Train in the cloud, decide at the edge

Real-time AI will reshape the physical world.

But, the cloud can't run it.

Physical systems need 20–30 ms decisions to stay safe, efficient, and autonomous.

eg. vehicles, factories, grids, ports, cities

But cloud averages +100s ms, burning power and budget sending raw data 100 to 1,500 miles away.

The world needs a 20 ms, sustainable edge layer – and a way to manage millions of nodes and models as a single, easy-to-consume fabric.

Nectar is building that layer:
a real-time AI fabric for the physical world.

The Box solves physics. Brain solves complexity.

The Box

‍A compact, immersion-cooled, GPU-dense node that lives at towers, PoPs, campuses, and factory floors.

The Brain

A control layer with predictive AI caching that learns where and when each model and job goes, so developers don’t play tower Tetris.

Faster

20–30 ms latency device → decision, where data is generated.

Greener

25% less power + carbon; radically energy and space-efficient.

Cheaper

30-40% lower TCO than cloud/DIY in telco and private 5G scenarios.

Together, customers quickly deploy, operate, and monetize AI at the network edge - while increasing profit and sustainability.

Inference where data is generated
opens more bandwidth and cloud GPUs for model training.

Cloud AI factories train and evolve models; Nectar runs them where milliseconds and watts matter most.

Backed by the leaders in AI infra

Help define the real-time AI fabric for the physical world with us.

Telecoms + towers co's

Pilot a Box at a tower or PoP, measure 20–30 ms latency on real workloads, and explore new monetization models at the edge.

Manufacturing + logistics

Run inspection, safety, and autonomy workloads on-site with deterministic 20–30 ms decisions – without building your own edge stack.

OEMs + enterprise researchers

Co-design the standard 20 ms node and fabric APIs for telcos, integrators, and enterprises deploying real-time AI in the field.

Investors + partners

Align on the new economics of real-time AI – and how a distributed 20 ms fabric becomes the missing infrastructure layer of this shift.

Join the revolution.

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Train in the cloud – decide at the edge.

The robot autonomy ceiling.

Three forces are converging to break today’s robot infrastructure.

Models are exploding

Transformer-based perception and planning models (RT-2, Octo, π₀) need 10–100× more FLOPs than legacy stacks.

Data is flooding

Every robot streams 10–100+ Mbps of sensor data. Cloud round-trips add 80–200 ms of jitter — too slow for real-time control.

Costs are compounding

Per-robot GPU stacks don’t amortize. Fleet operators face rising CapEx with every unit deployed.

Why now?

Three forces are converging.

Foundation models hit physical tasks

RT-2, Octo, π₀ and other transformer-based models finally give robots human-level perception and planning — but they need 10–100× more FLOPs than legacy stacks.

Fleets are deploying at scale

Amazon, DHL, and dozens of startups are moving from pilots to 100+ unit rollouts. Each robot adds 10–100 Mbps of sensor data and model demand.

The infra layer is missing

Cloud adds jitter. Per-robot GPUs don’t amortize. There’s no shared, on-site AI layer purpose-built for fleets.

On-site AI infrastructure, purpose-built for robot fleets.

One box. Shared by the fleet. Managed end-to-end.

The Nectar Box

A compact, liquid-cooled GPU appliance that sits on-site and delivers <20 ms p95 inference to every robot on the floor.

Nectar Brain

A fleet orchestration layer that routes models, balances load, and continuously improves — so operators manage one system, not N robots.

Dual-use architecture

Real-time inference during shifts. Off-shift GPU cycles feed back into model training — no extra hardware needed.

Safety-first design

Hardware-level isolation, encrypted model storage, and a locked-down runtime — designed for environments where uptime is non-negotiable.