Hyperscience Hypercell is an enterprise intelligent document processing platform built by Hyper Labs, Inc. It automates document-driven back-office workflows by converting semi-structured and unstructured documents into machine-readable data. Source documents include handwritten forms, invoices, medical records, and legal correspondence.
The platform targets the operational drag and error rates common in legacy OCR tools. High-volume organizations often face backlogs, delayed billing, and compliance risk from incomplete or inaccurate extraction. Hypercell aims to reduce that manual overhead through automated classification and extraction pipelines.
Hypercell runs on a modular machine learning architecture. It combines specialized pre-trained models, custom-trained models, and a zero-shot vision language model called ORCA. Documents are ingested, classified, and processed through connected “Blocks” arranged into workflow “Flows,” with low-confidence cases routed to human or AI-in-the-loop review.
Hyperscience reports document accuracy of up to 99.5% and automation rates up to 98%. The platform also includes a generative AI module, Hypercell for GenAI, that builds vector databases for retrieval-augmented generation. It supports FedRAMP High authorization, TX-RAMP Level 2, and air-gapped on-premises deployment.
Pricing
Hyperscience does not publish pricing on its website. No free plan, free trial, or self-service tier is listed, and enterprise or usage-based pricing figures are not disclosed. Prospective customers must contact sales or request a demo to receive a quote. The platform is also listed on the AWS Marketplace and Google Cloud Marketplace, letting enterprise buyers apply pre-committed cloud spend toward a purchase.
* Disclaimer: Please note that pricing information may not be up to date. For the most accurate and current pricing details, refer to the official website.
Key Features
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Zero-shot extraction from untrained layouts via the ORCA VLM
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Low-code Blocks and Flows builder for custom workflows
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15+ pre-trained models for freight and transportation documents
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Human-in-the-loop review dashboard with field-level confidence scores
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Automated PII redaction for medical, legal, and government records
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Vector database generation for RAG and LLM fine-tuning
Use Cases
Freight Delivery-to-Cash Automation
Automates processing of load document packets, including Bills of Lading and Proof of Delivery, from ingestion through invoice generation. Hyperscience reports turnaround times of 10 to 15 minutes and a reduction in manual review of about 47 hours per week.
Public Sector Benefits Intake
Processes citizen applications and recertification documents for programs like SNAP. Hyperscience reports a 70 to 90% reduction in manual data entry and roughly a 43% decrease in incomplete submissions.
Enterprise GenAI Data Preparation
Structures non-machine-readable back-office documents for LLM integration. The platform labels and annotates dark data to feed vector databases without exposing sensitive PII.
Federal High-Volume Document Ingestion
Supports large federal agencies processing millions of records inside a FedRAMP High authorized environment. Hyperscience reports the Social Security Administration processes up to 250 million documents annually on the platform.
Regulatory Compliance and PII Redaction
Scans medical, legal, and administrative paperwork to mask personal details before disclosure. This supports compliance with regulations such as FOIA and GDPR.
Strengths & Weaknesses
Strengths
Reports up to 99.5% extraction accuracy, including handwritten content.
Holds FedRAMP High, TX-RAMP Level 2, SOC 2 Type II, and Cyber Essentials Plus certifications.
Offers zero-shot extraction via the ORCA model without upfront training on new layouts.
Supports SaaS, private cloud, on-premises, and air-gapped deployment options.
Includes 15+ pre-trained domain models for logistics and government document types.
Weaknesses
Pricing tiers and licensing costs are not published, requiring a sales conversation.
Custom workflow logic via code blocks requires Python programming knowledge.
Highly specialized document formats may need custom model training before reaching peak automation.
No self-service signup or instant trial; access requires scheduling a demo.
Who Is This For?
Transportation and freight logistics providers: carriers, brokers, and 3PLs automating load package verification to speed up billing and driver payouts.
State and federal government agencies: public sector bodies needing FedRAMP High compliant tools to process benefits applications and lower payment error rates.
Financial services and insurance institutions: enterprises processing high volumes of forms, mortgage applications, and claims that require accurate, auditable extraction.
Enterprise AI and data engineering teams: teams building RAG architectures that need structured, annotated data from non-machine-readable documents.
Frequently Asked Questions
What extraction accuracy does Hyperscience Hypercell report?
Hyperscience reports customers regularly achieve up to 99.5% accuracy and 98% automation, including for handwritten text.
What deployment options are available?
Hypercell supports SaaS on AWS or Google Cloud, customer private tenants on AWS, Azure, or Google Cloud, on-premises and air-gapped environments, and FedRAMP High authorized delivery via Palantir FedSTART.
Can it process document types it has never seen before?
Yes, the ORCA vision language model supports zero-shot extraction using natural language prompts, so untrained and irregular layouts can be processed without prior model training.
How does Hypercell support generative AI projects?
Hypercell for GenAI labels and structures back-office documents into ground truth data, building vector databases such as Vector AI, BigQuery, and AlloyDB for RAG and LLM fine-tuning.
What compliance certifications does the platform hold?
Hyperscience holds FedRAMP High authorization, TX-RAMP Level 2 certification, SOC 2 Type II verification, and Cyber Essentials Plus certification.
What does Hypercell cost?
Pricing is not publicly disclosed. Prospective customers need to contact sales or request a demo, though the platform is also available through the AWS and Google Cloud marketplaces.
Does the platform require technical skills to customize?
Basic Flows are built with a low-code interface, but advanced business logic and transformations rely on custom Python code blocks, which requires programming knowledge.
How are low-confidence extractions handled?
Documents falling below confidence thresholds are routed to a human-in-the-loop or AI-in-the-loop review interface, where field-level validation also feeds back into model training.
Can Hypercell redact sensitive information?
Yes, it can identify and redact personally identifiable information in medical, legal, and government records to support compliance requirements such as GDPR and FOIA.
What kind of organizations use Hyperscience today?
Reported customers include American Express, Charles Schwab, MetLife, the US Social Security Administration, and the US Department of Veterans Affairs, among others.
Hypercell integrates across major cloud ecosystems. On AWS it connects to services such as Amazon S3, Lambda, Aurora, RDS, Comprehend, SageMaker, Redshift, QuickSight, and Amazon Bedrock. On Google Cloud it supports Cloud Storage, GKE, Cloud SQL, AlloyDB, Vertex AI, Gemini models, BigQuery, and Looker. On Azure it works with Blob Storage, Azure Functions, Azure SQL Database, Azure AI/OpenAI, Synapse Analytics, and Power BI. It also connects to Microsoft Dynamics for ERP workflows and to Palantir FedSTART for FedRAMP High delivery. REST APIs and a Flows SDK are available for custom integrations with RPA tools, TMS, and ERP software.