The GPU-Native Data Layer

Higher margin
per token.

SQL, Python, models and MCP for agents, in one engine, on the GPUs you already own.

CORNDEX moves the heavy data path onto your GPUs. Utilization goes up, energy per job goes down, and the difference lands on your margin per token.

See why
In production across
Semiconductor Manufacturing Financial Services Telecom Healthcare
Why now

The AI era runs on GPUs.

While companies race to add capacity, expensive GPUs spend too much time waiting for data to be prepared and moved between systems. The costs keep adding up, even when the GPUs aren't doing useful work. As AI scales, that wasted capacity becomes harder to afford.

Getting more work out of the GPUs you already have means more value from every dollar spent.

$ WORKING IDLE Every core is paid for, but most are not utilized.
The engine

GPU-accelerated makes many trips.
GPU-native makes only one.

CORNDEX is a GPU-native data engine: scan, SQL, Python, models and MCP for agents, all in one process. Data crosses from CPU to GPU once.

TODAY · A SEPARATE SYSTEM FOR EACH STEP PREPARE ANALYZE INFER copy · wait copy · wait every hand-off is a CPU ⇆ GPU crossing, and the GPU waits DATA ANSWER WITH CORNDEX · ONE ENGINE PREPARE · ANALYZE · INFER one workflow, GPU-native no copies, no hand-offs, and the GPU stays busy
Languages & APIs SQL & Python MCP for AI agents
Data & storage Apache Iceberg Lakehouse or standalone
AI & engine Model training & inference, in-engine Cost-based GPU optimizer
Deployment On-prem & air-gapped
The payoff

What you get.

CHEAPER
  • The same work runs on fewer GPUs
  • Less electricity for every workflow
  • Hardware upgrades can wait
FASTER
  • Hours-long workflows finish in minutes
  • Pipelines finish before morning
  • Batch queues become interactive work
EASIER
  • One engine replaces many systems
  • No glue code, no copies
  • On your GPUs, air-gapped if needed
Tokenomics

The other side of your token bill.

Serving is one side of your token bill. The other side is the data path: the preparation, analytics, evals and context behind every token. CORNDEX runs that side on your GPUs.

DATA PREPARATION curation · analytics · evals · context RUNS ON CORNDEX TRAINING pretraining · fine-tuning INFERENCE & SERVING KV cache · batching $ · the token bill APPS & AGENTS training data, ready faster context, fewer tokens answers for agents THE DATA PATH · on your GPUs

Move the data path onto your GPUs, and the same power envelope makes more tokens, at a higher margin per token.

Use cases

Where CORNDEX runs today.

Semiconductor manufacturing · In production today

We help build more GPUs, and get more out of every GPU.

Problem. In HBM, the memory inside AI GPUs, yield is revenue, and explaining a yield drop from production test data took weeks.

Solution. AI models investigate the production data inside the engine.

Value. Problems that took weeks to explain are explained in days.

5-10x
faster execution, measured across the converted workloads. The speedup is what turned an overnight batch queue into live work.
Financial services

Scoring and fraud AI.

Problem. Scoring and fraud models are only as good as the data behind them, and the night is short.
Solution. Banks score customers and risk with AI models on the engine.
Value. A full night of data preparation finished before morning; fraud models learning from years of behavior instead of months.

Telecom

Years of history behind AI.

Problem. Network history too big to keep at full detail on the old stack.
Solution. Operators put years of raw history behind AI on the engine.
Value. Answers in seconds instead of days.

For datacenters & neoclouds

More from every megawatt.

At campus scale, the economics are set by power, not by GPU count. The data path is the quiet consumer: CPU fleets beside the GPUs, burning megawatts that never become tokens.

Power

More tokens per megawatt.

Hardware

The same GPUs do more work.

Energy

Every job takes less electricity.

The same campus, producing more.

Contact

Let's talk.

Tell us a bit about your data and your workloads, and we will take it from there.