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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 content filter and parental-control app that strips explicit material from sites, social, and AI chatbots without full blocking.
Security platform that guards GenAI apps and AI agents against prompt injection, data leaks and misuse for enterprise teams.
No public pricing
Free trial available
No public pricing
Free trial available
- ✦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
- ✦Real-time explicit-content filtering
- ✦AI chatbot filtering (ChatGPT, Gemini, Claude, Grok)
- ✦Sexting detection and alerts
- ✦Screen-time limits and app blocking
- ✦Removal-prevention anti-tampering
- ✦Location alerts and social monitoring
- ✦Runtime protection for AI agents and apps
- ✦Prompt-injection and jailbreak prevention
- ✦Data-leakage detection in prompts
- ✦Shadow-AI discovery across apps and browsers
- ✦Policy controls by user, app and action
- ✦AI red-teaming and adversarial testing
- →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
- →Protect children from explicit content online
- →Prevent sexting on a child's device
- →Set healthy screen-time boundaries
- →Personal content filtering for adults
- →Securing conversational and RAG agents
- →Governing employee use of AI tools
- →Adversarial testing before deploying GenAI
- →Meeting AI compliance requirements