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Agentic AI platform with a coding desktop app, CLI, and cloud agents for autonomous software development and office work.
A Japanese AI firm that grew from shogi-AI research into industry ML solutions and a generative-AI platform, HEROZ ASK.
AI-focused cloud offering NVIDIA GPU compute, storage and MLOps tooling for training and inference at scale, with usage-based pricing.
Cloud platform from the makers of PyTorch Lightning for building, training and deploying AI in browser-based GPU Studios.
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No public pricing
No public pricing
No public pricing
- ✦Multi-agent collaboration for end-to-end tasks
- ✦Persistent memory and custom rules
- ✦Extensible skills and plugins
- ✦Rich context across code, images, and directories
- ✦Automatic codebase documentation generation
- ✦Terminal-native CLI and JetBrains IDE plugin
- ✦Cloud-hosted agents for enterprise use
- ✦Deep-learning and machine-learning core technology
- ✦HEROZ ASK generative-AI platform
- ✦BtoB and BtoC AI solutions
- ✦BLOOMWORKS product
- ✦Industry AI deployment case studies
- ✦LLM API router
- ✦OpenAI API proxy
- ✦Model aggregation (OpenAI, Gemini, DeepSeek, Llama, Qwen, Claude, etc.)
- ✦Unified OpenAI API standard
- ✦Unlimited concurrency
- ✦NVIDIA GPU instances (H100, H200, B200, GB200)
- ✦On-demand and preemptible GPU pricing
- ✦High-performance and object storage
- ✦Managed Kubernetes and Slurm (Soperator)
- ✦Serverless and managed inference (Token Factory)
- ✦MLOps tooling and 24/7 expert support
- ✦Commitment discounts up to 35%
- ✦Browser-based Lightning Studios with on-demand GPUs
- ✦PyTorch Lightning training framework
- ✦Model training, fine-tuning and deployment
- ✦Collaborative, shareable ML environments
- ✦Scalable multi-GPU/multi-node compute
- →Autonomous feature development in large codebases
- →Terminal-based AI pair programming
- →Cross-department task automation for legal, finance, HR
- →Onboarding developers to unfamiliar codebases
- →Deploying generative AI in enterprises
- →Applying ML to industry-specific problems
- →AI-driven business transformation (DX)
- →Integrating multiple AI models into applications using a single API
- →Accessing the latest AI models through a unified interface
- →Managing and scaling AI model usage with unlimited concurrency
- →Train large AI/ML models on GPU clusters
- →Run scalable inference workloads
- →Store and manage large training datasets
- →Run Slurm/Kubernetes AI pipelines
- →Prototype and train ML models in the cloud
- →Fine-tune and deploy foundation models
- →Run reproducible AI experiments collaboratively