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 specific convergence of optical soliton physics, waveguide engineering, and causal AI theory that has not existed 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.
02 The moat

Fifteen years of preparation.
Two patent families. One team.

The moat is not a single patent. It is the convergence of a specific scientific background, a decade of AI patent filings, and a sensing architecture that can only be designed by someone who spent thirty years working at the intersection of optical physics and machine intelligence. That convergence is not replicable on a three-year startup timeline.

The IP position

Patent family 01 · Jul 2019
US 20222245109 A1 — State Navigation
The complete causal intelligence framework. Participation matrix PM^kl, association strength measures, Causal Association Strength Matrix, rational state navigation. LiDAR, cameras, and radar named as sensory inputs. Filed before the category had a name.
Patent family 02 · Oct 2024
US 2026/0056318 A1 — ISS Platform
The hardware the theory was waiting for. Coded Propagation-Steered Illumination (CPSI), TDE primary mode, CFSRP secondary mode, single omnidirectional collector, participation matrix as native sensor output. CIP of PCT/CA2020/051000.
Prior art · 2008–2019
~20 issued AI and knowledge framework patents
The mathematical substrate — conditional occurrence probabilities, participation matrices, rational navigation under uncertainty — built over a decade before the synthesis became possible.

The framework for what is now called Physical AI has been in development here since 2019. The hardware that serves it was filed in 2024. The preparation is the moat.

The founder

Hamid Hatami-Hanza — PhD Electrical Engineering, UNSW Sydney. Thirty years at the intersection of optical physics and machine intelligence.

Founded Peleton Photonics ($22M raised) and Zenastra Photonics ($44M raised) — two photonic communications companies built on his optical soliton research. Twenty issued AI patents filed between 2008 and 2019, covering the causal intelligence framework that now underpins State Navigation.

This is not a first-time founder applying AI to hardware. It is the person who spent thirty years developing the theory and the hardware converging them into one system.

The preparation timeline

2008 — 2018
~20 issued AI patents. Causal intelligence framework, participation matrix, association strength measures. Built over a decade before the synthesis.
July 2019 ★
State Navigation filed — US 20222245109 A1. Complete causal AI theory. LiDAR named as the primary sensory input.
October 2024 ★
ISS Platform filed — US 2026/0056318 A1. CPSI architecture, TDE/CFSRP, single omnidirectional collector, PM^kl as native output.
January 2025
The category receives its name. The work that led here began sixteen years earlier.
2026 — Now
Seed round open. $6M. Prototype under construction. Tier-1 conversations active. International PCT filings in progress.
03 The ask

Seed round — $6M

$6M
Seed round · 2026
Use of funds
40%
Prototype & testing
Complete working CPSI prototype. Lab measurements. TDE disambiguation validation. Participation matrix real-time processing.
25%
IP & international filings
PCT regional entries EP (urgent), JP, KR, CN. GAP-07 D-fiber continuation. GAP-08 per-aperture collimation claim.
20%
Team
Optical engineer, embedded systems engineer, automotive systems specialist. 18-month runway to Series A milestone.
15%
Tier-1 engagement
NDA-stage technical demonstrations. Pilot program development. Automotive qualification groundwork.
Series A milestone: working prototype + one signed Tier-1 LOI

What you are investing in

IP
Two granted patent families. ~20 prior issued patents. International filings in progress.
Technology
CPSI sensing architecture. State Navigation intelligence framework. Both designed together.
Founder
30 years preparation. $66M raised across two prior photonic companies. Domain authority no one else has.
Investor access

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