AnyDevice auto-selects the right model for your needs and hardware, then optimizes and deploys it to any device — for inference that is faster, leaner and cheaper.
Your model stays yours
No transfer of weights or IP to start.
Data stays local
Run on device; cloud only by choice.
Optimized on every chip
Strong, cost-efficient performance across NVIDIA · Qualcomm · Intel · AMD · Apple.
Benchmark-led, no blind promises
Every metric is measured and carries an evidence label you can verify.
Choose a scenario to pre-fill your deployment audit with the right models, hardware and performance metrics.
One line of code; AI agents do the heavy lifting under the hood — selection, conversion, quantization, backend tuning, profiling and validation.
Tell us what tasks you want to run, for which scenarios, and where it will run.
Target hardware · scenario · constraints
AnyDevice profiles your hardware and characterizes your model, then automatically picks the best-fit model — or keeps the one you bring.
Hardware detection · model characterization · backend selection
AutoOpt automatically converts, quantizes, backend-optimizes, profiles and validates.
Convert · quantize · backend tune · profile · validate
Receive a deployment-ready path — not another research prototype.
Ready-to-deploy artifact · benchmark summary · compatibility notes
Every item carries a status — we don't claim production support before it's validated.
Run a 5-minute Edge AI Diagnostic and get a personalized before/after report estimating how much faster, smaller and cheaper your model could run on your target hardware — with an evidence label on every number.
[ Placeholder ] Personalized report preview — three outcome cards (Faster · Smaller · Lower Cost) with before/after ranges and evidence labels (Verified / Estimate / Benchmark Required), plus the recommended deployment path.
Keep control of your models, weights, data, infrastructure and IP — deploy on edge, private VPC or hybrid.
Optimize latency, memory and cost across the whole stack, on the hardware you actually ship.
Replace months of manual conversion, profiling and hardware adaptation with an automated pipeline.
Every result is shown with its evidence level and stays anonymized until a customer authorizes it in writing — no logos, names or numbers we can't stand behind.
[ Placeholder ] Anonymized pilot case card — challenge → stack → optimization → measured result (latency / memory / accuracy delta), plus an authorized customer quote. No logos, names, or numbers without written approval.
Credibility comes near the end of the funnel — proof that this team can deliver cross-model, cross-hardware deployment, framed around delivery, not résumés.
Founder · CEO / CTO
15+ years building edge AI, foundation models and hardware-software systems — from research breakthroughs to production impact at global technology companies.
Co-founder
Former Google product leader and CMU Robotics graduate with hands-on experience across on-device products, autonomous driving, robotics and multiple AI ventures.
Company names shown as text only. No logos without written authorization.
Straight answers on what you share, what's supported, and how to read the numbers.
No. You can start with model metadata, hardware specs and current benchmark results. Private weights or data can be reviewed later under an agreed security process.
The assessment supports CV, VLM, LLM, VLA and emerging world-model workloads, with the actual deployment path confirmed during technical review.
The form includes common NVIDIA, Qualcomm, Intel, AMD, Apple and other edge platforms; production support is confirmed after validation.
It is a preliminary estimate based on the information provided. Verified numbers require profiling and benchmarking on the target hardware.
Yes — private VPC, on-prem or controlled evaluation are available as enterprise options, subject to project requirements.
