Many enterprises are eager to adopt new AI tools, but face a major roadblock—moving sensitive data into third-party SaaS platforms isn’t an option. That’s where Tensor9 steps in. The startup has created a solution that allows AI vendors to securely deploy their software directly inside a customer’s own environment, whether that’s in the cloud, on bare metal servers, or anywhere in between.
Instead of requiring enterprise clients to upload data to a vendor’s cloud, Tensor9 lets vendors package their software in a format that can run natively within the client’s infrastructure. Then, using digital twin technology, Tensor9 mirrors that deployment—creating a miniature, remote model that shows how the software is performing inside the customer’s system. This gives software companies full visibility and control, without ever touching the client’s sensitive data.
Co-founder and CEO Michael Ten-Pow, a former AWS engineer, says this hands-on visibility is what separates Tensor9 from rivals like Octopus Deploy or Nuon. With digital twins, vendors can monitor performance, debug issues, and resolve problems remotely—without being inside the actual enterprise network.
“You can’t just throw a piece of software over the wall and hope it works,” Ten-Pow said. “Our system gives vendors real-time visibility. They can see how it’s running, log in, diagnose issues, and fix them.”
The rise of enterprise AI adoption is creating strong demand for this kind of solution. Ten-Pow points out that banks and financial institutions often refuse to move data off-premise, especially at scale. He gave the example of an AI-powered enterprise search tool trying to serve a client like JPMorgan—there’s no way a vendor could ask for access to six petabytes of internal data and expect approval.
Originally, Ten-Pow was exploring how to help software startups fast-track SOC 2 compliance, but customer feedback quickly shifted his focus. Enterprises didn’t just want security certifications—they wanted full control over where their software ran. That realization became the foundation for Tensor9, which he officially launched in 2024. He later brought in fellow AWS veterans Matthew Michie and Matthew Shanker as co-founders.
The startup found early traction with voice AI platforms, and now supports a broader range of sectors including enterprise search, data management, and AI infrastructure. Customers already using Tensor9’s tech include 11x, Retell AI, and Dyna AI.
After bootstrapping through its first year, Tensor9 recently raised a $4 million seed round led by Wing VC, with support from Level Up Ventures, Devang Sachdev of Model Ventures, and NVAngels—an angel network of ex-Nvidia employees.
Ten-Pow said getting buy-in from investors was surprisingly smooth. Many VCs had seen their portfolio companies face this same issue—getting software inside customer environments without lengthy custom deployments.
“There’s real technical complexity under the hood,” Ten-Pow explained. “But we’ve abstracted that away for the customer, which made the pitch resonate.”
The company will use the funding to expand its team and evolve the platform, aiming to support even more verticals in the coming months.
As Ten-Pow puts it, software deployment is entering a new era—not just cloud or on-premise, but wherever the software needs to live. And Tensor9 wants to be the infrastructure powering that shift.
