Enterpret mcp for Claude, Notion & more
Customer intelligence infrastructure for teams building with Al
Enterpret connects support, sales, and market signals into structured context teams and Al can use to drive retention and revenue.
Al accelerated customer analysis.
It's still hard knowing what to fix or build next.
Enterpret maintains the structure, context, and evidence teams need to prioritize what affects churn, expansion, adoption, and roadmap decisions.
From one-off answers to a system your product and CX teams run on
Feedback is organized into themes that evolve with your product so answers don’t change every time
Every answer is tied to who it came from, what part of the product it relates to, and how important it is
Track what changed after every decision from ticket volume to retention so you know what worked
Powered by the infrastructure that keeps every signal connected and measurable
Adaptive Taxonomy
Structure customer signals into shared themes and categories, so every team and AI workflow operates from the same understanding
Context Graph
Connect customer signals to the feature, issue, segment and business outcomes tied to them
Enterpret MCP
Create tickets, alerts, and workflows directly from findings without copying results, rewriting context or follow-ups
Built for every customer signal
Customer Intelligence
Prioritize what to build based on what drives retention, revenue, and product demand across all customer feedback
Support Intelligence
Resolve what’s driving ticket volume and repeat contacts across all support interactions
Sales Intelligence
Fix what’s blocking revenue and capture product demand from sales conversations and CRM data
Market Intelligence
Win where customer needs and competitors are shifting using external reviews, social, and market signals
Frequently Asked Questions
AI product feedback analysis uses artificial intelligence to automatically collect, categorize, and surface insights from customer feedback across every channel — support tickets, NPS surveys, app store reviews, sales calls, Intercom conversations, and more. Product teams use it to validate features before building them, size demand against real customer signals, detect issues in real time, and prioritize roadmaps with conviction instead of guesswork.
The old way: a spreadsheet, a Slack channel full of screenshots, and someone's weekend. Most teams manually process a fraction of the feedback they receive. The rest gets ignored — which means bugs go undetected, winning features get deprioritized, and churn signals are missed until it's too late.
Enterpret's AI changes the math. Using natural language processing, adaptive topic modeling, and sentiment analysis, it automatically categorizes every piece of feedback across all your sources — eliminating human bias, processing thousands of signals simultaneously, and surfacing the patterns that actually move product decisions.
AI tools can generate answers from customer signals quickly, but the underlying understanding often resets with every new prompt, workflow, or analysis.
Enterpret provides the infrastructure layer underneath those workflows by continuously structuring customer signals into shared customer understanding tied to product context, customer segments, and business outcomes.
This allows teams to:
- Prioritize based on business impact instead of mention volume
- Maintain consistent understanding across workflows
- Connect customer understanding to retention, adoption, expansion, and support outcomes
- Operationalize insights directly into workflows and systems
Enterpret works as the infrastructure for AI workflows, rather than replacing them.
Enterpret analyzes support tickets, NPS and CSAT survey responses, app store reviews (iOS and Google Play), sales call transcripts, Intercom and Zendesk conversations, social media, G2 and Trustpilot reviews, Slack messages, and more. Any channel where customers express themselves — Enterpret reads it, categorizes it, and surfaces the signal.
Product teams use Enterpret to size feature demand against real signals — how many customers asked for it, what revenue is at risk, which customer segments are affected, and how it connects to NPS or churn trends. That context replaces "the loudest voice wins" prioritization with a defensible, data-backed case for every roadmap decision.
Manual analysis forces teams to tag feedback one ticket at a time — a process that introduces human bias, takes weeks, and covers only a fraction of what customers actually said. Enterpret processes thousands of signals simultaneously, without bias, and delivers answers in hours. It also connects insights directly to Jira, Linear, and Slack — so nothing sits in a doc waiting to be acted on.
Enterpret's adaptive taxonomy self-organizes around your product's language — not a generic category list — and connects feedback to customer context like ARR, plan, and lifecycle stage. Unlike tools that analyze feedback in isolation, Enterpret routes insights directly into your existing product workflows via Jira, Linear, Slack, and other integrations.
