Enterpret is a customer feedback intelligence platform built for product, engineering, and support teams. It aggregates qualitative feedback from support tickets, app store reviews, sales calls, and surveys into one system. Teams use it to synthesize insights and prioritize product roadmaps with structured data instead of scattered notes.
The platform connects to feedback channels and ingests unstructured text along with audio transcriptions. Its adaptive taxonomy engine uses custom machine learning models to auto-categorize, tag, and cluster data based on each company’s product architecture and terminology. This taxonomy updates continuously rather than relying on static keyword rules.
Enterpret also includes AI agents for natural language querying of the feedback database. Users can ask questions in plain language and receive synthesized summaries instead of building manual search filters. This is paired with close the loop workflows that connect feedback clusters to issue trackers like Jira and Linear.
The company markets Enterpret to mid-market and enterprise organizations that need a single system for feedback across many channels. Customer examples cited on the company’s materials include Notion, Canva, Figma, Strava, and Loom. Enterpret has raised venture funding from firms including Kleiner Perkins and Peak XV Partners.
Pricing
Enterpret does not publish plan tiers, a free trial, or a free plan on its website. Pricing is customized based on organization size, feedback volume, and connected data sources, with enterprise quotes available after a sales consultation. Some third party software directories cite a starting estimate near $1,000 per month, but this figure is not confirmed by Enterpret’s own site and should be treated as unverified. Buyers should request a direct quote to get accurate, current numbers.
* Disclaimer: Please note that pricing information may not be up to date. For the most accurate and current pricing details, refer to the official website.
Key Features
- ✓
Unifies feedback from support tickets, call transcripts, and app stores
- ✓
Adaptive taxonomy engine that updates categories as products change
- ✓
AI agents for natural language querying of feedback data
- ✓
Automatic clustering that surfaces bugs and feature request patterns
- ✓
Custom dashboards for sentiment trends and feedback volume tracking
- ✓
Close the loop integrations with Jira and Linear for engineering teams
Use Cases
Data-Driven Roadmap Prioritization
Product managers aggregate feature requests across support channels, sales calls, and app reviews. Filtering by account revenue tier helps teams quantify business impact before committing roadmap time.
Post-Launch Regression Monitoring
After a major release, teams monitor incoming feedback through automated clustering. Emerging friction points and bugs group together quickly, letting teams patch issues before complaints escalate.
Support Triage and Escalation
Support leaders identify recurring ticket themes and cross reference them with engineering backlogs. Direct links from ticket clusters to Jira streamline how issues reach development teams.
Competitive Sentiment Analysis
Product and marketing teams ingest call transcripts and public reviews to track competitor mentions. Categorized complaints and praise give quantitative backing for positioning decisions.
Executive Stakeholder Reporting
Leadership teams use automated dashboards to monitor sentiment trends across core product areas. Thousands of raw responses become visual summaries for quarterly reviews without manual compilation.
Strengths & Weaknesses
Strengths
Unifies disparate feedback channels, including tickets, call logs, and app reviews, into one repository.
Adaptive taxonomy updates categorization models as product features change without manual rule maintenance.
Native Jira and Linear integrations map customer feedback directly to engineering workflows.
Natural language AI agents let team members query unstructured feedback conversationally.
Metadata filtering lets teams segment feedback by customer revenue, plan tier, and account cohort.
Weaknesses
Pricing, tier structures, and trial options are absent from the website, requiring a sales call.
An initial data connection and taxonomy training phase is required before classification is optimized.
Self-serve plan purchasing and instant account provisioning are not available on the website.
Non-digital feedback requires manual upload, since native offline ingestion is not built in.
Who Is This For?
Product Managers: need to quantify qualitative feedback and validate roadmap decisions with structured sentiment data.
Customer Support and Operations Leaders: need to identify ticket drivers and escalate technical regressions to engineering.
User Research Teams: need to analyze large volumes of survey responses and interview transcripts at scale.
Product Marketing Leaders: need to track competitor sentiment and evaluate customer perception from real interactions.
Frequently Asked Questions
Does Enterpret offer a free trial?
Not publicly. Organizations must request a demo through the website to evaluate the platform.
How much does Enterpret cost?
Pricing is custom and based on organization size, feedback volume, and connected sources. No published tiers exist, so a sales quote is required for exact numbers.
How does the adaptive taxonomy differ from keyword tagging?
It uses machine learning models that adjust tags based on context and product changes, rather than relying on static keyword rules.
Is there a learning curve during setup?
Yes. An initial data connection and taxonomy training phase is needed before classification accuracy is optimized.
Can Enterpret connect feedback to revenue data?
Yes, through its Salesforce integration, teams can filter feedback by customer account size, ARR, or plan tier.
What issue-tracking tools does Enterpret support?
Enterpret natively integrates with Jira and Linear, connecting feedback clusters directly to development backlogs.
Who is Enterpret best suited for?
Mid-market and enterprise product, support, and customer experience teams handling feedback across many channels.
Can non-digital or offline feedback be added?
Native offline ingestion is not built in, so non-digital feedback requires manual upload and transformation.
Does Enterpret support querying data without writing filters?
Yes, its AI agents allow conversational natural language queries against the feedback database instead of manual filters.
Is self-serve account setup available?
No. Account provisioning requires a sales consultation rather than instant self-serve signup.
Enterpret integrates with Zendesk and Intercom for support tickets and chat transcripts, Gong for sales call recordings, Jira and Linear for engineering backlogs, Slack for anomaly alerts, Salesforce for CRM account data, Google Play Store and Apple App Store for public app reviews, and Typeform or SurveyMonkey for survey responses.