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Always-on cloud AI agent that runs multi-step workflows and monitoring on a dedicated 24/7 VM to automate business tasks.
Kiro is a spec-driven agentic coding tool for IDE, CLI and web that turns prompts into specs and catches bugs with property-based tests.
Unified API gateway that routes requests to 400+ LLMs across 70+ providers with failover and no subscription.
Agentic AI platform with a coding desktop app, CLI, and cloud agents for autonomous software development and office work.
AI super-assistant plus enterprise ML platform: ChatLLM for teams and end-to-end model building for enterprises; broad, pricing not shown.
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- ✦Always-on agent on a dedicated 24/7 VM
- ✦Multi-step task automation (docs, PPT, video, research)
- ✦Proactive monitoring with alerts and actions
- ✦Shared/self-improving agent knowledge network
- ✦Page deployment and drive storage
- ✦Spec-driven development (requirements, design, tasks)
- ✦Parallel agents, local or cloud
- ✦Property-based and correctness testing
- ✦Works in IDE, CLI, web and mobile
- ✦Multiple models (Claude, open-weight, Auto)
- ✦Headless CLI for CI/CD
- ✦Context from tools like Figma and Terraform
- ✦One unified, OpenAI-compatible API for 400+ models
- ✦Automatic provider failover for higher uptime
- ✦Edge routing for low latency
- ✦Custom data and provider policies
- ✦Pay-as-you-go credits usable across any model
- ✦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
- ✦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
- →Automating recurring business workflows overnight
- →Generating reports, documents and presentations
- →Monitoring uptime, pricing or metrics with auto-actions
- →Running research and content tasks hands-off
- →Turning prompts into maintainable, spec-matched code
- →Catching bugs unit tests miss
- →Reviewing PRs and fixing bugs in CI/CD
- →Accessing many LLMs through one integration
- →Adding provider redundancy to AI apps
- →Comparing model price and performance
- →Powering agents and AI-native products
- →Autonomous feature development in large codebases
- →Terminal-based AI pair programming
- →Cross-department task automation for legal, finance, HR
- →Onboarding developers to unfamiliar codebases
- →Chat with many AI models in one place
- →Build and deploy ML models
- →Automate tasks with AI agents