Melodi is the essential analytics platform for teams building and managing AI agents.

Unlike traditional product analytics tools focused on screen-based interactions, Melodi is specifically designed to understand and improve AI agent performance and user experience by analyzing conversational data at scale.

Melodi helps:

  • Product Managers understand user behavior, measure satisfaction across all interactions, and prioritize high-impact agent improvements.
  • Operations Teams monitor agent performance, track key metrics, and report on the success of AI initiatives.
  • Data Scientists & ML Engineers access curated, production-grade datasets for fine-tuning, prompt engineering, evaluation, and custom model development.

​
Core Capabilities

Melodi provides a suite of tools to monitor, analyze, and improve your AI agent:

  • Monitoring: Track essential metrics like user engagement, retention rates, session volume, and user segmentation. Gain critical insights into how users interact with your agent over time.

  • Melodi Intelligence & Automated Evaluations: Leverage automated LLM-based evaluations to gain deeper insights with minimal setup:

    • Session Outcome: Get an out-of-the-box estimate of user experience for every session, even without explicit feedback.
    • User Intents: Automatically understand and segment what users are trying to accomplish.
    • Issue Monitoring: Proactively track known problematic scenarios like hallucinations or user frustration.

  • User Feedback, Human Review, and Data Labeling: Collect explicit user feedback (e.g., ratings, comments) and implicit signals via flexible APIs and components. Use the platform for internal human review and structured data labeling, turning raw interactions into valuable datasets for both product analysis and ML training.

​
Key Data Concepts

Melodi organizes data around these core concepts:

  • Projects: The top-level container, typically representing a single AI agent, product, or feature.
  • Threads: Represent individual sessions or conversations, containing sequences of messages. Threads support multi-turn interactions and various message roles (user, assistant, RAG lookup, tool calls).
  • Messages: The individual turns within a Thread, capturing content, role, type, and metadata.
  • Feedback: Labels or ratings applied to Threads or Messages, either by end-users or internal reviewers. Can be customized beyond simple positive/negative ratings.
  • Users: Represent end-users interacting with your agent, allowing for metadata tracking and segmentation.

​
Melodi in the LLM Ops Landscape

Melodi operates adjascent to the LLM Ops and AI Observability space, sharing goals with tools focused on tracking and improving AI agent performance. Where observability tools solve for engineering, Melodi solves for product and business owners.

Alignment with Technical LLM Ops Tools: Like many LLM infrastructure and observability platforms, Melodi provides visibility into agent behavior, monitors interactions, and helps identify areas for performance optimization and evaluation.

Melodi’s Unique Focus: Where Melodi distinguishes itself is through its analytics-centric approach. It emphasizes translating agent performance data into actionable insights for product strategy and user experience improvement. Key differentiators include:

  • Connecting AI to Business Outcomes: Melodi connects technical agent behavior directly to user satisfaction, engagement metrics, and ultimately, business objectives.
  • Strategic Insights: Beyond purely technical monitoring (e.g., latency, cost, trace debugging), Melodi delivers strategic insights valuable for product managers and leadership regarding user segmentation, adoption trends, and experience gaps.
  • Bridging Technical & Business: Melodi is designed to be accessible and valuable for both technical teams (providing data for ML) and non-technical stakeholders (providing clear product and user insights).

Complementary Role: Think of Melodi as providing the essential analytics and strategic intelligence layer within your LLM Ops stack. While other tools might focus deeply on the technical DevOps cycle (e.g., infrastructure monitoring, detailed tracing, deployment pipelines), Melodi focuses on the “why” – understanding the user impact and strategic direction – helping ensure your AI investments deliver tangible business value.

Was this page helpful?