Microsecond-class reflex intelligence for embodied AI.
Production AV (autonomous vehicle) stacks miss pedestrians at highway speed because their reflex loop runs in tens of milliseconds. Our reflex kernel runs in 4.23 microseconds (measured on an A100 GPU).
A deterministic reflex layer that sits beneath the perception stack — designed to catch what the network misses, in time to brake.
A product line of Univault Technologies. Currently in active discussions with Tier-1 automotive suppliers. Series A allocation closing.
Why this matters
Modern AV perception is brilliant at understanding scenes, and slow at the moment when speed is the only thing that matters. A car at 25 m/s travels half a meter in 20 milliseconds. The pedestrian is already in the lane.
Cross-architecture validation
We evaluated the same reflex layer against three independently developed, real perception detectors on public AV benchmarks. It recovers a meaningful share of the hazards each one misses. The catch rate tracks each detector’s blind spots — 86.77% on LION-Mamba, 64.6% on CenterPoint, 59.2% on BEVFusion. The point is that the reflex layer adds a recovery margin on top of every detector we tested, not just one.
Network-miss catch rate
nuScenes · n=127 network misses · evaluated on Modal A100-80GB. Signed claim.
Network-miss catch rate
nuScenes · n=238 network misses · same reflex configuration · Modal A100-80GB. Signed claim.
Why this matters for the OEM (original equipment manufacturer). We tested the same reflex layer against three independent real detectors — LION-Mamba, CenterPoint, and BEVFusion. It recovers a meaningful share of the hazards each one misses (59–87%, tracking each detector’s blind spots). A safety layer that only helps one perception stack is a coupled dependency; one that adds a recovery margin on top of whichever detector the OEM chose is a portable safety floor — because the OEM gets to keep choosing.
What we’ve validated
Public benchmark, public dataset, production cloud GPU evaluation — the results we walk Tier-1 partners through line by line.
p50 reflex latency
Median reflex-kernel latency, measured on Modal A100-80GB. Orders of magnitude faster than a learned perception inference loop. Automotive-runtime port is the next funded milestone.
of a 2024 SOTA detector’s misses · signed
Reflex recovers 86.77% of the hazards LION-Mamba (2024 SOTA) misses across 6 Argoverse 2 cities (full 0–30m forward cone, 2% FPR (false-positive rate), n=1,368, 95% CI (confidence interval) 84.9–88.5%). Ed25519-signed, reproducible offline.
Cross-architecture, cross-dataset
Validated against three real detectors (LION-Mamba, CenterPoint, BEVFusion) across Argoverse 2 (6 cities) and nuScenes (Boston, Singapore). Catch rate 59–87%, by detector.
Modeled scenario (not a signed claim): paired with a production-grade base detector at 85% TPR (true-positive rate) / 2% FPR, the cascade reaches 94.5% TPR at 4.6% FPR, combining measured Reflex with a modeled production NN (neural network). Per-region tables, stopping-distance numbers, and the full evaluation walk-through are part of our Tier-1 partner conversations.
Watch the demo →Open roles
We’re hiring deliberately. Every seat below either ships the AV product or unlocks a Tier-1 conversation. If you’ve worked on production automotive safety, we want to hear from you.
Port the reflex layer to production-grade automotive runtime. AUTOSAR Adaptive + SIMD. ASIL-D-track project experience required.
Author the Type-2 SEooC dossier. TÜV SÜD, Exida, or independent OEM-functional-safety background.
Open Tier-1 supplier relationships (Bosch, Continental, Magna). Move us from cold to NDA to paid pilot.
Ex-Mobileye / Bosch / Continental / Magna director, recently between roles. 4-8 hr / month, equity.
Tier-1 supplier, OEM, robotics platform, eVTOL (electric vertical take-off and landing) program — if your roadmap depends on a microsecond-class safety floor, we should be talking.