AI automation and visibility for mid-sized companies
Open-source AI agent orchestration platform for incident response and automation
Stage
Established
Not given
Founded
2020
Not given
Based in
US
Not given
Pricing model
Freemium
Open source
Pricing
Free plan with 2 daily analyses. AI Visibility Starter $29/month (10 daily analyses, 3 queries). AI Visibility Growth $79/month (25 daily analyses, full 6 queries). AI Visibility Autopilot $149/month (50 daily analyses, full 6 queries, weekly re-audit). Services: Free Diagnostic (15 min), Local Star
Not given
Key features
AI citation audit testing what AI models say about your brand in real queries
AI identity files checker for ai.json, llms.txt, robots.txt
AI crawler policy analysis for GPTBot, ClaudeBot, Perplexity
Schema.org markup structured data analysis
Technical SEO audit with canonical URLs and hreflang
Open Graph tags verification for AI sharing
Core Web Vitals and Lighthouse performance scoring
Market intelligence on pain points and decision criteria
AI-powered workflow automation for sales and support
MCP server integration for Claude and Cursor
Incident response with agent investigation and pull request generation
Automated workflows triggered on schedules or webhooks
Agent summoning from GitHub, GitLab and Radicle with /ryph command
Semantic memory system for shared and persistent agent learning
Credentials kept behind gateways with no access in sandbox
Customized environments per repository with cached dependencies
Browser use for agent interactions with live watching
Artifacts and pages for reports and overviews
Deployment options from laptop to Kubernetes
Tool proxy with allowlisted MCP server integrations
What makes it different
Free visibility audit for AI assistants with no card required
Transparent ROI modeling before any automation work is built
Production workflows deployed in 2-4 weeks with team handover
Audits measured on the same frozen prompt set for comparable results
Focus on small to mid-sized companies 10-200 employees
Agents work without holding real credentials or keys
Changes ship as patches that require review before merge
Runs on your own infrastructure from laptop to cloud
Semantic memory learns across runs with deduplication and contradiction handling
Built-in safety assuming agents can be manipulated or exploited
INITE
Ryph