Early-stage deep-tech startup

Distributed AI compute
for a global node mesh

NeuroMesh AI is building a secure, observable orchestration layer that enables AI workloads to run across a distributed network of agent machines. We’re at the beginning of our journey — focusing on core architecture, reliability, and security.

Agent runtime controlled execution & sandboxing

Control plane scheduling, routing, retries

Integrity verifiable outputs & audits

Telemetry health, latency, throughput

Today: prototype → private pilots → early network growth

Focus: reliability & security before scale

Mesh Operations Dashboardpreview telemetry

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Tasks / min

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Median latency

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Throughput

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Online / Degraded / Offline

Node status

online degraded offline

Node	Region	Status	Latency	Load	Agent

This dashboard is a live-style preview with realistic node behavior (churn, load spikes, recovery). In production, telemetry is backed by signed metrics and audit logs.

What we’re building

A distributed execution fabric that feels like a single system: job scheduling, agent coordination, secure runtime, and observability built-in from day one.

Agent Runtime

Node software installed on partner/agent PCs — controlled execution, scheduling hooks, and health reporting.

Hardware-aware worker with resource policies

Secure task isolation & deterministic replay

Signed updates and strict versioning

Control Plane

Resilient orchestration that routes workloads, balances capacity, and keeps pipelines reliable under failure.

Scheduling, queues, backpressure & retry strategy

Region-aware placement & latency optimization

Policy-driven routing and safe execution

Observability & Integrity

First-class visibility: per-node health, job traces, performance, and verifiable results.

Metrics for latency, throughput, and saturation

Audit logs and job provenance

Result validation & anomaly detection

Roadmap (early stage)

We build the “hard parts” first — security, reliability, and observability — before rapid scale.

Phase 1 — FoundationsNow

Agent runtime prototype, control plane skeleton, telemetry pipeline, and security model definitions.

Phase 2 — Private pilotsNext

Limited rollout with partners: stability testing, scheduling validation, and operational tooling.

Phase 3 — Reliability at scaleThen

Hardening, automated recovery, region-aware routing, and strong observability across node fleets.

Phase 4 — Network expansionLater

Broader onboarding and production-grade workload orchestration for AI pipelines.