Hyperscience

Automatically extracts structured data from unstructured documents like invoices, contracts, forms, and reports with high accuracy. Uses AI to understand document layout, context, and content relationships to pull relevant information without manual data entry.

Automatically categorizes and routes documents into predefined classes or workflows based on content understanding. Learns from examples to classify documents with high accuracy without explicit rule-based logic.

Maintains comprehensive audit trails and compliance documentation for all document processing activities. Tracks data lineage, processing decisions, and human reviews to support regulatory requirements and audits.

Processes and extracts data from documents in multiple languages with language detection and translation capabilities. Handles documents with mixed languages and regional variations.

Provides user-friendly interfaces for human reviewers to validate, correct, and approve automated extractions. Enables efficient review workflows with annotation, feedback, and model improvement capabilities.

Enables training of domain-specific AI models on proprietary documents without requiring machine learning expertise. Provides low-code interfaces for organizations to customize models for their unique document types and business rules.

Orchestrates end-to-end business processes by connecting document processing with downstream systems and human workflows. Manages task routing, approval chains, and system integrations to automate complex multi-step operations.

Connects Hyperscience with existing enterprise systems including ERPs, accounting software, CRMs, and databases. Provides pre-built connectors and APIs for seamless data flow without replacing existing infrastructure.

Provides confidence scoring and automated quality assurance to identify extractions requiring human review. Routes uncertain or high-risk extractions to human reviewers while allowing high-confidence results to proceed automatically.

Processes large volumes of documents in batch mode for high-throughput scenarios. Handles thousands of documents efficiently with progress tracking, error handling, and result aggregation.

Standardizes data extracted from documents with varying formats into consistent, normalized output. Handles format conversions, data type standardization, and field mapping across different document sources.

Tracks and reports on automation performance metrics including accuracy rates, processing volumes, cycle times, and cost savings. Provides dashboards and analytics to measure ROI and identify optimization opportunities.

Automatically detects processing exceptions, errors, and edge cases, then routes them to appropriate handlers or escalates to human teams. Manages retry logic, fallback procedures, and exception workflows.