We’ve all attended our fair share of tech vendor conferences over the years. Every conference every year has more or less sounded the same, with aspirational talk of digital transformation, euphoric opening anthem videos, feel good customer stories, and snazzy demos.
Now as the age of AI has moved firmly into the mainstream, something is different. Every tech vendor is trying to up the ante as they attempt to lay claim on the future of AI. I attended Nvidia GTC a few weeks ago, and their scale, ambition, and expanse is something to behold. Jensen Huang’s two and a half hour keynote was a tour de force. He projected at least $1 trillion in high-confidence compute demand through 2027. He highlighted a supply chain capable of churning out multi-gigawatts of new AI factories every single month. He laid out plans to build data centers in space. And he featured robots of all shapes and form factors ready to roam the halls of businesses, factory floors, and homes.
Huang made it clear that traditional data centers are dead, replaced by gigawatt-scale “AI factories” whose sole purpose is to generate tokens—the new commodity of the enterprise. The message delivered at the event sets a new benchmark for the rest of the industry to chase.
In less than a month, Hyperscience will be showcasing our story in a big way at Google Cloud Next in Las Vegas. As a partner of Google Cloud with several marquee joint customers running Hyperscience on Google Cloud, we look forward to engaging with current customers, partners, and prospects at the event. We expect Google to raise the bar on the industry yet again, making the case for their integrated stack of software and compute for running end-to-end AI ecosystems.
The missing link: fueling the AI factory with ground truth
But there is a critical missing link between the gigawatt-scale compute power showcased by Nvidia and the expansive AI ecosystems championed by Google Cloud. In his GTC keynote, Jensen Huang declared that the “inference inflection has arrived,” meaning AI has moved beyond training and is now actively reasoning and doing productive work. However, he also rightly pointed out that these incredibly fast AI models require structured data – the “ground truth” of the enterprise – to have context, meaning, and trustworthiness.
The reality for most organizations is that this “ground truth” is trapped in the back office, buried inside an endless, messy array of unstructured documents. You can have the most powerful GPUs and the most integrated cloud ecosystems in the world, but without a trusted on-ramp to accurately convert these raw documents into high-fidelity structured data, the entire AI engine grinds to a halt.
The full spectrum: connecting systems of record and systems of AI
To understand why capturing this ground truth is so challenging, we have to look at the two distinct ways organizations need to process documents today. To truly run an intelligent enterprise, businesses must master a dual mandate: seamlessly powering both the “upper loop” and the “lower loop” of automation.
The upper loop is the traditional engine of business operations. It focuses on quickly extracting a targeted 5 to 10 high-impact fields to feed deterministic systems of record, such as an ERP or CRM. This loop is all about high-volume efficiency—processing frequent, straightforward transactions quickly and cost-effectively at massive scale.
However, the agentic systems and Generative AI models that run inside tomorrow’s AI factories require an entirely different approach—the lower loop. Generative AI demands deep context, meaning, and understanding to reason effectively. In this loop, the data requirement explodes from a handful of fields to anywhere between 100 and 500 critical data points per document. For example, if an AI agent is evaluating a modern mortgage application, it needs a holistic view of the applicant’s financial health, understanding the nuances of gig-economy income, alimony payments, and varying credit card debt.
Neither loop is inherently better than the other; they simply serve two completely different sets of technologies (systems of record vs. systems of AI). The challenge is that legacy document processing vendors are confined entirely to the upper loop, breaking down when confronted with lower loop complexity. Conversely, trying to use expensive hyperscaler API tools to brute-force high-volume upper loop tasks leads to runaway compute costs. To succeed, organizations need a unified architecture capable of applying the exact right level of intelligence for the exact right system.
Orchestrating Intelligence: The Hyperscience Hypercell
To successfully deliver on this dual mandate, organizations cannot rely on rigid, monolithic models. They need a centralized document operations control plane. This is exactly what the Hyperscience Hypercell provides. As an intelligent orchestration layer, it breaks the traditional document processing paradigm by seamlessly managing both upper and lower loop automation within a single pipeline.
The secret to this is intelligent inference layering. High-volume transactions in the upper loop are highly cost-sensitive, and organizations do not want to burn expensive GPU compute on straightforward, frequent extraction tasks. The Hypercell handles these standard workflows by routing them to highly efficient, specialized CPU-based models.
However, when a workflow demands the deep context and complex reasoning of the lower loop, the platform applies a different tool for the job. The Hypercell seamlessly shifts to a powerful GPU-centric layer, deploying heavy-duty Vision Language Models (VLMs) like Hyperscience’s proprietary ORCA model to scrape a larger corpus of data, perform complex calculations, and make policy-based recommendations.
Harnessing the Power of LLMs and Nemotron
This composable intelligence perfectly aligns with the massive AI ecosystems showcased at Nvidia GTC and Google Cloud Next. The Hypercell allows enterprises to easily plug into the world’s best models. Customers can harness third-party LLMs like Google Gemini—which is supported through the Hyperscience native GenAI model library—or integrate powerful tools like Nvidia’s production-ready Nemotron models into their orchestration. This ensures that an AI agent’s decisions are continuously grounded in trusted, authoritative enterprise data.
Built for Developers Delivering Trusted Data Pipelines
For developers tasked with building these integrations, the Hypercell acts as the “Kubernetes of document automation.” Rather than forcing a one-size-fits-all model, it utilizes a containerized approach comprised of Blocks (specialized, self-contained automation units) and Flows (configurable orchestrations).
This dynamic assembly gives developers an unmatched platform to build trusted data-delivering pipelines for both systems of record and systems of AI. Ultimately, it allows organizations to harness the transformative compute power and cloud ecosystems of Nvidia and Google, without falling victim to the runaway inference costs associated with brute-forcing API tools.
Putting AI to Work at Google Cloud Next
The gigawatt-scale AI factories envisioned by Nvidia and the scalable ecosystems of Google Cloud represent the future of enterprise computing. But to truly realize their potential, organizations must bridge the gap between their unstructured back-office documents and their advanced systems of AI. As the industry gathers for Google Cloud Next in Las Vegas, Hyperscience will be showcasing exactly how to build this mission-critical data on-ramp to make that future a reality. If you are attending the event, we invite you to stop by our booth to see the Hypercell in action or book a one-on-one meeting with our team to discuss your specific automation challenges.
Most importantly, be sure to add our speaking session to your agenda: “Building a Mission-Critical Data On-Ramp for AI.” Join Joe Harrington, Head of AI Engineering at PwC Global Technology, alongside Hyperscience Field CTO Chip VonBurg, as they discuss how to move GenAI out of the experimental sandbox. They will showcase exactly how PwC is leveraging the Hyperscience Hypercell combined with Google Gemini on Google Cloud to deliver structured, highly accurate, and GenAI-ready data at mission-critical scale.
The inference inflection point is here—it’s time to fuel your AI with the ground truth it deserves.