Milana
Milana is an AI agent that analyzes user session replays to identify behavioral obstructions and deliver prioritized, actionable recommendations for product teams. It automatically detects patterns like rage clicks, auth token expiries, and onboarding restarts, then suggests concrete next steps such as creating a ticket, drafting a PRD, or sharing findings in Slack. The platform aggregates insights across thousands of sessions to help teams focus on the highest-impact fixes and hit activation goals.
- AI Agents
- Data & Analytics
- Artificial Intelligence
- Enterprise Software
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Product teams collect analytics, session replays, and user feedback, but these raw data sources don't tell them what to do next. Teams are left to manually correlate behavior patterns, prioritize issues, and translate insights into action, which is slow and often misses critical problems.
Solution
Milana is an AI agent that learns from each user session replay and helps teams hit their activation goals. It watches every session replay and studies user behavior—why they hesitate, when they get confused, where they give up—then tells teams what's wrong, how big the problem is, and what to do about it. The platform automatically surfaces key obstructions, quantifies their frequency, and provides direct actions such as "Fix with Claude," "Create ticket," "Create PRD," or "Share in Slack." Milana aggregates findings across thousands of sessions to produce prioritized recommendations grounded in observed user behavior.
Target Audience
Product managers, growth teams, and engineering leaders at software companies who need to convert user behavior data into prioritized product improvements and hit activation targets.
Features
- AI-driven analysis of user session replays with behavioral pattern detection (rage clicks, auth token expiry, onboarding restarts, AI misunderstanding intent)
- Automated identification and prioritization of key obstructions with frequency rates (e.g., 29% obstruction rate over past 7 days)
- Action-oriented recommendations with integrated workflows for fixing issues, creating tickets/PRDs, and sharing insights in Slack
- Session-level insights with quantitative metrics (e.g., session duration, unanswered issues, unread alerts)
- Cross-session analytics that aggregates findings across thousands of sessions to surface systemic issues