Use Cases
Where fixed automation falls short
Neuralzome targets tasks that resist conventional automation, where variation, occlusion, and changing conditions break fixed programs within weeks.
Applications
Three uncertain operating environments
All three share one problem: reality diverges from the program, while traditional automation needs offline intervention to adapt.
High-mix lines that outpace reprogramming
High-mix assembly cells cycle through fixture changes, new variants, and engineering revisions faster than traditional robot programs can be updated. Neuralzome learns intent from demonstrations and adjusts as variants change, without an offline reprogramming session.
60% less changeover downtime across 2024-2025 early-access pilot deployments
Unstructured bin picking at commercial throughput
Clutter, overlap, and shifting light make warehouse picking a difficult partial-observability problem. The Neuralzome perception bridge identifies what the robot can see, quantifies what it cannot, and acts within its confidence bounds.
94% pick success in clutter, measured during our early-access program
Defect detection beyond the camera's view
Fixed-threshold inspection misses its own uncertainty. Neuralzome tracks prediction confidence with defect probability and sends uncertain parts for human review.
3x lower false reject rate in 2025 pilot benchmarks
Deep Dive: Assembly
Flexible assembly, in detail
High-mix assembly cells cycle through four to eight fixture variants in a given month. Historically, each change required offline reprogramming, keeping a process engineer occupied while the cell stood idle.
The challenge
Challenges include part-position variation inside the grasp envelope, short-notice fixture revisions, and contact-dependent sequencing. Fixed programs may complete the motion yet miss the part, leaving the defect to appear downstream.How Neuralzome handles it
The decision runtime reads demonstration intent rather than joint trajectories. Variant changes need minimal re-demonstration. Confidence-gated grasp planning holds and alerts when part position lacks the required precision.Handles position variation in the grasp envelope
Probabilistic grasp planning accounts for uncertain part pose instead of assuming deterministic fixture placement.
Adjusts to fixtures with minimal re-demonstration
Policy transfer between fixture variants occurs in the runtime, without recording complete trajectories from scratch.
Sequences steps using contact detection
Force-torque integration confirms contact-dependent sub-assembly steps instead of relying on time-gated motion primitives.
Deep Dive: Picking
Warehouse picking, in detail
Warehouse picking combines depth sensors, RGB cameras, and conveyor weight confirmation across three environments. Each signal is noisy, so the decision layer must fuse them and meet the upstream conveyor's latency budget.
The challenge
Stacked items cause occlusion, specular surfaces confuse depth sensors, and lighting shifts by three stops between morning and afternoon. As SKU mix changes, fixed-threshold success rates decline within weeks.How Neuralzome handles it
The perception bridge scores grasp candidates with uncertainty. Low-confidence candidates prompt repositioning and rescanning instead of a speculative grasp. Edge inference keeps decision latency under 50ms without a cloud round-trip.94% pick success in clutter
Measured in high-occlusion bins with mixed SKU geometry, rather than idealized single-layer layouts.
Sub-50ms edge decision latency
Inference runs on compact edge hardware beside the robot controller, removing cloud latency from the critical path.