Every AI company is paying for computers that spend a third of their time doing nothing.
We get those computers back to work.
Four thousand chefs are cooking one enormous meal together. Each costs a hundred dollars an hour. The kitchen costs four hundred thousand dollars an hour to operate.
But the recipe requires constant coordination—blending sauces, passing ingredients, syncing timing. Every few seconds, all four thousand chefs stop chopping and crowd the hallway to hand things to each other.
The hallway traffic takes a third of the workday.
Multiply across every AI company on Earth—$400 billion of computers over three years—
The software that tells the chefs when and how to coordinate was written eight years ago, when kitchens were smaller and simpler.
It makes one plan at the start of the workday and never changes it. Meanwhile, the kitchen has grown ten times bigger.
It is a fast car
with the steering locked.
XportL is the traffic cop. We watch the hallway, predict the jams, and reroute the chefs—every millisecond, automatically.
Thousands of chefs trying to cross a hallway that wasn't built for them. Bottlenecks. Backed-up queues. Routes the coordinator never updates.
We watch traffic, predict jams, reshape routing. The hallway flows again.
One chef is moving at three-quarter speed. Not sick — not gone — just slow. Every other chef in the kitchen waits at the every coordination point for the slow one.
The whole kitchen slows down to match. NVIDIA's own tools say "keep running" — they can't tell.
One telemetry pipeline. Two distinct recoveries. The same intelligence layer that solves congestion solves fail-slow — and tomorrow it solves more.
We install a small piece of software that listens to every interaction between computers—millions per second.
Nothing the customer is doing changes. We just listen.
A small AI model forecasts which hallways will be jammed a tenth of a second from now.
A weather forecast for traffic jams.
We quietly tell the coordinator to use a different route, a smarter strategy.
The customer never knows we did anything. Their work finishes sooner.
A training run that used to take 30 days—now finishes in 25. On the same hardware.
| Cluster Profile | GPUs | Annual Compute | XportL Saves |
|---|---|---|---|
| Single Pod small lab · startup | 1,024 | $22.4M | $1.18M |
| Mid-Tier Cluster neocloud · enterprise lab | 4,096 | $89.7M | $4.71M |
| Frontier Cluster major AI lab · sovereign | 16,384 | $358.8M | $18.8M |
| Hyperscale Data Center Meta · xAI scale | 100,000 | $2.19B | $115M |
Math: GPU-hours/yr × $2.50/hour × 35% communication waste × 15% recovery.
We charge 25% of the dollars saved.
The customer keeps three quarters of every dollar we recover. Aligned incentives, no winner-loser dynamic.
Three frontier customers alone = roughly $14M ARR.
Ten = $47M ARR.
Total dollars the AI industry will waste on hallway-waiting time every year.
The portion running on networks we target. AI clouds, sovereign labs, large enterprises.
A 3.5% capture. Just 90 frontier customers at $4.7M ARR each.
The universe of buyers is fewer than 200 companies globally. Every name is already in the news.
One telemetry pipeline. One graph model of cluster state. One control plane that can act on what it learns. Three customer problems that all reduce to the same shape: watch every rank, find the one that doesn't belong, do something about it.
Recover wasted GPU time from network congestion. Recover wasted GPU time from fail-slow hardware. Both already demonstrated on prototype.
Detect anomalous collective traffic patterns: gradient manipulation, weight exfiltration, insider sabotage during training runs. No production tooling exists for this today.
Programmable DPU/SmartNIC firmware that pushes XportL's intelligence directly into the fabric silicon. Multi-year, multi-million-dollar contracts.
What this seed funds: Year One. Performance optimization, first paying customer, published benchmark. The Integrity and Hardware lines are the long-term arc the seed earns the right to pursue — not what we are pitching, but what makes this a venture-scale outcome rather than a feature.
For two years, every dollar of AI investment went to building the kitchens. The next two years reward whoever can make those kitchens cook faster.
Every new AI data center is being built on a kind of network where Nvidia has no special advantage. Greenfield. Anyone's to win.
Coordinator software designed in 2017. AI training has grown a thousand-fold since. The gap is now obvious.
Every GPU-hour is sold months in advance. The phone rings on its own.
Serial entrepreneur with a public-market exit. Took prior venture from inception through IPO.
Deep operator experience in brand strategy, GTM, capital formation, execution discipline.
Linux systems engineer and entrepreneur. Built the working prototype: closed control loop, GNN predictor, real NCCL plugin validated on NVIDIA hardware.
Deep expertise in kernel-level instrumentation, distributed systems, production infrastructure.
Four senior engineers. Real-hardware deployment. A paying first customer. Reserve to raise the next round from strength.
Investors deploying capital-gains proceeds may be eligible for deferral, basis adjustment, and exclusion of gain after the statutory holding period. Details on request.
Two million dollars. Eighteen months. A small team that has already built the prototype, validated the plugin on real NVIDIA hardware, and is ready to put it in front of real customers.
We're taking a small group of seed investors who understand that the pickaxes get rich, not just the gold miners.