Deepen AI Raises Seed II Funding Led by Majlis Advisory to Scale Autonomous Systems Data Infrastructure
Deepen AI, a Santa Clara-based startup building data infrastructure for autonomous systems and robotics, has raised a new seed funding round led by Majlis Advisory, as the company accelerates its mission to become a core “physical AI” data and safety execution platform for real-world autonomy.
The company’s latest financing, completed in early 2026, marks its Seed II round and supports expansion of its sensor-fusion data infrastructure, calibration systems, and validation tools used in safety-critical AI applications such as autonomous vehicles and robotics. Deepen AI focuses on what it describes as “ground truth” data for multimodal AI systems, helping teams ensure accuracy across LiDAR, camera, radar, and other sensor inputs used in autonomous environments.
Founded in 2017 by Mohammad Musa, Deepen AI operates in the rapidly growing autonomy stack, providing data lifecycle tools that include annotation, calibration, simulation validation, and safety verification. The company’s software is designed to reduce errors in machine learning datasets, which can lead to costly delays or failures in autonomous system deployment.
The latest round, led by Majlis Advisory, reflects growing investor interest in “physical AI” infrastructure—technologies that support robotics, autonomous driving systems, and real-world machine learning applications. Majlis Advisory’s participation is strategic, with access to capital networks across the Middle East and broader institutional channels, particularly in sectors tied to automotive, logistics, and sovereign AI initiatives.
Deepen AI has not publicly disclosed the full list of additional equity investors in the Seed II round, but historical backers and ecosystem participants include angel investors from the AI and robotics community, along with accelerator participation from programs such as Plug and Play. The company has raised approximately $100,000 across earlier rounds, though its Seed II financing represents a more structured institutional entry point into its growth phase.
The company’s platform is designed for engineering teams developing advanced driver-assistance systems (ADAS), autonomous vehicles, robotics, and multi-sensor AI stacks. Deepen AI’s tools are used to validate datasets and ensure consistency across large-scale training pipelines, which is increasingly critical as modern AI systems move from simulation environments into real-world deployment.
A key focus of the new funding is scaling Deepen AI’s production capabilities and expanding its go-to-market strategy as demand increases for high-integrity datasets in autonomy. The company is positioning itself as a foundational layer for “Physical AI,” a category describing AI systems that interact directly with the physical world rather than operating purely in digital environments.
Deepen AI also benefits from strong technical leadership. Founder Mohammad Musa has a background spanning AI, computer vision, and large-scale machine learning systems, and the company has attracted engineers and researchers with experience in autonomous driving, robotics, and sensor fusion.
The startup operates in a competitive but expanding sector alongside companies building data labeling, simulation, and validation infrastructure for autonomous systems. As the robotics and self-driving industries mature, the need for reliable ground-truth data is becoming a key bottleneck, particularly as Vision-Language-Action (VLA) models and multimodal AI systems require increasingly complex training inputs.
With backing from Majlis Advisory, Deepen AI plans to expand delivery capacity, improve its data validation tools, and strengthen enterprise adoption across automotive and robotics customers. The company is also expected to deepen partnerships with organizations deploying safety-critical AI systems at scale.
As autonomous systems move from pilot programs into commercial deployment across transportation, logistics, and industrial robotics, Deepen AI is positioning itself as a core infrastructure provider for ensuring data integrity, safety validation, and multimodal calibration across the physical AI ecosystem.
