Investor room access

Full investment documentation →
01 The market

The autonomous machine
market needs sensing
that understands.

Every autonomous vehicle, industrial robot, and smart infrastructure system requires sensing that produces causal, actionable scene comprehension — not point clouds that AI has to reconstruct meaning from after the fact. No deployed system does this today.

LiDAR market by 2030
$9.4B
Automotive LiDAR alone. Industrial and infrastructure sensing adds a further $4–6B. Both markets blocked by the same unsolved problem.
Tier-1 spend on sensing R&D
$2.1B
Annual Tier-1 automotive supplier investment in sensing and perception. Most of it is incremental improvement on broken architectures.
Deployment timeline
2027–29
Window for sensing platform decisions in the next automotive generation. Tier-1 supplier selection cycles run 18–36 months ahead of production.

Why the market is still open

Every major LiDAR program of the last decade — Velodyne, Luminar, Innoviz, Ouster — was built on the same architectural assumption: sense first, understand later. The sensor produces a point cloud. The AI receives it and tries to reconstruct meaning.

This is not an engineering problem that better components can solve. It is an architectural mismatch that requires a different kind of sensor — one designed from the beginning to produce causally structured data that intelligence can reason from directly.

No deployed system does this. The market is open not because the problem is new but because solving it requires a rare convergence of deep optical physics, device engineering, and causal AI — disciplines that have seldom sat in one place until now.

What Tier-1 customers actually need

Requirement 01
No moving parts
Automotive qualification requires MTBF > 15,000 hours. No rotating mirror or MEMS LiDAR has passed this bar in production volume.
Requirement 02
Cost at automotive scale
Target: under $200 per unit at volume. Current solid-state LiDAR costs $800–2,000. The architecture must change, not the yield.
Requirement 03
Full 360° with no gaps
Multi-sensor fusion to cover blind spots adds latency, cost, and calibration complexity. A single-module 360° solution has never been achieved without rotation.
Requirement 04
On-device intelligence
Cloud-dependent perception is a safety liability. Edge processing at the sensor is a hard requirement for life-critical deployment.
// The opportunity

The autonomous vehicle, robotics, and industrial autonomy industries are converging on a single architectural requirement: a perceptual system that produces structured, causally-reasoned representations of the physical world — not distance maps.

Current LiDAR produces point clouds. Current AI produces statistical inferences. Neither is sufficient for life-critical autonomous systems at scale. The gap between what exists and what is required is the market SciPhAI is built to serve.

Tier-1 automotive platform decisions for 2027–2029 deployment are being made now. The architecture committed in this cycle shapes the sensing IP landscape for the next decade.

The sensing gap

Every commercial LiDAR today produces a point cloud. Our architecture produces a structured, causally organized representation instead. The difference is between a distance map and a causal understanding of the physical environment.

The intelligence gap

Statistical AI degrades unpredictably in novel environments. Causal analytical intelligence degrades gracefully. For mission-critical applications this is not a performance preference — it is a liability threshold.

The timing window

Platform decisions for 2027–2029 autonomous systems deployment are being made now. The architecture committed today defines the IP landscape for the next decade.

// What you are investing in

IP

20 issued US patents covering the foundational AI framework. Two major families pending — the causal-intelligence framework (priority July 2019) and the sensing architecture (priority October 2024). International filings in progress.

Technology

A sensing architecture and a causal-intelligence layer, co-designed from the physics of the problem. The sensor produces exactly what the intelligence needs; the intelligence reasons from exactly what the sensor knows.

Founder

30 years of preparation in photonics, knowledge engineering, and autonomous intelligence. $66M raised across two prior photonic companies. The domain authority that makes this architecture possible belongs to one person.

Timing

Physical AI named as a category in January 2025. SciPhAI filed in 2019. Tier-1 platform decisions for the next automotive generation are being made now — the cycle that sets the sensing IP landscape for the decade.

// Investor access

Request an investor briefing

A one-hour session with us covering the technology, the IP position, and the market opportunity. Full investment documentation is available in the investor room after the briefing.

No automated responses. We reads every request personally. Expect a reply within 48 hours.

Briefing request

Request a technical briefing

A one-hour session with us covering the technology, the IP position, the market opportunity, and the round terms. Pitch deck and business plan available under NDA after the briefing.

No automated responses. we reads every request personally. Expect a reply within 48 hours.

Request received

Thank you. We will be in touch within 48 hours to schedule a time that works. The pitch deck and business plan are available under NDA after your first conversation.

If you have already had a briefing and have login credentials, the full investment documentation is available in the investor room →

SciPhAI is open to investment conversations with the right partners. If that includes you, we would like to talk.

Request an investor briefing →