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Enterprise AI coding assistant that pulls context from an entire codebase to power chat, code edits and debugging.
Agentic AI platform ('Aiden') that automates incident response, infrastructure-as-code and observability tasks with policy-based governance.
Open-source framework for automated end-to-end UI testing of mobile and web apps, with a paid cloud for parallel device runs.
Continuously analyzes MySQL, MariaDB, and PostgreSQL workloads to recommend and safely apply configuration and query fixes.
AI Text2SQL database client that generates and fixes SQL from natural language across 30+ databases, with dashboards.
No public pricing
Free trial available
Free trial available
No public pricing
Free trial available
- ✦Codebase-aware developer chat
- ✦AI code completions and inline edits
- ✦Customizable and shareable prompts
- ✦Automatic bug identification and debugging help
- ✦Context filters to exclude sensitive repos
- ✦Integrates with major code hosts and IDEs
- ✦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
- ✦Human-readable YAML test flows
- ✦Local CLI and Studio testing for free
- ✦Open-source, CI-friendly design
- ✦Cloud device farm for parallel runs
- ✦AI-agent integration through MCP
- ✦Self-healing tests with local agents
- ✦Workload-based configuration tuning
- ✦SQL query analytics and optimization suggestions
- ✦Schema optimization (duplicate/unused index detection)
- ✦24/7 automated health and security monitoring
- ✦One-command agent installation
- ✦Human approval required before applying changes
- ✦AI Text2SQL query generation
- ✦One-click SQL error fixing
- ✦GUI database management and ER diagrams
- ✦AI data analysis and dashboards
- ✦Support for 30+ databases
- ✦Local data processing for privacy
- →Engineers asking questions about an unfamiliar large codebase
- →Teams standardizing common coding tasks with shared prompts
- →Developers debugging errors faster with AI-assisted context
- →Enterprises running large-scale code migrations
- →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
- →Automate mobile app UI regression tests
- →Run tests in parallel across many devices
- →Integrate UI testing into CI pipelines
- →Let AI agents generate and run app tests
- →Database teams reducing manual tuning workload
- →Hosting providers optimizing customer databases at scale
- →Engineering teams without a dedicated DBA fixing performance issues
- →AWS RDS users tuning managed database instances
- →Write SQL from plain language
- →Manage multiple databases in one client
- →Generate BI dashboards from data
- →Migrate and sync schemas/data