Mantis Biotech is making ‘digital twins’ of humans to help solve medicine’s data availability problem

Large language models trained on vast datasets could speed genomics research, streamline clinical documentation, improve real-time diagnostics, support clinical decision-making, accelerate drug discovery, and even generate synthetic data to advance experiments.

But their promise to transform biomedical research often runs into a bottleneck: Beyond the structured data healthcare relies on, these models struggle in edge cases like rare diseases and unusual conditions, where reliable, representative data is scarce.

New York-based Mantis Biotech claims it’s developing the solution to fill this data availability gap. The company’s platform integrates disparate sources of data to make synthetic datasets that can be used to build so-called “digital twins” of the human body: physics-based, predictive models of anatomy, physiology, and behavior.

The company is pitching these digital twins for use in data aggregation and analysis. These digital twins could be used for studying and testing new medical procedures, training surgical robots, and simulating and predicting medical issues or even patterns of behavior. For example, a sports team could predict the likelihood of a specific NFL player developing an Achilles’ heel injury based on their recent performance, training load, diet, and how long they’ve been active, Mantis’ founder and CEO Georgia Witchel explained to TechCrunch in a recent interview.

To build these twins, Mantis’ platform first takes data from a variety of sources such as textbooks, motion capture cameras, biometric sensors, training logs, and medical imaging. Then, it uses an LLM-based system to route, validate, and synthesize the various data streams, and runs all that information through a physics engine to create high-fidelity renders of that dataset, which can then be used to train predictive models.

“We’re able to take all these disparate data sources and then turn them into predictive models for how people are going to perform. So anytime you want to predict how a human being is going to be performing, that is a really good use case for our technology,” Witchel said.

The physics engine layer is key here, Witchel told TechCrunch, because it helps the platform enhance the available information by grounding the generated synthetic data and realistically modeling the physics of anatomy.

“If I asked you to do hand-pose estimation for someone who is missing a finger, it would be really, really hard, because there are no publicly available datasets of labeled hand positions of someone who is missing a finger. We could generate that dataset really, really easily, because we just take our physics model and we say, remove finger X, regenerate model,” she said.

Since Mantis’ platform fills gaps in data sources, Witchel thinks there’s potential for it to be used widely across the biomedical industry, where information on procedures or patients can be difficult to access, is unstructured, or siloed into various sources. She stressed edge cases or rare diseases, where data is hard to obtain since there are often ethical and regulatory constraints around including patients’ data in public datasets, or using it for training AI models.

“You know how when you see a three-year-old running around, and they have a Barbie, and they’re holding it by one leg and smashing it against a table? I want people to have that mindset with our digital twins,” she said. “I think that’s going to open up people to this idea that humans can be tested on when you’re using virtual humans. I feel currently, people operate with the exact opposite mindset, which totally makes sense, because people’s privacy should be respected. In fact, I don’t really think people’s data should be exploited at all, especially when you have these digital twins.”

For now, Mantis has seen success in professional sports, presumably because there is a need to model high-performing athletes. Witchel said one of the startup’s main clients is an NBA team.

“We create these digital representations of the athletes, where it basically shows here’s how this athlete has jumped, not just today, but for every single day in the past year, and here’s how their jumps are changing over time compared to the amount that they’re sleeping, or compared to how many times they lift their arms above their head,” she explained.

The startup recently raised $7.4 million in seed funding led by Decibel VC, with participation from Y Combinator, a few angel investors, and Liquid 2. The funding will be used for hiring, advertising, marketing, and go-to-market functions.

The next step for Mantis, Witchel said, is to continue building out the tech, and eventually release the platform to the general public, targeting preventative healthcare. The company is also working to cater to pharmaceutical labs and researchers working on FDA trials, aiming to deliver insights into how patients are responding to treatments.

Recommended related domains:
1. AIBioics.com

The “Convergence” Narrative: The Mantis Biotech article highlights a critical shift: we are moving beyond traditional biotech into an era where Large Language Models (LLMs) are used to route and validate biological data, and Physics Engines are used to simulate human anatomy.

Why this domain? AIBioics.com is the most logical, category-defining name for this new industry. It perfectly phonetically blends “AI” with “Bioics” (Biological Systems). This domain serves as the ultimate umbrella brand for any platform that sits at the intersection of artificial intelligence and biological research. Just as Mantis is creating “digital twins” by synthesizing disparate data, this domain synthesizes the two most valuable keywords in the tech world right now: AI and Bio.

2. BioicsAI.com

The “Systems” Edge: Mantis doesn’t just look at static data; it creates predictive models of anatomy, physiology, and behavior. This is not just “AI in Bio,” it is “Bio as a System” powered by AI.

Why this domain? BioicsAI.com flips the script, putting the emphasis on the biological system itself (“Bioics”) being enhanced by AI. This subtle shift makes it sound like a robust, enterprise-grade software solution. It is ideal for a company that wants to position itself as the “operating system” for biological data, much like how Mantis uses its platform to integrate textbooks, motion capture, and biometrics into one cohesive predictive model. It has a strong “SaaS for Biotech” vibe.

3.BiotechMaterial.com

The “Material” Science: The article specifically mentions that Mantis uses a “Physics Engine” to ground its synthetic data. They simulate the physics of anatomy—like removing a finger to model a hand injury. This requires an understanding of the material properties of biological tissue.

Why this domain? While the other domains are broad and conceptual, biotechmaterial.com is a concrete, high-value asset for the hardware and simulation side of the industry. It represents the “stuff” that digital twins are made of—the synthetic datasets, the physical parameters, and the material science behind tissue modeling. In a world where AI needs high-fidelity data to train on, this domain represents the raw “material” that fuels the AI revolution in biotech.

Source:https://techcrunch.com/2026/03/30/mantis-biotech-is-making-digital-twins-of-humans-to-help-solve-medicines-data-availability-problem/