Data Extraction & Rule Validation

What Is Document Intelligence?

Document Intelligence dashboards are powerful, no-code interfaces that let you harness the full potential of your AI Employees without chatting with them. Think of it as a control panel where you can input data, trigger workflows, and see results structured cleanly, like a spreadsheet. Instead of managing interactions through conversation, dashboards let you run workflows with precision, monitor outputs, and review results -- all in one place.

What Can You Do with a Document Intelligence Dashboard?

• Trigger workflows without chat -- Use the dashboard to run any workflow by directly providing multiple inputs.
• View outputs instantly -- Results are displayed in a structured, Excel-like table for clarity and monitoring.
• Stay in control -- Whether you are extracting data or validating rules, you can review and manage the workflow end to end.

Example: Dashboard with Validation and Extraction

Imagine a dashboard that accepts two inputs (like a document and metadata), runs both a Rule Validation Agent and an Extract Entities Agent, and presents the outputs -- structured entities plus rule-evaluation results -- in a clear table.

Supported Agent Types

While any agent can be added to a workflow, only select agents can publish outputs to the dashboard:

• Text Output Agents -- Any agent that generates plain text results.
• Extract Entities Agent -- Extracts structured entities from documents.
• Rule Validation Agent -- Evaluates custom rules against text or documents.

Extract Entities Agent

This agent extracts entities from provided documents based on defined extraction columns. It uses advanced language model configurations to accurately identify and extract relevant entities.

Use Cases

• Extract key information (names, dates, locations) from contracts or legal documents.
• Automate data extraction from financial reports.
• Parse research articles to identify significant entities.

Column Types

For each column, enter the name of the entity and its description. Columns support various types:

• Text, Number, Boolean
• Groups -- Extract multiple fields at once (like objects)
• Multi-Value -- Support for arrays

HITL (Human-in-the-Loop)

Optional human-in-the-loop review lets users review and approve extractions before they are published.

Inputs

• Documents -- The document you want to use for extraction.
• Extraction columns -- Create columns and define search instructions for each. The agent extracts and organizes data based on those instructions.
• Text Input -- Alternate or additional input to documents.

Output

Each extracted column gets published to the dashboard. You can also enable the confidence score for outputs using feature flags.

Rule Validation Agent

This agent validates rules against extracted data and documents. It ensures that the rules applied yield accurate and compliant results.

Use Cases

• Validate compliance of documents with predefined standards.
• Check consistency and correctness of extracted data.
• Automate quality control processes by verifying rule adherence across documents.

Inputs

• All Rules -- Standalone rules used to validate data quality, formats, and values.
• Instructions -- Extra guidance or notes if needed.
• Extraction Column -- Connects with the Extraction Agent to retrieve extracted values.
• Documents -- The document you want to validate the rules on.
• Text Input -- Additional text you want to validate your rules on.

Output

The agent returns a validation result as text, indicating whether the rules passed along with relevant details about the validation process.

Nested Rules and Filtering

Rule Validation also supports complex nested rules and filtering. These can be set up using the update_persona API call:

• Nested rulesets -- Set up nested rules for use cases like Prior Authorization.
• Aggregation expressions -- Combine rules using rule_id and boolean tokens like AND, OR, NOT.
• Filtering -- Rules can be applied conditionally based on defined filter criteria. Each rule has a field for filter_criteria and nested_rulesets.

Optional Inputs for Dashboard AI Employees

Dashboard AI Employees support optional inputs, allowing workflows to run even when some inputs are not provided. You can mark any input as optional, giving dashboards the flexibility to handle partial or varying data without blocking execution.