As AI reshapes the way we create, consume, and interact with information, the value of original content and the rights of those who create it have never been more important.
We are at an inflection point in the AI era, one that demands more than just innovation. It requires transparency and trust. And ultimately a new business model.
Over the past decade, power and capital have consolidated in the hands of a few massive technology companies that have built their empires by scraping and monetizing the content of the global internet, often without permission or compensation for those creating original content. From journalists and publishers to everyday individuals, the work of countless creators has been harvested at no cost to fuel commercial AI systems.
This raises serious concerns, not just about fairness and the erosion of sustainable business models for content creators, but also about the integrity and trustworthiness of the data being used to train these AI systems. These practices, while fueling rapid breakthroughs, present fundamental questions about intellectual property, transparency, and the long-term health of the creative and technical ecosystems that AI depends on.
Today, Cloudflare announced that it is now the first Internet infrastructure provider to block AI crawlers accessing content without permission or compensation, by default. Starting today, website owners can choose if they want AI crawlers to access their content, and decide how AI companies can use it. AI companies can also now clearly state their purpose – if their crawlers are used for training, inference, or search – to help website owners decide which crawlers to allow. Cloudflare’s new default setting is the first step toward a more sustainable future for both content creators and AI innovators. This announcement is a continuation of a vision that CEO Matthew Prince recently articulated in this Fortune article where he called attention to the responsibility that technology companies have to protect the rights of creators. It’s a pivotal stance, and Hyperscience has joined Cloudflare in their vision to remake the internet.
Enterprise AI Can’t Be Built on Unreliable Data
Enterprises, particularly those in regulated industries like government, healthcare, and financial services, don’t just need powerful AI, they require AI that’s accountable, auditable, and aligned with ethical and legal standards. At Hyperscience, we believe that trust, compliance, and explainability in AI are not just nice-to-haves but are non-negotiable.
However, that is impossible to achieve if your foundation is scraping data of unknown origin. Without clear provenance, there’s no way to verify accuracy, ensure compliance with privacy regulations, or respect intellectual property rights. Models trained this way inherit the biases, misinformation, and legal ambiguity of their data sources, making them fundamentally unfit for use in sensitive, high-stakes enterprise environments.
This is why we’ve taken a different approach.
The Hyperscience Difference
Our success has always been rooted in original research and proprietary Machine Learning models, which are built on accurate data identification and extraction, and accurate data labeling. Our models are trained using a combination of proprietary data that is relevant to your organization, and in some instances, synthetic data, especially when the need to protect certain Personally Identifiable Information (PII) arises. This has unleashed new levels of productivity for our customers, allowing them to connect end-to-end processes and accelerate decisioning from the back office to the front office, and positions Hyperscience as a market-leader in the fast-growing data labeling and hyperautomation markets.
For instance, Hirschbach, one of North America’s leading transportation solution providers, relies on Hyperscience to deliver modern, seamless customer and driver experiences. Hirschbach uses Hyperscience to accurately extract, label and process data from a vast array of unstructured documents that come with every truck delivery, and passes this information into downstream systems to automate key business processes.
Similarly, Hyperscience is revolutionizing how the U.S. Department of Veterans Affairs processes health benefits claims for veterans. The VA uses Hyperscience to process over 1 billion documents annually, dramatically reducing administrative backlog and accelerating claims approvals. The system has freed up hundreds of VA staffers from manual data entry while generating approximately $45 million in annual cost savings. Most importantly, the solution has reduced the time it takes for US veterans to receive their benefits from three months to three days.
Our Infrastructure is Built for Transparency, Not Black Boxes
Unlike public / generic LLMs that operate behind opaque APIs, Hyperscience offers modular, composable AI infrastructure that gives enterprises control over their models, their data, and the cost required to run the AI system. The Hyperscience Hypercell is built on AI components called Blocks and Flows, and activates only the necessary Blocks within each Flow, optimizing processing time, cost, accuracy, and automation. This occurs by routing each specific identification, classification and extraction task to the right machine learning model in real time for optimal outcomes. Flows can evolve dynamically by incorporating new Blocks and AI models into each orchestration pipeline. That means every prediction, every result, and every workflow can be inspected, governed, and explained, and managed within budget.
This isn’t just good practice, it’s essential for building AI that is safe, cost-effective, scalable, and sustainable in the enterprise.
Humans at the Center, Always
Human-in-the-Loop (HiTL) is a core design principle at Hyperscience because we believe the best outcomes happen when AI and people work together. By keeping humans in control of key decision points, we ensure our models continuously learn from expert feedback, deliver higher accuracy, and remain aligned with real-world expectations, especially in high-stakes, regulated environments where trust and accountability matter most.
By respecting the role of people and the integrity of the data they work with, we help customers achieve meaningful outcomes without cutting ethical corners.
A Call for Responsible AI at Scale
Cloudflare’s announcement is a meaningful step forward and a call to action for the entire industry. If we want AI to benefit everyone, then software companies must commit to AI that is powerful, but principled.
We need systems that honor the contributions of creators in every field, whether in software development, healthcare and biotech, art and music, or journalism. And we need a framework that ensures a fair and productive exchange between the AI systems we build and the people and content that make them possible.
At Hyperscience, we’re proud to support this vision. And we remain steadfast in our belief that responsible innovation isn’t a constraint, it’s the path to durable, transformative impact.