A reusable AI platform for measuring, guiding, and improving expert tool-mediated procedures. We turn video, motion, and outcome data into expert-grounded performance benchmarks.
In expert tool-mediated procedures, the final result matters — but it rarely shows the execution path that produced it. FinePoint shows how it happened — and what to improve next.
Critical signals happen during the procedure — in movement, tool use, sequence, retries, and corrections.
Expert coaching is valuable, but difficult to make consistent, repeatable, and trackable.
Teams need evidence that connects execution quality, feedback, and outcome improvement.
We turn expert procedure data into structured feedback, performance benchmarks, and improvement tools.
A focused product for measuring, guiding, and improving surgical simulation performance. The product compares each session against expert references and turns video, motion, task, and outcome data into specific feedback on movement quality, tool handling, step sequence, tissue interaction, and result quality.
Time, distance, path length, and task-completion patterns.
Smoothness, steadiness, acceleration, vibration, and abrupt motion.
Whether key steps are performed in the right order and at the right procedural checkpoints.
Reloads, repeated attempts, adjustments, hesitations, and recovery moments.
Deviation from expert references, accepted performance ranges, and procedure-specific standards.
How execution patterns relate to visible result quality, such as suture spacing, closure quality, marking accuracy, or symmetry.
Measures movement efficiency, smoothness, consistency, reloads, retries, corrections, expert deviation, and visible result quality.
Evaluates marking accuracy, procedural sequence, dissection steps, closure quality, tool-tissue interaction, intermediate checkpoints, and final symmetry.
The same platform can support device training, robotics benchmarking, and regulated procedural operations where expert execution, step correctness, and outcome quality must be measured and improved.
Measures movement quality, tool handling, consistency, retries, corrections, and visible result quality.
Evaluates marking accuracy, step sequence, dissection quality, closure quality, tool-tissue interaction, and final symmetry.
Extends the same platform architecture to procedures where expert execution, step correctness, and outcome quality must be measured and improved.
We work with early partners to configure and validate one defined surgical simulation workflow — such as suturing or cleft lip repair simulation — using available expert examples, learner sessions, and outcome data.
A 60–90 day pilot produces a working benchmark module, targeted feedback outputs, and evidence of how procedural performance can improve.
FinePoint is being developed with clinical and nonprofit partners working on real surgical training needs — from foundational suturing skills to complex multi-phase procedure simulation.
FinePoint's first deployment supports surgical residents practicing surgical sutures on a 3D-printed simulator, paired with an iOS training app and internal review platform.
The system captures user sessions, tracks performance over time, supports expert review, and delivers feedback on movement quality, consistency, retries, and outcome quality.
FinePoint is expanding into multi-phase surgical procedure training with Global Smile Foundation, focused on unilateral cleft lip repair simulation.
The work extends beyond dexterity to evaluate marking accuracy, procedural sequence, dissection steps, closure quality, tool-tissue interaction, and final symmetry.
We work with surgical training and simulation partners to configure one defined procedure — such as suturing or cleft lip repair simulation — into a repeatable benchmark and improvement workflow. A focused pilot produces procedure maps, expert-reference criteria, performance reports, targeted feedback, and progress tracking.