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NeicroneSubstrate

A real-world validation layer, in development, for taking physical AI from simulation into production.

Designed around instrumented fleets and controlled hazard environments, with a chain-of-custody record from capture to deployment.

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01 / 03
Neicrone Field Unit 06, a tracked ground unit in non-linear terrain
// FIELD UNIT 06

Pushing the limits of physical AI.

A tracked field unit in non-linear terrain: the kind of conditions Substrate is built to capture.

UNIT
06 · Tracked
TERRAIN
Non-linear
HORIZON
Physical AI
Enter the yard
Problem Space
0108

The bottleneck is not the model.

Capability is outpacing deployment. The constraint has moved downstream, into physical evidence.

Edge conditions simulation misses: mud and water, snow and ice, low visibility, mechanical wear and extreme weather, above an instrumented truck streaming LiDAR, radar, camera and IMU data into a real-world dataset
The bottleneckUnresolved

Sim-to-Real Execution Gap

Vision-Language-Action (VLA) models are advancing fast, but hardware deployments fail because real-world physical data is scarce, expensive, and dangerous to harvest in public spaces. Simulations cannot capture non-linear edge conditions like mud, sudden weather shifts, or mechanical wear.

Unmodelled failure modes

  • Non-linear surface conditions

    Mud, standing water, ice transitions

  • Sudden weather shift

    Inside a single trajectory window

  • Mechanical wear drift

    Across actuator duty cycles

  • Public-space harvesting

    Legally constrained, expensive, unsafe

The Neicrone solutionIn development

Neicrone Substrate

A real-world validation layer being built on instrumented commercial fleets and controlled hazard environments, designed to give physical AI the evidence it needs to move from simulation into production.

Target capability
  • Commercial fleets instrumented as edge collection nodes
  • Sensor-instrumented sandboxes for controlled hazard testing
  • Perception, dynamics, and safety checked against real conditions
  • A chain-of-custody record from sensor to training corpus
Target pipeline
  1. CollectReal world & sandbox
  2. ValidateCheck & annotate
  3. CurateChain of custody
  4. TrainBetter models
  5. DeploySafer fleets
Substrate Architecture
0208

One integrated evidence layer.

Neicrone Substrate is not six separate products. It is designed as one real-world validation system, built in stages.

Tracked field robot with a survey drone in alpine terrain01 / 06
FLD-01

Field Operations

Mission orchestration across distributed robotic fleets, designed to bring telemetry, command dispatch, and outcome recording into one operational spine.

IN DEVELOPMENT

Robots / Drones / Fleets / Telemetry / Command / Records

Ground robot in a flooded mud test yard under rain02 / 06
SBX-02

Physical AI Sandboxes

Controlled hazard environments (rain, mud, debris, thermal extremes, mechanical wear). Built to reproduce edge cases before production deployment.

PLANNED

Rain / Mud / Debris / Thermal / Wear / Edge cases

Class 8 truck on a mountain highway with sensor overlays03 / 06
TLM-03

Freight Telematics

Class 8 trucks and autonomous logistics platforms as distributed sensor arrays, intended to supply real-world validation data from long-haul routes.

PLANNED

Trucks / Autonomous / Routes / Telemetry / Validation data

Rotating LiDAR sensor on an edge compute board04 / 06
EDG-04

Edge Sensor Pipelines

Compressed inference at point of capture. LiDAR, radar, vision, IMU. Streams designed to respect bandwidth limits and keep a chain-of-custody record.

NEXT

LiDAR / Radar / Vision / IMU / Compression / Chain of custody

Robotic arm on a hardware-in-the-loop bench in an engineering lab05 / 06
HIL-05

HIL Safety Clearance

Hardware-in-the-loop validation gates, where models are to be tested against real actuators, latency, and failure modes before fleet deployment.

PLANNED

Models / Actuators / Latency / Failure modes / Safety gates

Convoy of autonomous trucks on a highway at sunset06 / 06
LOG-06

Logistics Deployments

Production fleets running under Substrate instrumentation, with evidence from operations feeding back into model retraining.

LONG-TERM

Fleets / Operations / Retraining / Continuous improvement

Swipe · 06 layers

Physical AI / Robotics / Global deploymentReal-world data. Better models. Safer deployments.
Mechanism
0308

Three layers of evidence.

From capture through validation to deployment, the substrate is the continuous thread.

Tracked field unit with camera, LiDAR, thermal and actuator feeds
01// CAPTURE

Physical AI systems operate on real substrate.

Field operations, sandboxes, and logistics fleets generate raw evidence: sensor streams, actuator commands, outcomes, edge cases. Andromeda selects which substrate matters, sensor pipelines compress and prioritize, and every record is designed to carry its chain of custody.

Operator reviewing hardware latency, sensor noise, failure modes and sandbox replays
02// VALIDATE

Evidence flows into safety gates.

Models are tested against real hardware latency, real sensor noise, and real failure modes. Edge cases found in the field are replayed in sandboxes. The model either passes, or the substrate shows why it should not.

Fleet of field units at dusk with a deployment status overlay
03// DEPLOY

Cleared models go into production.

Production fleets are designed to run under continuous Substrate instrumentation. New edge cases and failure modes feed back into retraining and sandbox scenario generation, so the loop keeps closing.

The Infrastructure Gap
0408

Physical AI has a data problem.

Intelligent machines can only become reliable when they can continuously learn from the environments in which they operate.

But physical-world data is fragmented across sensors, machines, simulations and operational systems.

Neicrone is building the infrastructure to connect them.

From realityto intelligenceto safer deployment

An instrumented vehicle and survey drone capture LiDAR, radar, camera, thermal, IMU and GPS data across mountain terrain, combined with weather, terrain, road and hazard context and fleet telemetry into validated evidence
01// PERCEPTION

Machines need to see.

Connect heterogeneous sensors and physical signals into a coherent representation of the environment.

Camera, LiDAR, radar, thermal, IMU and GPS feeds stacked as glass panes over one mountain highway
02// CONTEXT

Machines need to understand.

Combine spatial, temporal and machine-state information to understand what is happening around a system.

Vehicle on a mesh terrain map beside spatial, temporal and machine-state context: location, terrain, weather, season, health, load, performance
03// EVIDENCE

Systems need to learn.

Transform raw observations into structured datasets and operational records that can support evaluation and improvement.

Captured frames stacking into structured datasets, event records, metadata and provenance, model performance and failure cases
04// CONTROL

Physical intelligence needs oversight.

Introduce human review, traceability and operational controls between autonomous systems and consequential real-world actions.

Operator in a control room with human-in-the-loop, audit trail, access controls and safety gates
Spatial Intelligence
0508

Andromeda selects the ground. Substrate instruments it.

Geospatial targeting that knows where edge cases matter most.

Andromeda edge-case intelligence: a globe marking extreme weather, terrain, infrastructure, traffic and hazard hotspots, projected down onto stacked terrain, weather, infrastructure, traffic, hazard and seasonality layers
01THE PROBLEM

Where do edge cases live?

Your model has never seen mud in summer. It has not handled the intersection geometry of this highway. It has not operated in -20°C. You have a global deployment space but a finite collection budget.

Guessing which environments matter is expensive. Sampling everywhere is impossible.

02THE SOLUTION

Andromeda maps substrate priority.

Andromeda is Neicrone's geospatial engine, designed to identify which physical environments matter for your deployment: which geographies carry unseen edge cases, and which terrains, weather patterns, traffic conditions, and hazard classes should lead your collection strategy.

  • Terrain & Surface
  • Weather & Climate
  • Road & Intersection Geometry
  • Traffic & Mobility
  • Hazards & Environment
  • Seasonal Variation

Andromeda selects the ground.
Substrate instruments it.

System Architecture
0608

Edge to cold storage, one path.

Request enters at the edge, resolves through server components, and terminates in a tiered store. No hidden hops.

The request path: a client laptop, server code and an API gateway shield, PostgreSQL, Redis and S3/R2 stores, and a global edge network
01// PRESENTATION

  • Next.js 15, App Router (SSR)

    React 19 · RSC

    Server Components stream the document shell; client islands hydrate only where interaction exists.

  • CSS Compositor Substrate

    GPU layers · No canvas

    Depth fog, reticle field, and grain composited on the GPU. No WebGL context, no runtime shader compile, nothing to fall back from.

02// APPLICATION

  • API Gateway / Server Actions

    Zod · RSC mutations

    Typed mutations validated at the boundary. Partner intake, telemetry queries, and sandbox scheduling share one schema surface.

03// PERSISTENCE

  • PostgreSQL

    Supabase · RLS

    Relational source of truth: partners, fleets, runs, clearance gates. Row-level security scoped per organisation.

  • Redis Telemetry Cache

    Sub-ms read

    Hot window for live fleet state and sandbox occupancy. Written by the edge ingest workers, read by dashboards.

  • Amazon S3 / Cloudflare R2

    Object · Cold

    Raw sensor episodes, point clouds, and video. Content-addressed, immutable, lifecycle-tiered.

EdgeCloudflare anycast · WAF · TLS
TransportHTTPS / WSS · mTLS internal
RegionMulti · Active-active
Retention90d hot · 7y cold
Evidence & Provenance
0708

Chain of custody, by design.

Substrate is being built so that sensor readings, operator decisions, and model outputs carry a verifiable record from capture to deployment.

01

Signed at capture

Sensor Origin

Sensor readings are designed to be timestamped, located, and signed as they are ingested, so later changes can be detected.

02

Logged with context

Operator Intent

Human decisions are recorded alongside who made them, when, and why, including the reason for any override.

03

Append-only

System State

Model outputs and confidence scores are written to an append-only log, so a run can be traced back to the inputs behind it.

04

Built for review

Compliance Path

Evidence is structured to support safety assessment work such as ISO 26262 and IEC 61508. It supports a certification case; it is not a certification.

A sensor-equipped car and drone feed a record that passes through sensor origin, operator intent and system state cards into a hash-chained, append-only log, forming a verifiable audit trail

Hash-Chained Records

Each record is designed to reference a hash of the one before it, so an edit after the fact breaks the chain and shows up on verification.

Independent Logs

Sensor data, operator input, and model output are logged separately, so one record can be checked against the others.

Evidence at Capture

The record is built as events happen rather than reconstructed afterwards, so reviewers see what the system actually saw.

// STATUS: DESIGN SPECIFICATION · IN DEVELOPMENT
Trust / Safety / Compliance
Substrate Access
0808

Build your substrate.

Request access to Neicrone Substrate. We assess your deployment context, validate your edge cases, and instrument your fleets.

A partner workstation submits a substrate access request that passes application, review and onboarding into a stack serving fleets, telemetry, models and evidence

Secure Access

Controlled onboarding with security review and compliance alignment.

Validated Deployment

We assess your environment, edge cases, and technical requirements.

Instrumented Fleets

Telemetry, observability, and auditability designed in from the first engagement.

Clear Path to Access

From request to access through a defined, transparent review process.

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