Analytics Tool for LLM-powered Products

It’s like Amplitude but for

Voice Agents

Chatbots

Conversational AI

Feedback Intelligence lets you convert user interactions into “what users want to achieve”, configure metrics to measure success, debug bad responses, and get actionable recommendations on what to fix.

The Problem

Explicit feedback and standard analytics
will not help you improve your LLM app

You don’t learn much from
“churned users” dashboards

Traditional product analytics gives you downstream metrics, button clicks, and page views. You still don’t know your user intent or why they are not getting there.

Less than 10% of users give explicit feedback so it’s biased

There is no incentive for users to give a thumbs down, 2 stars rating, or fill out a support ticket. Even when they do, using that information to optimize your LLM app is hard and ineffective.

Manually reviewing conversations in a spreadsheet is dull

You end up randomly sampling user conversations and you go over them manually trying to find the root causes like bad prompts, queries, context, or knowledge gaps.

The Solution

Use Feedback Intelligence to align your LLM app
responses with user intent

Go through the product improvement loop on autopilot:
user interaction -> user intent extraction and categorization -> metrics configuration to measure success ->
‍bad response analysis -> optimization recommendations -> improved LLM app ->
‍more satisfied users -> more user interaction.

Interaction analytics

Find user intent through implicit feedback analysis

Analyzes implicit feedback like failed attempts, keywords, and success rates to gauge user intent and satisfaction.

Bad response analysis

List issues automatically and debug bad responses

Identify unmet user intent and highlight knowledge gaps, bad prompts, and bad queries. Get a list of problematic conversations and issues ranked by impact score.

LLM app optimization

Improve your LLM app through actionable recommendations

FI tells you how to fix your queries, prompts, context, knowledge base, and more. Or what data you need to gather to fine-tune the model. You can even create synthetic data and augment datasets on the spot. All are based on implicit feedback.

The only explicit feedback we actually trust

“Understanding user preferences and needs not only aids in data curation and optimizing LLM apps but also ensures that the products align with user expectations and deliver meaningful results.”

Sr. PM at a scale-up in CRM

“Continuous improvement of a chatbot is crucial to ensure it’s reliable in production and aligned with business KPIs. Without customizable metrics and actionable insights, the optimization process becomes a long, manual task.”

VP of AI at a Fortune500 Insurance company

“Implicit feedback helps reduce hallucinations and ensures more representative data, leading to more accurate and reliable conversational AI.”

Sr. Director of Data at an enterprise

Works with your LLM stack

Integrate your app with our SDKs in just 5 minutes, or simply upload your interaction data for instant analysis.
Gain actionable insights to power smarter decisions across teams.

See all integrations

See a live example of Feedback Intelligence in action

Analyze implicit feedback, diagnose bad responses, configure metrics to align with business goals,
and leverage user interaction insights to generate higher-quality synthetic data —
showcased through real-world examples.

Launch Playground

When we developed the LLM-powered AI assistant part of our product offering we implemented a direct feedback mechanism with thumbs up/down and an input field. We thought that channel was enough to know when things go wrong and then improve the assistant using that feedback.But the reality was different — it was 100 times harder to incorporate the collected feedback from the AI assistant into the improvement pipeline.Also, our users do not like to give us feedback — less than 10% of users are giving feedback so it’s biased.We added Heap as well - it only provides the number of clicks inside and outside of the AI assistant.

Director of Product Development at Fortune500

Certification

SOC 2 Type 2

Feedback Intelligence is currently in the process of obtaining SOC 2 Type 2 certification.

ISO 27001

Feedback Intelligence is working towards achieving ISO 27001 certification.

GDPR compliant

We safeguard your data through secure processing in compliance with GDPR.

It’s like Amplitude but for

Voice Agents

Chatbots

Conversational AI

Feedback Intelligence lets you convert user interactions into “what users want to achieve”, configure metrics to measure success, debug bad responses, and get actionable recommendations on what to fix.

The Problem

Explicit feedback and standard analytics
will not help you improve your LLM app

You don’t learn much from
“churned users” dashboards

Traditional product analytics gives you downstream metrics, button clicks, and page views. You still don’t know your user intent or why they are not getting there.

Less than 10% of users give explicit feedback so it’s biased

There is no incentive for users to give a thumbs down, 2 stars rating, or fill out a support ticket. Even when they do, using that information to optimize your LLM app is hard and ineffective.

Manually reviewing conversations in a spreadsheet is dull

You end up randomly sampling user conversations and you go over them manually trying to find the root causes like bad prompts, queries, context, or knowledge gaps.

The Solution

Use Feedback Intelligence to align your LLM app
responses with user intent

Go through the product improvement loop on autopilot:
user interaction -> user intent extraction and categorization -> metrics configuration to measure success ->
‍bad response analysis -> optimization recommendations -> improved LLM app ->
‍more satisfied users -> more user interaction.

Interaction analytics

Find user intent through implicit feedback analysis

Analyzes implicit feedback like failed attempts, keywords, and success rates to gauge user intent and satisfaction.

Bad response analysis

List issues automatically and debug bad responses

Identify unmet user intent and highlight knowledge gaps, bad prompts, and bad queries. Get a list of problematic conversations and issues ranked by impact score.

LLM app optimization

Improve your LLM app through actionable recommendations

FI tells you how to fix your queries, prompts, context, knowledge base, and more. Or what data you need to gather to fine-tune the model. You can even create synthetic data and augment datasets on the spot. All are based on implicit feedback.

The only explicit feedback we actually trust

Works with your LLM stack

Integrate your app with our SDKs in just 5 minutes, or simply upload your interaction data for instant analysis.
Gain actionable insights to power smarter decisions across teams.

See a live example of Feedback Intelligence in action

Analyze implicit feedback, diagnose bad responses, configure metrics to align with business goals,
and leverage user interaction insights to generate higher-quality synthetic data —
showcased through real-world examples.

Get started with Feedback Intelligence

Gain clear insights into user needs through implicit feedback, making it easy to optimize your LLM applications.

It’s like Amplitude but for

Voice Agents

Chatbots

Conversational AI

Feedback Intelligence lets you convert user interactions into “what users want to achieve”, configure metrics to measure success, debug bad responses, and get actionable recommendations on what to fix.

The Problem

Explicit feedback and standard analytics will not help you improve your LLM app

You don’t learn much from “churned users” dashboards

Traditional product analytics gives you downstream metrics, button clicks, and page views. You still don’t know your user intent or why they are not getting there.

Less than 10% of users give explicit feedback so it’s biased

There is no incentive for users to give a thumbs down, 2 stars rating, or fill out a support ticket. Even when they do, using that information to optimize your LLM app is hard and ineffective.

Manually reviewing conversations in a spreadsheet is dull

You end up randomly sampling user conversations and you go over them manually trying to find the root causes like bad prompts, queries, context, or knowledge gaps.

The Solution

Use Feedback Intelligence to align your LLM app responses with user intent

Go through the product improvement loop on autopilot: user interaction -> user intent extraction and categorization -> metrics configuration to measure success -> ‍bad response analysis -> optimization recommendations -> improved LLM app -> ‍more satisfied users -> more user interaction.

Interaction analytics

Find user intent through implicit feedback analysis

Analyzes implicit feedback like failed attempts, keywords, and success rates to gauge user intent and satisfaction.

Bad response analysis

List issues automatically and debug bad responses

Identify unmet user intent and highlight knowledge gaps, bad prompts, and bad queries. Get a list of problematic conversations and issues ranked by impact score.

LLM app optimization

Improve your LLM app through actionable recommendations

FI tells you how to fix your queries, prompts, context, knowledge base, and more. Or what data you need to gather to fine-tune the model. You can even create synthetic data and augment datasets on the spot. All are based on implicit feedback.

The only explicit feedback we actually trust

Works with your LLM stack

Integrate your app with our SDKs in just 5 minutes, or simply upload your interaction data for instant analysis. Gain actionable insights to power smarter decisions across teams.

See a live example of Feedback Intelligence in action

Analyze implicit feedback, diagnose bad responses, configure metrics to align with business goals, and leverage user interaction insights to generate higher-quality synthetic data — showcased through real-world examples.

Loved by people building LLM-powered productsTrusted by enterprises

Get started with Feedback Intelligence

Gain clear insights into user needs through implicit feedback, making it easy to optimize your LLM applications.

Explicit feedback and standard analytics
will not help you improve your LLM app

You don’t learn much from
“churned users” dashboards

Use Feedback Intelligence to align your LLM app
responses with user intent

Go through the product improvement loop on autopilot:
user interaction -> user intent extraction and categorization -> metrics configuration to measure success ->
‍bad response analysis -> optimization recommendations -> improved LLM app ->
‍more satisfied users -> more user interaction.

Integrate your app with our SDKs in just 5 minutes, or simply upload your interaction data for instant analysis.
Gain actionable insights to power smarter decisions across teams.

Analyze implicit feedback, diagnose bad responses, configure metrics to align with business goals,
and leverage user interaction insights to generate higher-quality synthetic data —
showcased through real-world examples.

Loved by people building LLM-powered products
Trusted by enterprises