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Key Features

DocAI Fabric is a GenAI-native document processing platform that automatically splits, classifies, extracts, and validates data from complex business documents without templates or model training, giving enterprises faster automation with explainable, controlled results.

The system runs and improves from day one without legacy setup burdens.

No Templates, No Training: Instant Adaptation​

Splits files into documents, classifies them by type, and extracts relevant fields for document-centric process automation, without prior template engineering, labeled datasets, or model training cycles. New form layouts and document variants are handled immediately. Users describe the business context, document types, and custom fields; they do not experiment with prompts or explain what an invoice or a ship-to address is.

OCR Decoupled from Reasoning, Grounded by Reverse-Matching​

Computer Vision-powered OCR runs independently from VLM reasoning. Extracted data is reverse-matched against the source document, significantly reducing hallucinations and ensuring outputs are grounded in what the document actually says.

Deterministic Normalization and Validation​

A rule-driven layer automatically normalizes, validates, and cross-checks extracted data, enforcing consistency, catching errors, and re-running processing steps with structured feedback. Gives teams deterministic control over output quality independent of model behavior.

Automatic Feedback from Validation to Reduce Escalation to Humans​

Coming soon

When validation catches an issue, the system automatically re-runs the relevant processing step with structured feedback, correcting its own output before escalating to a human reviewer.

Production-Ready Human Review Interface​

A purpose-built review interface for verifying document splitting, classification, and extraction results, designed for speed and accuracy in production workflows:

  • Keyboard-driven workflow: hotkey support for rapid navigation and decision-making, minimizing mouse dependency for high-volume reviewers.
  • Reviewer instructions: project-specific guidelines are surfaced directly in the review interface, so reviewers know exactly what to look for without switching context.
  • Built-in translation: instantly translate document content for multilingual document processing.
  • Context-aware AI co-pilot: a conversational assistant fully aware of the document content, extracted data, and project configuration. Reviewers can ask questions to understand unfamiliar documents, clarify ambiguous fields, or get explanations for extraction decisions.
  • Historical context: see how similar documents were processed and reviewed before, helping reviewers make consistent decisions across the same document types.
  • Embeddable in other systems: the review interface can be embedded into third-party applications, enabling document review directly within existing business workflows without requiring users to leave their primary tools.

Source Highlighting for Faster Review​

Extracted data is linked to its precise location in the document, so reviewers can instantly verify values against the original source without manual searching.

Decision Reasoning for Every Step​

The system explains why it split pages, classified a document, extracted a specific value, or matched data to the company record. This makes manual review faster, since reviewers see the rationale behind each decision, and when something goes wrong, it is straightforward to identify the root cause and adjust guidelines to correct it.

Context-Aware Reasoning Validation​

Beyond deterministic rules, an AI co-pilot reasons across the full business transaction, leveraging industry knowledge and company policies to surface issues a checklist cannot catch, explain its rationale, and help reviewers make informed decisions faster.

Customer-Specific Few-Shot Learning​

Human corrections immediately improve accuracy within each customer project. Once a reviewer corrects a value, the system applies that knowledge to the very next document processing, with no retraining and no delay.

Proactive Analytics and Guided Remediation​

Coming soon

Pinpoints recurring quality issues, summarizes typical reasons behind them, escalates them, and suggests concrete improvements.

Automatic Prompt Refinement with User-Guided Overrides​

Coming soon

The system continuously refines its own prompts for better results. For marginal document variations (a specific contract type, invoice supplier, or tax form variant), users can provide natural-language guidelines on how to extract data without touching prompts or code.

Natural-Language Configuration with Deterministic Control​

Workflows configured through a natural-language agent, formalized in deterministic, verifiable, auditable artifacts.

Evaluation, Model Benchmarking, and Version Control​

Built-in evaluation tooling lets teams benchmark extraction quality across different models, measure the impact of any configuration change, and publish verified project versions to production with full version control.

Model-Agnostic Across the Full Spectrum​

Supports frontier commercial models down to small open-source models running on-premises. Teams choose the right cost/quality/privacy trade-off per use case without platform changes.

Single-Container Architecture, Deploy Anywhere​

Runs as a single container easily deployed in private clouds and on-premises environments. Horizontally scalable with no complex multi-service orchestration.

Agent-Discoverable via MCP​

Coming soon

Exposes document understanding capabilities as tools through the Model Context Protocol (MCP), so other AI agents in a customer's ecosystem can automatically discover, configure, and invoke document processing (classification, extraction, validation) without custom integration code. Turns the platform into a composable building block for any agentic workflow that needs to understand documents.

This is not "LLM added on the side" to a legacy IDP stack, and not a one-to-one dependency on any single model. It is a framework for applying GenAI correctly to enterprise document automation.