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Applied-AI studio and R&D group building enterprise products like the FeatureOS feedback platform for product teams.
Serverless AI cloud for running inference, training and sandboxes on GPUs with fast cold starts and pay-per-use billing.
AI super-assistant plus enterprise ML platform: ChatLLM for teams and end-to-end model building for enterprises; broad, pricing not shown.
No-code visual builder to create, deploy and orchestrate AI agents across 200+ models, for individuals to enterprise.
Google's agentic development platform and IDE for building software with autonomous, Gemini-powered coding agents.
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- ✦In-house product line including FeatureOS and SupportWire
- ✦Feedback board, roadmap, changelog, and knowledge base modules (FeatureOS)
- ✦AI-powered feedback analysis and duplicate detection (FeatureOS)
- ✦API and webhook access for custom integrations
- ✦Applied AI R&D consulting engagements
- ✦Product engineering services for client teams
- ✦Serverless GPU compute defined in Python
- ✦Sub-second container cold starts
- ✦Autoscale 0 to 1000+ GPUs
- ✦Inference, training and batch workloads
- ✦Secure sandboxes for untrusted code
- ✦Built-in logging and observability
- ✦ChatLLM access to multiple top AI models
- ✦AI agents and automation
- ✦No-code full-stack app creation
- ✦Enterprise generative AI platform
- ✦Structured ML model building
- ✦Optimization and forecasting
- ✦No-code visual agent builder
- ✦200+ AI models via service router
- ✦100+ prebuilt templates
- ✦Agent skills, plugins and workflows
- ✦AI Media Workbench for video/image
- ✦Enterprise controls (SSO, permissions, self-host)
- ✦Agent-first IDE experience
- ✦Autonomous planning and code execution
- ✦Integrated editor, terminal and browser control
- ✦Powered by Google's Gemini models
- ✦High-level developer supervision
- →Product teams collecting and prioritizing customer feedback (via FeatureOS)
- →Companies needing custom applied-AI research or engineering
- →Startups outsourcing product engineering to a specialist studio
- →Deploying and scaling model inference
- →Fine-tuning and training models
- →Running batch/parallel AI jobs
- →Executing untrusted code in sandboxes
- →Chat with many AI models in one place
- →Build and deploy ML models
- →Automate tasks with AI agents
- →Automating business workflows
- →Building custom AI agents without code
- →Content and media generation
- →Deploying agents across a team or org
- →Building apps with AI agents
- →Automating multi-step coding tasks
- →Prototyping and iterating on software
- →Assisting developers on complex work