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AI super-assistant plus enterprise ML platform: ChatLLM for teams and end-to-end model building for enterprises; broad, pricing not shown.
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.
AI agent that works inside a product's real codebase so PMs, designers and engineers can ship and refactor frontend changes.
Desktop workspace letting multiple AI coding agents (Claude Code, Codex, Cursor) collaborate on shared context and specs.
No public pricing
No public pricing
No public pricing
- ✦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
- ✦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
- ✦AI agent that works in real product code
- ✦Role-based workflows for PMs, designers and engineers
- ✦Prototyping with production code
- ✦Legacy interface refactoring
- ✦Design-system alignment
- ✦Runs multiple coding agents (Claude Code, Codex, OpenCode, Cursor) in one workspace
- ✦Bring-your-own-subscription model for existing agent accounts
- ✦Agent-to-agent communication for questions, reviews and handoffs
- ✦Shared filesystem, decision history and specs per task
- ✦Mid-chat model switching without losing context
- ✦macOS desktop app
- →Chat with many AI models in one place
- →Build and deploy ML models
- →Automate tasks with AI agents
- →Turning prompts into maintainable, spec-matched code
- →Catching bugs unit tests miss
- →Reviewing PRs and fixing bugs in CI/CD
- →Shipping frontend features faster
- →Prototyping directly in real code
- →Refactoring legacy UIs
- →Turning designs into product code
- →Developers coordinating multiple AI coding agents on the same project
- →Teams collaborating around shared agent context and specs
- →Switching between different LLMs mid-task without losing history
- →Reviewing and handing off in-progress coding work between agents