RightNow AI - Get AI Answers Right Now

Close the gap between AI models and GPU hardware

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Updated Jul 2026 · Added Jul 2026

rightnowai.co

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What is Rightnow Ai?

RightNow AI is a YC-backed research lab building infrastructure tools that bridge AI models and GPU hardware. The platform provides CUDA editors, inference optimization frameworks, and managed GPU infrastructure for deploying and operating production workloads at scale. It's designed for engineers and enterprises that need to optimize AI model performance on GPUs, from development through deployment.

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Key Features of Rightnow Ai

CUDA-focused editor with profiling and PTX/SASS inspection
GPU emulation and remote GPU workflows
Inference optimization around kernels and runtimes
Managed GPU infrastructure for deployment and telemetry
Automated GPU-kernel search via agents
Sparse attention optimization
Token-level inference efficiency

Who Should Use Rightnow Ai?

Optimize LLM inference for production deployment

Automate GPU kernel synthesis and optimization

Profile and benchmark CUDA code across hardware variants

Manage inference workloads on distributed GPU infrastructure

Reduce latency in token-level inference execution

Rightnow Ai: Pros & Cons

✓Pros

YC-backed research team with published papers on GPU optimization
Automated kernel synthesis reduces manual optimization effort
Comprehensive tooling from development to production deployment
Open-source agent infrastructure available
Endorsed by NVIDIA leadership

Frequently Asked Questions about Rightnow Ai

Can I use RightNow AI to profile and optimize CUDA kernels for specific GPU hardware?

Yes. RightNow AI includes a CUDA editor with profiling tools and PTX/SASS inspection, letting you examine kernel performance at a low level. You can also benchmark your code across different hardware variants to see how kernels behave on the GPUs you actually deploy to.

Does RightNow AI help reduce inference latency in production LLM deployments?

That is a primary use case. RightNow AI offers token-level inference efficiency optimization, sparse attention acceleration, and kernel-level runtime tuning. You can manage inference workloads on the platform's distributed GPU infrastructure and get telemetry on what is actually happening during execution.

What is GPU emulation in RightNow AI used for?

GPU emulation allows you to develop and test CUDA code without having the physical hardware on hand, and to run remote GPU workflows. This is useful for iterating quickly during development before you deploy to your production GPU cluster.

Does RightNow AI automate kernel optimization or synthesis?

Yes, the platform includes agents that can perform automated GPU-kernel search and synthesis, reducing the manual effort of hand-tuning kernels. The agent infrastructure is available as open source, so you can adapt it to your own optimization pipelines.