Coderabbit

Coderabbit is an AI-powered code review tool that reviews pull requests, catches bugs, suggests fixes, and helps developers ship cleaner code faster.

CodeRabbit is an AI-driven code review platform that connects to your Git workflows to review pull requests automatically. It analyzes code changes in context, flags bugs and security issues, suggests readable fixes, and summarizes each pull request so reviewers know where to focus. Developers can chat with the assistant, apply one-click fixes, and use the tool in their IDE or CLI to catch problems before code is merged.

Daytona

Daytona provides secure, standardized cloud development environments and sandboxes so developers can start coding instantly with their preferred IDE.

Daytona provides secure, standardized cloud development environments that remove setup, configuration, and dependency hassles for software teams. It delivers isolated sandboxes and preconfigured workspaces that mirror production, so developers can run and test code safely, including AI‑generated code, without touching local machines. Daytona connects with popular IDEs and existing platforms, helping organizations keep environments consistent while letting teams work the way they prefer.

Goodfire

Goodfire builds a platform that lets teams inspect, edit, and control AI models from the inside to reduce errors and make systems more reliable.

Goodfire develops tools that show what happens inside neural networks so teams can understand why models behave a certain way. Its platform decodes model neurons and turns them into features that developers can search, inspect, and adjust directly. This helps reduce hallucinations, improve safety, and fine-tune model behavior without retraining from scratch.

Modular

Modular is a platform for building, optimizing, and deploying generative AI across CPUs, GPUs, and custom chips with MAX infrastructure and Mojo language.

Modular builds an AI compute platform that lets developers run, optimize, and scale generative AI on CPUs, GPUs, and specialized accelerators without rewriting models for each chip. It offers the MAX infrastructure for high-performance AI deployment and the Mojo programming language for Python-compatible, low-latency development. Teams use Modular to get more speed from existing hardware, reduce infrastructure complexity, and keep one workflow for training and inference across different environments.

Nscale

Nscale delivers high-performance GPU cloud infrastructure so businesses can train, fine-tune, and run large-scale AI models efficiently.

Nscale provides full-stack AI infrastructure that combines modern data centers, dense GPU clusters, and orchestration software so teams can train, fine-tune, and deploy advanced AI models at scale. It offers managed GPU cloud, high-speed networking, and tooling for inference and experimentation, helping organizations handle demanding AI and high performance computing workloads with reliable performance and efficiency.

Positron

Positron builds hardware and software that run large AI models faster, cheaper, and with lower energy use for enterprise and research workloads.

Positron develops custom hardware and software systems that run large AI models with faster inference, lower energy use, and better cost efficiency. Its Atlas platform delivers ready-to-deploy AI infrastructure for long-context and next-generation workloads, while upcoming silicon is designed to support even larger, more demanding models. These systems help companies scale AI without expanding power-hungry data centers.

Protégé

Protege connects data owners with AI teams, providing secure, high-quality training data at scale for building and fine-tuning AI models.

Protege is a platform that helps organizations turn their proprietary data into curated training datasets for AI teams. It connects data owners with vetted model builders, handling discovery, permissions, licensing, and delivery so both sides can share and use real-world data safely. Companies use Protege to find, combine, and filter large volumes of structured and unstructured data to train and improve AI models faster.

Baseten

Baseten provides infrastructure for developers to deploy, scale, and run AI models in production with fast, reliable, cost-efficient inference.

Baseten provides infrastructure for developers to deploy and run AI models in real products without building their own serving stack. It lets teams serve large language models, image generation, and other custom models with low-latency, high-throughput inference and autoscaling. Developers use Baseten to move trained models into production quickly, monitor performance, and control costs across different clouds and regions.

Sixfold

Sixfold is an AI underwriting platform that reads insurance submissions, surfaces risk insights, and automates routine work so underwriters decide faster and more accurately.

Sixfold provides an AI underwriting platform for insurers that reads submissions, extracts key risk data, and structures it for review. It highlights risk signals, prioritizes cases, and drafts underwriting narratives, cutting manual document review and research time. The software supports property, casualty, life, and disability lines, helping underwriters process more submissions, keep decisions consistent, and focus on judgment instead of data gathering.

Synthpop

Synthpop is a healthcare AI platform that automates referrals, prior auth, eligibility, and claims workflows so providers cut admin time and speed up patient care.

Synthpop builds healthcare AI agents that handle repetitive admin work across referrals, prior authorizations, eligibility checks, documentation, and claims follow-ups. It connects to EHR, billing, and e-prescribe systems to read, update, and move data between forms and legacy tools. Providers use it to reduce manual data entry, lower denials, and shorten wait times so staff can focus on direct patient support.