What You Should Know
- Baltimore-based Inner Logic (formerly Semaphor) has secured an $11.5M seed round co-led by General Catalyst and Bison Ventures, with participation from J2VP, Defined, PTX, Valia Ventures, Page One, Alumni Ventures, and Flare.
- Replaces costly physical cadaver labs and animal testing by giving device manufacturers a virtual testing environment to design, iterate, and validate procedural devices across thousands of virtual patient anatomical models overnight.
- Provides the physics-based computer vision and physical AI simulation layer necessary to train, test, and deploy fully autonomous and intelligent surgical systems safely before clinical deployment.
- Co-founded by CEO Tito Porras, MD (former Johns Hopkins neurosurgery resident), CTO Mathias Unberath, PhD (Johns Hopkins computer vision expert), and Chief Robotics Officer Axel Krieger, PhD (pioneer behind the first autonomous laparoscopic soft-tissue surgery).
- Successfully trained AI algorithms entirely in simulation for real-hardware orthopedic trauma deployment, as well as performing autonomous gallbladder removal steps in a live animal model without human surgeon intervention.
In Silico Surgical Autonomy
The medical device manufacturing, surgical robotics, and procedural technology sectors are confronting an acute R&D bottleneck. For decades, surgical device innovation—from interventional catheters and heart valves to robotic arms—has relied almost entirely on physical trial and error. Engineering teams construct physical prototypes, test them in cadaver labs or animal models, and evaluate performance one costly experiment at a time.
This physical testing cycle is inherently flawed. Cadaver and animal studies are prohibitively expensive, logistically complex, and represent only a narrow slice of the vast anatomical variations, tissue pathologies, and edge-case failure scenarios encountered in real-world operating rooms.
Moreover, as surgical devices shift from passive mechanical tools to intelligent, autonomous systems, relying solely on physical testing becomes mathematically impossible—the range of operational parameters is too broad for physical labs alone to validate.
To eliminate physical prototyping constraints and build the foundational data infrastructure for procedural autonomy, Inner Logic (formerly Semaphor), allows device manufacturers to design and validate physical and robotic tools across thousands of virtual patients grounded in real clinical data.
Physical AI and Physics-Based Simulation
Inner Logic’s platform creates an in silico intelligence layer that mirrors the simulation frameworks that enabled autonomous vehicle navigation:
- Anatomical Synthetic Population Engine: Generates high-fidelity virtual patients built from real clinical imaging and procedural datasets, ensuring simulation results hold true in physical clinical settings.
- Physics-Based Sensor & Computer Vision Modeling: Simulates real-time tissue deformation, anatomical variation, and imaging conditions to train computer vision models without human annotation bottlenecks.
- Orthopedic & Soft-Tissue Autonomy Proofs: Validated end-to-end by training orthopedic AI models entirely in simulation for real hardware deployment, alongside autonomous laparoscopic gallbladder removal in live animal models.
- Multi-Patent Intellectual Property: Developed by a founding team holding over 40 patents and 400 peer-reviewed publications across surgical computer vision, robotics, and physical AI.
“Surgery is on the same path as autonomous driving, which became real once the industry built the simulation and data systems to show a car could drive itself,” stated Tito Porras, MD, Co-Founder and CEO of Inner Logic. “At Inner Logic, we are building that layer for all of procedural medicine starting with the way devices are designed and tested. When device makers can develop and prove intelligent systems this way, the quality of a patient’s care stops depending on which hospital they can access or which surgeon is on call.”
As Inner Logic rolls out its development infrastructure across medical device manufacturers, it establishes a decisive operational foundation to shift surgery from a manual craft into an automated, auditable, and accessible science.
