Moda
Moda provides a reliability and monitoring layer for AI agents, turning conversations and tool traces into low-noise, actionable signals to catch failures before users do.
At a Glance
- AI Infrastructure Teams
- Customer Support Automation Teams
- Enterprise AI Developers
AI Tools by Moda
(1)
Moda
AI Marketing Automation Platform
Discussions
No discussions yet
Be the first to start a discussion about Moda
Latest News
Moda Launches on LinkedIn and Y Combinator: The reliability & monitoring layer your AI agents need.
Co-founder Mohammed Al-Rasheed introduces Moda AI for catching silent failures in AI agents.
Moda featured as a Winter 2026 Y Combinator company solving AI system unreliability.
Products & Services
A SaaS platform for monitoring and reliability of AI agents, featuring trace-first drilldowns, failure detection, and quality/safety signals.
Market Position
Positions itself as a reliability layer specifically for AI agents that detects behavioral failures (laziness, frustration) rather than just technical logs, with a focus on 'no-configuration' deployment and 'trace-first' debugging.
Leadership
Founders
Mohammed Al-Rasheed
Previously built AI agents and infrastructure at Shopify, Notion, and Clio. Dropped out of University of Waterloo (Management Engineering/CS).
Pranav Bedi
Built data infrastructure for banks and insurance firms. Worked on AI agents at Shopify and Cerebral Valley. Dropped out of University of Waterloo (Computer Science).
Executive Team
Mohammed Al-Rasheed
Co-Founder
Experience in AI agents and infrastructure at Shopify, Notion, and Clio.
Pranav Bedi
Co-Founder
Experience in data infrastructure and AI agents at Shopify and Cerebral Valley.
Founding Story
Founded by Mohammed Al-Rasheed and Pranav Bedi, who dropped out of the University of Waterloo to solve what they identified as the biggest problem of the next decade: unreliable AI systems. Drawing from their experiences building AI agents at Shopify, Notion, and Clio, they built Moda to detect silent failures that logs often miss.
Business Model
Revenue Model
SaaS (likely usage-based or subscription, although specific pricing tiers are not yet public).
Target Markets
- AI Infrastructure Teams
- Customer Support Automation Teams
- Enterprise AI Developers
- Monitoring customer-support and service bots for silent failures
- Detecting and preventing AI security threats (prompt injection, jailbreak)
- Reducing user frustration by catching and fixing agent laziness or incorrect responses
- Engineering reliability through real-time alerts and root-cause analysis
- Testing and validating fixes to agent behavior before production deployment
- Not disclosed, focus on companies running AI agents in production.