Melodi

Start driving real ROI

and take the guesswork
out of building AI Agents

Melodi flags costly failures in real conversations—before they damage trust, retention, or revenue.

Get started

Read the docs

Start driving real ROI

and take the guesswork
out of building AI Agents

Melodi flags costly failures in real conversations—before they damage trust, retention, or revenue.

Get started

Read the docs

improve your ai agents

Melodi is a data scientist-in-a-box for product teams building and managing AI agents

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

Why Most Teams Struggle with AI Agents

Manual analysis of AI conversations leads to guesswork, not data-driven decisions

AI vendor analytics are biased toward showing positive performance

The best fixes often aren't more ML, but changes to UX, documentation, or processes

Melodi's Approach to Measurable AI Success

Identify user intents and quantify which interactions are failing your customers

Size and prioritize opportunities based on business impact, not just technical fixes

Apply the Melodi Method to systematically improve your AI's performance and ROI

with melodi

Achieve real goals with your AI agents

Agentic AI Products

Increase adoption and retention and reduce AI agent errors.

Customer Support Agents

Improve your digital resolution rate with more successful conversations.

core capabilities

Melodi provides essential tools for AI Product Managers

Leverage automated LLM-based evaluations to gain deeper insights into session outcomes and user intents.

Melodi Intelligence and Automated Evaluations

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.

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.

User feedback, Human Review, and Data Labelling

the melodi method

A product-centric approach for user-driven AI improvement beyond technical tweaks

A product-centric approach for user-driven
AI improvement beyond technical tweaks

Understanding User Goals

What are users really trying to achieve?

Segmentation

How do different user groups experience the agent?

Diverse Problem Types

ML model issues are only one part of the picture

Update Knowledge / Source Content
AI responses are incorrect, outdated, or incomplete due to issues in the underlying documentation or knowledge base used for retrieval (RAG).
Adjust AI Behavior (Prompts/Models)
The AI’s core logic, response generation, or output needs refinement via changes to prompts, model configuration, or fine-tuning.
Conversation Design & UX Improvements
The AI might be technically correct, but the interaction flow or presentation is confusing, awkward, or unhelpful.
Support or Escalation Workflow Fixes
Issues arise from how the AI handles situations it cannot resolve, such as ineffective routing to human support or poor handoff.
Product or System Changes
User frustration or confusion stems not from the AI itself, but from underlying issues or complexities in the main product or system the AI assists with.