// NEICRONE FIELD HARDWARE · ACTIVE UNIT

Pushing the limits of physical AI.

Instrumented field units that log real-world sensor, dynamics, and safety data so autonomous systems ship on evidence, not simulation.

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// BOOT COMPLETE

// ALL FIELD OPS NOMINAL

Neicrone field sensor pod mounted on an autonomous ground unit, quarry site

06Lines nominal

// EDGE PIPELINE

// FIELD OPS SYNC

// STANDBY

// System Core

Every deployment starts as a system you can enter.

Live render

STANDBY

// ZOOM100%

// VIEW

// READ

// HOVER A COMPONENT · SCROLL TO ENTER

// Problem Space

0105

The bottleneck is not the model.

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

// 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 SOLUTIONRESOLVED

Field Infrastructure Platform

We convert live commercial logistics fleets into edge-data collection nodes and provide secure, sensor-instrumented physical sandboxes so robotics OEMs can validate perception, dynamics, and safety in real-world conditions.

// DELIVERED CAPABILITY

Live commercial fleets converted into edge collection nodes
Sensor-instrumented physical sandboxes under controlled hazard
Perception, dynamics, and safety validated on real substrate
Chain-of-custody telemetry from sensor to training corpus

// Field Ops

0205

Six lines, one substrate.

Every product line writes into the same spatial timeline. Nothing is siloed; nothing is simulated.

FLD-01

Neicrone Field Ops

Crewed and autonomous teams that instrument, run, and log real-world trials. Standardised run sheets, incident capture, and chain-of-custody on every episode.

ACTIVE

SBX-02

Physical AI Sandboxes

Secured, sensor-saturated environments with reproducible hazard conditions: surface, weather, occlusion, and load.

ACTIVE

TLM-03

Freight Telematics Engine

Fleet-scale ingest of CAN bus, GNSS, IMU, and payload state, normalised into a single queryable spatial timeline.

ACTIVE

EDG-04

Edge Sensor Pipelines

On-vehicle capture, compression, and selective upload. Bandwidth-aware triage keeps the rare event and discards the rest.

ACTIVE

HIL-05

Hardware-in-the-Loop Safety Clearance

Staged clearance from bench HITL to supervised public operation, with an auditable evidence trail at every gate.

CERTIFIED

LOG-06

Active Logistics Deployments

Production integrations inside working freight lanes, warehouses, and yards. Revenue operations, not staged pilots.

SCALING

// The Infrastructure Gap

0305

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.

01

// PERCEPTION

Machines need to see.

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

02

// CONTEXT

Machines need to understand.

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

03

// EVIDENCE

Systems need to learn.

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

04

// CONTROL

Physical intelligence needs oversight.

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

// System Architecture

0405

Edge to cold storage, one path.

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

// STACK TOPOLOGY v15.3

04Tiers reporting

T0

// EDGE / INGRESS

Cloudflare Edge CDN / WAF

ANYCAST · 300+ POP

TLS termination, bot mitigation, and rate limiting at the edge. Static and ISR payloads served from cache before origin is consulted.

T1

// 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.

T2

// 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.

T3

// 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.

// TRANSPORT: HTTPS / WSS · MTLS INTERNAL// REGION: MULTI · ACTIVE-ACTIVE// RETENTION: 90D HOT · 7Y COLD

// Partner Intake

0505

Request sandbox access.

Intake is reviewed by Field Ops. Clearance is staged, and bench HITL precedes any supervised operation on live infrastructure.