Before you say yes, you should know what you’re up against.
ORCA (Optical Reasoning and Cognition Agent) is a proprietary Vision Language Model (VLM) framework, engineered to process both visual and text elements across semi-structured and unstructured documents. A major advantage of ORCA is its ability to deliver day-one automation by accurately interpreting previously unseen documents without requiring organizations to build and train a model from scratch.
To meet the rigorous transparency, compliance, and risk management standards necessary for enterprise use, ORCA distinguishes itself from generic LLMs by keeping data completely private and operating without reliance on public, third-party APIs. The framework is uniquely “accuracy-harnessed,” allowing businesses to set strict target performance thresholds and bring in a human when these thresholds are not met via an integrated Human-in-the-Loop (HiTL) verification system.
Now we’re inviting you to go head-to-head in our ORCA Challenge.
But first — a bit more background about your competitor.
What is a VLM?
A Vision Language Model (VLM) is a powerful machine learning model that fuses computer vision with large language models. VLMs treat text, images, audio, video and code equally and simultaneously, rather than treating non-text data as an afterthought. VLMs use deep semantic and visual context to reason through an image, making them capable of understanding everything from text and tables to logos and charts, out-of-the-box. However, on its own, a single VLM model cannot guarantee accuracy or efficiently manage complex enterprise workflows.
A VLM Framework is a multi-component architecture that can be made up of multiple VLMs and/or specialized agents. The Hyperscience ORCA VLM Framework wraps proprietary models around open-source VLMs. This allows for the utilization of the latest or best-performing models on the market and the application of strict quality controls.
For example, ORCA uses dynamic confidence thresholding, continuous Quality Assurance (QA) sampling, and Human-in-the-Loop (HitL) supervision to review uncertain predictions, mitigating hallucinations and guaranteeing data accuracy. ORCA can also be used as a modular component alongside other models within a larger workflow, ensuring tasks are performed by the most efficient, cost-effective model or it can be connected to external databases or persistent Knowledge Store to enable RAG workflows that ground the VLM’s reasoning in verified enterprise data.
Where does ORCA shine?
ORCA dominates in scenarios characterized by high variability and visual complexity.
Use ORCA when:
- You need immediate automation: By eliminating the ML “cold start” problem, ORCA bypasses the weeks usually spent aggregating and labeling training data.
- Processing unstructured content: Whether dealing with messy, hand-filled forms, long-form contracts, or erratic layouts, ORCA leverages its visual reasoning to contextualize the data.
- Security is paramount: Because ORCA is hosted safely within the Hyperscience infrastructure, sensitive PII remains entirely under your control, dodging the data leakage risks associated with public LLMs.
- Accuracy is non-negotiable: In recent benchmarks, ORCA outperformed leading open-source models and frontier LLMs (like Claude 3.5 Sonnet and Gemini 1.5 Pro) on complex documents like Bills of Lading and Invoices.
Ready to Take the ORCA Challenge
You’ve seen what ORCA can do inside enterprise workflows, now it’s time to see how you stack up. This is your chance to go head-to-head with cutting-edge AI by putting your document skills to the test against ORCA.
In this “data duel,” you’ll extract key information from real-world documents like utility bills, receipts, and medical notes as quickly and accurately as you can to see if you can outpace ORCA’s speed and accuracy.
ORCA in Action:
US-based Fortune 500 Financial Services Firm
This large institution had a fragmented, heavily manual document capture process as a result of legacy technology, acquisitions, and requirement changes. They wanted to standardize intake channels across their four main business units into one solution that could address their high document volumes and variability (over 3,000 distinct document types).
They selected Hyperscience in large part due to ORCA’s ability to remove the need to train thousands of models and instead provide classification and extraction of core fields across their unstructured and semi-structured document types out of the box. They anticipate this will reduce their implementation time by 8 months.
Additionally Hyperscience will:
- Reduce transaction Average Handling Time (AHT)
- Allow the organization to keep data entirely within the Hyperscience platform – no sending sensitive data to public APIs or LLMs – to meet security and data isolation requirements
- Support the onboarding of one business unit and easily scale to the additional three with the ability to process their 2M+ annual page volumes
Wholesale Mortgage Company
One of the largest Wholesale Mortgage companies in the US needed to be able to scale in response to easing interest rates. They also wanted to increase their underwriting speed and decision accuracy. Under their old process, over 30% of applications needed manual review, limiting their ability to provide the fast turn-around times their customers demanded and increasing the cost of each loan to the business.
With Hyperscience they were able to address this issue with a solution that delivered 90% automation of pay stubs – a notorious documents processing challenge – in just 8 weeks. They will soon be expanding to bank statement automation and other income verification documents in the loan application packet.
The result:
- Underwriter time savings of over 2,000 hours per month
- 12.5% increase in revenue acceleration resulting in an additional $1.1M in closed won business per month
- Increased agility to scale in response to rate cuts without additional headcount
How ORCA Fits in a Broader Workflow
ORCA acts as the cognitive engine within Hypercell’s Agentic Orchestration pipeline. Using a composable architecture of Blocks and Flows, Hypercell dynamically uses ORCA alongside other specialized machine learning models like:
- Pre-Processing & Core Models: Before ORCA even touches a document, native ML models automatically sanitize the image—deskewing, de-noising, and rotating the file to ensure a pristine input.
- Composite Routing: Specialized models for Field ID, Table ID, and Optical Intelligent Character Recognition (OICR) can be orchestrated in tandem with ORCA to balance GPU costs and CPU efficiency. The new ORCA Composite Blocks handle multiple steps like machine identification and transcription simultaneously to simplify developer setup.
- ORCA-in-the-Loop: If ORCA’s confidence falls below established accuracy thresholds, it intelligently triggers an exception. During this Human-in-the-Loop review, ORCA provides Supervision Page Location Focus, guiding the human directly to the estimated visual location of the field on the page to drastically speed up resolution. ORCA can also be used as an AI-in-the-Loop agent, evaluating documents against internal Knowledge Stores to enrich data and reduce human touchpoints.
Together, this orchestration ensures ORCA isn’t operating in isolation — it’s embedded within a broader system designed for precision, efficiency, and scale. It’s not just intelligent; it’s operationally optimized for real enterprise complexity.
Did you beat ORCA?
Did you beat ORCA or did your speed and accuracy fail to match the AI?
Share your results on LinkedIn and tag Hyperscience. Bragging rights are on the line.
If you haven’t taken the challenge, what are you waiting for? Take the ORCA Challenge!