A vendor revises a form, a scanner shifts a margin — downstream extractions break and rework piles up.
Case systems, workflow tools, and analytics get half-structured exports that still need human cleanup.
Production use doesn’t make the system any smarter — corrections live in spreadsheets, not models.
The PoC model and the production model are two different rebuilds with different timelines and different teams.
Start extracting from new document types without building large template libraries or writing brittle rules.
Reduce the human effort spent reading, rekeying, checking, and correcting document data.
Handle layout variation, new document versions, vendor-specific formats, and inconsistent scans with less rework.
Turn unstructured and semi-structured documents into JSON-ready data downstream applications can use directly.
Reviewed results feed the model improvement loop — production use becomes a source of better training data.
Move from early demos to high-volume production workloads inside the same Nashville environment.
Extract claim numbers, policy details, loss descriptions, adjuster notes, line items, supporting evidence, and coverage-related facts.
Capture patient details, service codes, lab values, clinical evidence, plan criteria, and treatment documentation.
Extract borrower information, income data, loan values, KYC fields, underwriting facts, and supporting financial evidence.
Read structured forms, eligibility documents, permits, public-sector intake packets, and compliance evidence.
Extract line items, amounts, dates, vendor details, account numbers, tax data, and payment information.
Identify parties, dates, obligations, clauses, renewal terms, exceptions, and key entity relationships.
Nashville sits between document classification and enterprise reasoning. It gives downstream systems the structured data they need to answer questions, validate decisions, and trigger action.
Classifies and separates mixed document streams.
Extracts fields, tables, entities, handwriting, clauses, and relationships.
Reasons over structured data and retrieved enterprise content.
Routes, escalates, validates, and triggers workflow actions.
Chicago identifies the document. Nashville extracts the facts. Orion reasons from them. Polaris moves the work forward.
No. Nashville is a multi-modal document understanding layer. It can use text, but it also interprets layout, tables, labels, checkboxes, handwriting, and visual structure.
Forms, claims, applications, medical records, financial documents, invoices, government forms, contracts, letters, correspondence, and other structured or semi-structured business documents.
Nashville is designed to avoid brittle template dependence. It can learn document patterns and use visual and semantic understanding instead of relying only on fixed field positions.
Yes. Nashville is designed for documents that include handwriting, stylized text, poor scans, and irregular layouts.
Structured data that can be returned through APIs or used by downstream systems, including Orion for reasoning and Polaris for workflow automation.
Reviewed outputs can be curated and used to improve models, allowing extraction performance to strengthen as the system processes more real-world documents.
Nashville turns complex, messy, real-world documents into structured data your teams and systems can use.
We enable highly regulated organizations to build, govern, and operate domain-specific models within their own infrastructure and governance frameworks.