You've built an incredible Generative AI application. It's engaging users, automating tasks, and showing immense promise. Your dashboards are lighting up with activity: token counts are soaring, response times are decent, and completion rates show green across the board. You're thinking, "Success, right?"

Wrong.

What if, despite those pristine metrics, 30% of your users left yesterday feeling deeply frustrated because your AI completely missed their intent? What if your "high performing" AI agent is quietly eroding customer relationships and brand trust, all while your analytics suggest everything is fine?

Most AI teams are, in essence, flying blind, meticulously measuring everything except what truly impacts their users and their business.

The Problem: Traditional Metrics Don't Work for AI

Here's the harsh reality: Your standard observability tools treat AI interactions like black boxes. They'll tell you an API call was made and a response generated, but they completely miss the quality of that interaction.

Consider this scenario as example: Your AI customer service agent handles 1,000 conversations with impressive 2-second response times. Your operational metrics are glowing. But dig deeper and you might discover:

• 37% of users had to repeat their question because the AI misunderstood

• 23% abandoned mid conversation due to irrelevant responses

• 41% escalated to human agents, defeating the purpose entirely

These critical failures remain invisible to traditional monitoring tools, yet they're destroying your user experience and ROI.

The Bottom Line: Traditional Observability Tells You Your AI Is Running. You Need to Know If It's Actually Working.

This is precisely why we built insideLLM : the first analytics platform designed specifically for understanding what’s really happening inside your large language model (LLM) applications, from the user's perspective.

What insideLLM Actually Does for You

Unlike traditional tools that only show you system health, insideLLM provides crucial visibility into user experience health:

• 🔍 Granular Flow Inspection: See exactly where users struggle or succeed in their AI interactions. Pinpoint the specific conversation turns or user inputs where things go wrong, not just that they went wrong generally.

• 🚨 Intelligent Problem Discovery: Automatically surface critical issues that standard metrics miss entirely. Discover patterns like, "users who ask about pricing get irrelevant responses 34% of the time," allowing for targeted improvements.

• 📊 Actionable Performance Insights: Transform opaque AI conversations into structured, analyzable data. Gain concrete evidence on which prompt changes improve outcomes and which inadvertently hurt performance.

• 🎯 AI Alignment Verification: Ensure your LLM consistently stays on brand, adheres to guidelines, and achieves its intended business outcomes. Establish a closed loop feedback system for continuous improvement, replacing guesswork with data.

Why This Changes Everything for Your AI Team

• Stop Playing Guessing Games: Know precisely why 23% of users abandon conversations due to AI misunderstanding and get the data to fix it swiftly.

• Drive Strategic Decisions: Stop wondering where to invest your precious AI development resources. Know exactly which capabilities drive 80% of user satisfaction and focus your efforts.

• Diagnose Issues faster: Instead of spending days debugging based on vague user complaints, pinpoint the root cause of problems immediately with conversation level insights.

Traditional roadmaps often focus on shipping features; the most successful AI teams, however, focus on experiments and iteration informed by robust measurement. insideLLM provides that critical measurement layer through our unique flow based data model.

Ready to Stop Flying Blind?

Email us at founders@insideLLM.com or book a 15 minute demo to see insideLLM in action.