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AI Assisted Failure Mode Discovery & Prevention Across Subsystems
A precision surgical robotics manufacturer developing a multi arm laparoscopic robotic surgery console with haptic feedback and stereoscopic visualization.
Runs onTatvam- late stage prototype design redesigns reduced
- 70%late stage prototype design redesigns reduced
- accelerated DFMEA authoring velocity
- 3.5xaccelerated DFMEA authoring velocity
How the work runs
The pressure that made this worth automating, the steps the system runs, and what came out the other side.
Pressure & Trigger Points
- Engineers struggled to anticipate complex electro mechanical failure modes arising from subtle thermal expansion, micro cable friction, and motor controller jitter.
- Historical lessons learned from previous surgical robotic platforms remained locked in PDF post market surveillance reports and retired engineer notebooks.
- An unpredicted cable tension fatigue failure occurred during late stage animal lab trials, requiring an expensive 6 month mechanical redesign.
The run · 5 operational steps
Click any step to inspect telemetry signals, model reasoning, and governance gates.
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AI Assisted Failure Mode Discovery Engine
Tatvam analyzes CAD assemblies and subsystem function trees, suggesting potential mechanical, electrical, and software failure modes automatically.
Real-time operational telemetry & queue
MCP grounded vector inference
Policy constrained with audit write-back
Verified Business Outcomes
- Uncovered 45 critical latent failure modes during early CAD modeling prior to physical prototype fabrication.
- Late stage prototype design redesigns reduced by 70%, saving $1.2M in engineering rework costs.
- Accelerated DFMEA authoring velocity by 3.5x using AI assisted failure mode suggestions.
Capabilities this relies on
- document intelligence
- risk scoring
- generative design
- evidence audit trail
- policy compliance
- human approval
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