Nebuly is a paid tool. Please visit their website for pricing details.

Nebuly

Enterprise analytics that shows how employees and customers use AI agents.

paid

Last Updated 8/28/2026

About Nebuly

Nebuly is an enterprise user analytics platform for AI agents. It organizes employee and customer conversations into signals about adoption, unmet needs, response quality, and return on investment. Teams send interaction data through the Interaction API, then examine what people ask, how agents answer, and whether those answers satisfy user needs. The dashboard groups conversation topics and engagement metrics, helping product, operations, and business leaders spot recurring demand, prioritize agent improvements, and assess where AI use creates value. Nebuly is built for organizations running AI agents at enterprise scale. Its sales process starts with a demo, while a browser playground is available for evaluation. Pricing is commercial and quote based, so buyers must contact Nebuly to discuss cost, deployment terms, interaction volume, and governance requirements.

Best for

Enterprises running internal AI agents for employees

Companies analyzing customer-facing AI agent conversations

Product and operations teams responsible for agent adoption

Best use cases

Measure how employees or customers use an AI agent

Identify recurring questions and unmet user needs

Prioritize improvements using conversation and ROI signals

Compare before choosing

Choose Nebuly when conversation-level agent analytics are the main requirement

Compare the quote-based cost with tools that publish self-serve prices

Confirm the Interaction API fits the existing data pipeline before purchase

Features

Adoption analysis

Track conversation topics, interaction volume, and recurring behavior across employee or customer workflows. See which groups return to the agent, where usage concentrates, and where adoption remains weak enough to require training or product changes.

Unmet-needs discovery

Review repeated questions with agent responses and outcomes to identify needs the current experience does not resolve consistently. Use those findings to improve answers, add capabilities, or redesign workflows around recurring requests.

Value and improvement analysis

Compare satisfaction, adoption, and ROI signals found in conversations across workflows. Use that view to focus engineering and enablement work on agent experiences with the clearest user impact or value gap.

Pros

Conversation-level analysis connects user questions with agent responses and practical adoption signals.
The Interaction API gives technical teams a defined route for sending interaction data into the platform.
Topic and interaction views can help teams prioritize improvements based on observed user behavior.

Cons

Quote-based pricing makes it harder to estimate cost before speaking with the sales team.
Adoption requires sending interaction data through an API, which adds engineering and data-governance work.
The enterprise focus may add more sales and implementation overhead than a small team needs.