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LLM session analysis

LLM Session Explorer

Explore every LLM interaction and touchpoint, transforming raw data into an insightful user engagement narrative. 

Dive deeper, learn more about your LLM sessions

Navigating LLM sessions can be overwhelming. Missed issues compromise your application’s reliability and user trust. Every overlooked anomaly diminishes performance, confuses users, and undermines confidence in your GenAI product. Using Session Explorer helps you understand friction points from your users’ POV.

Detect and improve interactions

Transforming LLM session insights into iterative improvements

  • Use the “Timeline” tool to quickly review sessions, zoom out, and detect flagged behaviors like hallucinations or jailbreaks.
  • Explore raw sessions and gain granular insights to shorten fine-tuning cycles and understand the logic behind flagged interactions.
  • Focus on genuine alerts by reducing false positives and irrelevant noise, streamlining the LLM oversight process.

Fine Tune Detections

Unlock the narrative behind every interaction

  • Analyze entire sessions to derive insights that promote enhanced LLM performance.
  • Elevate stakeholder discussions with transparent, data-driven explanations of LLM behavior.
  • Identify and address recurrent session patterns, ensuring your LLMs’ narrative consistency and reliability.
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“In a space that is developing fast and offerings multiple competing solutions, Aporia’s platform is full of great features and they consistently adopt sensible, intuitive approaches to managing the variety of models, datasets and deployment workflows that characterize most ML projects. They actively seek feedback and are quick to implement solutions to address pain points and meet needs as they arise.”

Felix D.

Principal, MLOps & Data Engineering

“As a company with AI at its core, we take our models in production seriously. Aporia allows us to gain full visibility into our models' performance and take full control of it."

Orr Shilon

ML Engineering Team Lead

“ML models are sensitive when it comes to application production data. This unique quality of AI necessitates a dedicated monitoring system to ensure their reliability. I anticipate that similar to application production workloads, monitoring ML models will – and should – become an industry standard.”

Aviram Cohen


“With Aporia's customizable ML monitoring, data science teams can easily build ML monitoring that fits their unique models and use cases. This is key to ensuring models are benefiting their organizations as intended. This truly is the next generation of MLOps observability.”

Guy Fighel

General Manager AIOps

“ML predictions are becoming more and more critical in the business flow. While training and benchmarking are fairly standardized, real-time production monitoring is still a visibility black hole. Monitoring ML models is as essential as monitoring your server’s response time. Aporia tackles this challenge head on.”

Daniel Sirota

Co-Founder | VP R&D

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