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Online database-design tool with sample schemas and an AI generator to explore, modify or build database structures visually.
Agentic AI platform ('Aiden') that automates incident response, infrastructure-as-code and observability tasks with policy-based governance.
Google Labs experiment for building and sharing AI mini-apps from natural-language prompts, no coding required.
AI prototyping tool that generates UI matching your design system, letting product teams test features fast.
Cloud-agnostic AI/ML workflow orchestrator that runs pipelines inside a customer's own infrastructure for compute-heavy teams.
No public pricing
No public pricing
No public pricing
No public pricing
- ✦Library of sample database designs
- ✦Visual database designer / diagram tool
- ✦AI database generator
- ✦Modify and optimize existing schemas
- ✦SQL script export
- ✦Dialect converters (MySQL/PostgreSQL/MSSQL)
- ✦Automated service discovery and dependency topology mapping
- ✦SLO-based alert triage and prioritization
- ✦AI-driven root cause analysis with pre-built workflows
- ✦Human-approved remediation with full audit trails
- ✦Works alongside existing tools like Datadog, Grafana, New Relic
- ✦Governance and policy enforcement layer for agent actions
- ✦Build AI mini-apps from natural-language prompts
- ✦Visual editor for prompt/tool workflows
- ✦Share created apps with others
- ✦No-code AI app prototyping
- ✦AI UI generation from prompts
- ✦Match existing styling and design systems
- ✦Rapid, high-fidelity prototyping
- ✦Live team editing and sharing
- ✦Enterprise security and compliance
- ✦Python-native dynamic workflow authoring
- ✦Automatic failure recovery, caching, and versioning
- ✦Zero Trust architecture keeping data inside customer's cloud
- ✦Real-time inference and agentic-AI workflow support
- ✦High-throughput scaling (tens of thousands of actions per run)
- ✦Local development environment matching production behavior
- →Finding a starting schema for a project
- →Designing a database visually
- →Generating a schema with AI
- →Converting between SQL dialects
- →SRE teams reducing mean-time-to-resolution during incidents
- →Platform engineers wanting policy-governed AI infrastructure management
- →Enterprises needing SOC 2 / PCI / HIPAA-compliant AI operations
- →Prototyping an AI workflow quickly
- →Sharing a custom AI mini-app
- →Automating a task with chained prompts
- →Prototype new product features
- →Test designs with customers
- →Build design-system-consistent mockups
- →ML teams orchestrating training and inference pipelines at scale
- →Biotech/geospatial companies needing GPU-heavy pipeline orchestration
- →Enterprises migrating off Airflow for ML workflow management
- →Teams requiring workflows that never send data outside their own cloud