Enterpret

A one-sentence summary

Enterpret is an enterprise-level platform for customer intelligence and feedback analysis; it enables the integration of support tickets, surveys, app store reviews, sales calls, and social media content, and then transforms this data into actionable insights about products and customers through knowledge graphs, adaptive classification, Wisdom AI, and proactive agents.

Tool Introduction

Product, customer experience, and Voice of Customer teams often encounter issues such as fragmented feedback, inconsistent labeling, incomplete samples, and results that are difficult to act upon. Enterpret follows the approach of \"unification, understanding, and action,\" bringing feedback, accounts, users, products, opportunities, and business metrics together within the same context.

This platform is more suitable for digital companies that receive a large number of feedback entries each month, rather than individuals who collect a small number of surveys. Officials recommend that teams should have at least around 1,000 feedback entries per month, coming from sources such as support services, social media, or communities, in order to achieve consistent benefits.

The core architecture of Interpret 2.0

Main functions

Customer Knowledge Graph

Knowledge graphs automatically connect feedback, users, accounts, opportunities, products, and custom business objects, thereby adding context such as ARR, package type, lifecycle stage, region, or opportunity phase to each entry. Teams can prioritize handling issues related to high-value accounts or specific product lines, rather than relying solely on the frequency of mention.

Adaptive Taxonomy

An adaptive classification system learns the product terminology of a company and adapts to changes in business operations; it does not require the team to manually maintain a set of universal tags over the long term. Users can view the reasons behind classifications, edit keywords and topics, and correct the results at the level of individual records.

The help center describes them as L1, L2, and L3 level keywords, along with two semantic layers: Theme and Subtheme; the official website also states that a five-layer structure can be formed. These two descriptions reflect different approaches to categorizing keywords and themes.

Automatic intent classification

The platform categorizes feedback into types such as Help, Improvement, Complaint, and Praise, which helps the team distinguish between requests for assistance, suggestions for improvement, complaints, and compliments. Intent tags can be used in conjunction with topics, emotions, and customer attributes for filtering.

Wisdom AI

Wisdom is a conversational assistant focused on customer intelligence; users can ask questions in natural language regarding reasons for customer churn, functional requirements, changes in NPS, or feedback from specific customer groups. The responses can include narratives, charts, data tables, and direct links to the original feedback, and multiple follow-up questions are supported.

Model selection and Wisdom Rules

The models listed in the current help documentation are Sonnet 4.6 and Opus 4.6; the former is the default option, as it prioritizes speed, while the latter is suitable for more complex analyses. Common questions and procedures can be saved as Wisdom Rules for personal reuse or sharing within a workspace.

Proactive AI Agents

Agents continuously monitor new feedback and notify the team proactively when abnormalities, escalation risks, or significant trends arise. They enable the team to shift from periodic manual searches to continuous monitoring, but alert thresholds, duplicate merging, and responsibility routing still need to be configured.

Quantify, Trends, and Dashboards

Quantify is used to view the quantity and structure of feedback within a classification system, while Trends is used to track how topics, product categories, or customer segments change over time. Dashboards enable the creation of shared views of key metrics, which can be accessed continuously by product teams, customer experience teams, and management.

Anomaly detection

Anomaly detection alerts the team when the feedback pattern deviates significantly from the normal state, such as a sudden increase in complaints or rapid changes in functional requirements. An anomaly merely indicates statistical or behavioral changes; it is necessary to review the original feedback and business events to determine the cause.

Data augmentation and unified fields

The platform allows the addition of built-in enhanced fields such as mood changes, total response time, standardized country codes, and timestamp conversion. Customers who require specific calculations or business rules can have custom enhancements configured through the customer success team.

Saved Items

Users can save analyses, dashboards, and insights to avoid creating the same views repeatedly. It is suitable for storing fixed entries such as weekly meetings, roadmap reviews, customer risks, and post-release monitoring.

Supported feedback channels

Enterpret states explicitly that it can connect to more than 50 different platforms, covering support, survey, review, sales, social networking, and collaboration channels. The specific connectors, synchronization directions, and fields may vary; therefore, it is necessary to refer to the current integration catalog and scope of implementation when making purchases.

The workflow from feedback to action

1. Identify the feedback channels, customer master data, product hierarchy, key metrics, and questions that the team needs to answer.
2. It supports connections to various services such as surveys, reviews, calls, CRM systems, and social media channels, while also verifying time details, identities, and field mappings.
3. Knowledge graphs connect the multi-channel feedback from the same user and account to product, opportunity, and business attributes.
4. An adaptive classification system extracts keywords, topics, subtopics, intents, emotions, and other enhanced fields.
5. The team raises questions and examines the original evidence using Quantify, Trends, dashboards, or Wisdom.
6. Agents continuously monitor for anomalies and high-risk signals, sending alerts to Slack or the relevant responsible persons.
7. Confirmed bugs, requirements, or customer actions are synchronized to Jira, Linear, and other workflow tools, with tracking to ensure completion.

Usage tutorial

Prepare for data integration

1. List all feedback sources and their responsible persons, and determine which fields contain user, account, revenue, and product context.
2. Select the initial use cases, such as roadmap prioritization, churn risk, version quality, or the weekly Voice of Customer report.
3. Remove duplicate identities, invalid accounts, and sensitive fields, and configure Ingestion Blockers and Scrubbing rules.
4. The administrator connects to the data source, first synchronizes a limited range of historical data, and then compares the number of records in the source system with those in the platform.
5. Check whether the associations between different tickets, comments, surveys, and calls for the same user are correct, and then expand the scope of synchronization.

Establish an adaptive classification system

1. Import product hierarchies, feature lists, internal terminology, and existing feedback categories as business context.
2. Review the keywords and topics, as well as subtopics, from L1 to L3 to ensure that the structure is capable of addressing product and CX-related questions.
3. Randomly sample original feedback from each main category to record misclassifications, omissions, and semantic overlaps.
4. Edit the name, definition, or individual category directly to keep the structure in line with the current product version.
5. After introducing new features or entering new markets, check for category drift to prevent historical trends from being distorted by changes in measurement criteria.

Use Wisdom to study problems.

1. First, clarify the time frame, customer segments, product regions, and the business metrics to be compared.
2. Pose a verifiable question in natural language, such as why corporate clients have been complaining about the onboarding process recently.
3. Select Sonnet 4.6 or Opus 4.6 depending on the task’s complexity, and wait for the background analysis to complete.
4. View the charts, data tables, and references in the answers to examine the context of the representative original feedback.
5. Continue to inquire about differences among different customer groups, versions, or regions, and record the filtering criteria and parameters.
6. Save stable and recurring issues as Wisdom Rules for reuse in weekly reports, reviews, or shared workspaces.

Configure active proxy

1. Select the quality, upgrade, or information summary scenario, and specify the datasets and topics that need to be monitored.
2. Set parameters such as account, mood, amount of feedback, product category, or other conditions to control the scope of alerts.
3. Specify recipients via Slack, email, or the workspace, and define severity levels and response owners.
4. Use historical data to test the number of alerts, and adjust the thresholds in order to reduce noise and duplicate notifications.
5. After going live, regularly check for false positives, false negatives, and the actual outcomes of processing, in order to optimize the rules.

Workflow integration

MCP Server

Enterpret MCP Server enables external AI tools to access the organization’s complete feedback dataset, returning information on topics, accounts, emotions, original statements, and related fields. The aggregated data can be linked back to the records in the platform, facilitating the transition from AI-generated conclusions to the underlying evidence.

Different clients can use OAuth for connection or generate Bearer tokens; the specific method is determined by the client and the organization’s settings. MCP brings enterprise customer data into a new AI interaction interface, and administrators must control who is authorized to access this data, how tokens are refreshed, and what scope is allowed for data sharing.

MCP connection steps

1. The administrator determines which AI clients are allowed to access, what data is permissible, and what user roles are available.
2. Enable MCP in the Interpret settings, and select OAuth authorization or generate a dedicated access token based on the client.
3. Add connectors to Claude, Cursor, Notion, Glean, or other compatible tools.
4. First, use low-sensitivity questions to test entities, classification fields, aggregated numbers, and reference links.
5. Check the client’s settings for chat retention, training, sharing, and auditing before enabling official use.
6. Regularly review connected users, revoke permissions for those that are no longer in use, and rotate long-term tokens.

User roles and permissions

New users are assigned the Member status by default. Administrators, Editors, and Members can create or edit proxies, but Members can only edit the proxies they have created themselves; Viewers cannot create or modify any proxies.

Which users are it suitable for

• Product Team:Quantify functional requirements, identify bugs, assess the impact on different user groups, and provide evidence for the roadmap.
• Voice of Customer team:Unify multi-channel communications by creating weekly and monthly reports as well as management scorecards.
• Customer Experience Team:Track changes in NPS, CSAT, complaint themes, and service processes.
• Customer Success Team:Identify emotional shifts, signs of account loss, and actions that need to be taken regarding strategic accounts.
• Support team:Abnormalities in work orders, duplicate issues, and changes in version quality were detected.
• Sales and Marketing Team:Summarize competitors, objections, customer language, and customer success factors from calls and feedback.
• Management:Understand customer needs and business impacts through a unified dashboard, rather than relying on individual cases.

Typical use cases

• Calculate how many customers are involved in a particular functional requirement, what amount of revenue it generates, as well as which packages and life cycle stages are related to it.
• Abnormal complaints are detected before the support queue is expanded, and the affected versions, regions, or product areas are identified.
• Analyze the reasons for customer churn and verify them by reviewing the original tickets, calls, and surveys.
• Automatically convert confirmed bugs into Jira issues, and send feature requests to Linear along with the relevant customer context.
• Generate weekly Voice of Customer summaries to deliver trends related to their respective areas of responsibility to different teams.
• Compare the changes in themes, emotions, and NPS before and after the release of a new product to determine whether the product decisions have improved the user experience.
• Create competitive intelligence by extracting the actual customer statements from sales calls, reviews, and support feedback.

Product advantages

• Link feedback to accounts, revenue, opportunities, and products, avoiding sorting solely based on volume.
• Adaptive classification reduces the need for manual label maintenance and allows users to view and correct classifications.
• Wisdom provides responses along with original feedback references, which facilitate the verification of the narratives and figures generated by AI.
• Proactive agents continuously identify anomalies and upgrade risks, reducing the reliance on regular manual searches.
• Over 50 feedback sources, together with Jira, Linear, and Slack workflows, create a closed loop that covers everything from data collection to taking action.
• MCP and Export API enable customer intelligence to be fed into AI clients and data warehouses.
• Roles, SSO, SCIM, PII processing, and SOC 2 Type 2 are suitable for enterprise security procurement processes.

Usage restrictions and precautions

• The platform is primarily aimed at companies that receive a large volume of feedback; small teams or those with very few data points per month may find it difficult to demonstrate the value of their investments.
• AI sentiment, topic, and intent classification may be influenced by language, irony, context, and training distribution.
• Errors in account merging combine feedback from different people, which in turn affects revenue and churn analysis.
• Adjustments to the classification system can change the way trends are presented; it is therefore necessary to keep track of the definitions, versions, and changes made.
• The answer provided by Wisdom still needs to be verified by checking the references; the generated summary cannot be regarded as the final decision.
• Integration with Slack, sales calls, and support systems involves handling sensitive customer information, which requires minimalization of data and strict access controls.
• When MCP exposes feedback to external AI clients, it increases data retention, sharing, and permission boundaries.
• The official website does not disclose standard prices or a free version; the costs associated with procurement, implementation, and security assessment need to be evaluated separately.

Safety and privacy

Enterpret publicly releases its SOC 2 Type 2 reports, stating that its systems for security, privacy, and AI governance are based on frameworks such as ISO 27001, ISO 42001, and ISO 27701. The fact that these frameworks are used as a reference does not imply that each one has been independently certified; companies should request the relevant scope documents.

The platform is hosted in Amazon Web Services in the United States; data transmission takes place using secure protocols such as TLS 1.2, and static data is encrypted with AES-256. Multi-tenant data is logically isolated through company-, user-, and role-based access controls.

PII and data minimization

Customer feedback submitted to the platform has its personal identity information scanned and removed or masked. Customers can also set up Ingestion Blockers and Scrubbing rules to prevent certain types of content from entering the platform or to remove sensitive portions from the data.

Data subject rights and deletion

Under the GDPR framework, the customer is usually the data controller, while Enterpret acts as the processor to assist with requests to access, correct, or delete data. Upon termination of the contract, a written request from the customer can initiate the process of deleting the data; this process begins 30 days after the termination takes effect and is completed within approximately 3 to 4 weeks.

Prices and Purchases

As of August 22, 2026, Enterpret does not offer any standard packages, pricing per seat, a free version, or a trial period; the official website provides access mainly through reservation for demonstrations. The services and associated costs are specified in the corporate order, with the default payment term being annual prepayment within 30 days of receiving the invoice, unless otherwise agreed in the order.

It should be confirmed at the time of quoting.

• The fees are calculated based on the named user, the amount of feedback, the data source, historical data retrieval, or the functional modules.
• Do Wisdom, Opus models, Agents, MCP, Export API, and advanced security features fall under the basic order options?
• There are limits on the number of hours of support that can be provided, as well as additional fees for services and costs related to custom integrations.
• An increase in the amount of feedback, more data sources, and price adjustment rules at the time of contract renewal.
• Export format after contract termination, deletion time, and MCP authorization revocation process.

APIs, SDKs, and open-source status

Enterpret offers the Export API 2.0, which is used to export platform data to data warehouses, RevOps dashboards, product analysis tools, or customer success reporting systems. Specific endpoints, authentication details, pagination options, rate limits, and field information can be found in the help center or within the customer’s environment.

The platform also offers hosted MCP Servers, but no official open-source SDKs, platform source code, or model weights have been made available. The Export API and MCP are features related to the integration capabilities of commercial platforms; therefore, Interpret cannot be classified as open-source based on these elements.

Basic information

Recommendation score

Recommendation score: 4.5 / 5. Enterpret is suitable for large and medium-sized enterprises that have to process a large volume of feedback on a monthly basis, need to link customer feedback to its impact on accounts and revenue, and wish to move directly from the insights obtained to the development of their products and services.

The main shortcomings are the lack of transparency in pricing, high requirements regarding implementation and data management, and the need for occasional checks to ensure accurate AI classification and identity association. Teams that have limited data or only need simple survey-based statistics may not require such a comprehensive platform.

Frequently Asked Questions

Is Enterpret free?

No public, permanently free version, standard trial quota, or self-service pricing has been found. Teams usually need to schedule a demonstration, and the price quote is determined based on the amount of feedback, data sources, as well as users and features required.

Is Enterpret suitable for small teams?

It is possible to conduct an assessment, but official recommendations suggest that around 1,000 or more feedback entries per month are needed in order to better capture the true value of such feedback. When there is little feedback, simple forms, survey tools, or manual analysis may be a more cost-effective approach.

What model does Wisdom AI use?

The current help center lists Sonnet 4.6 as the quick default model, while Opus 4.6 is presented as a more advanced option. The available models and order permissions may change with platform updates.

Can the answer return to the original feedback?

Yes. Wisdom supports one-click citation; the insights and aggregated figures included in the answers can be linked to the original feedback available on the platform, facilitating manual verification.

Which classification levels are supported?

The help center explains the L1, L2, and L3 levels of keywords, as well as separate topics and subtopics. The five-layer structure mentioned on the official website can be understood as including these two types of topic layers as well.

Can it automatically create Jira or Linear issues?

The platform allows insights to be sent to Jira and Linear, where feedback can be linked, context can be enhanced, and updates can be tracked. The automatic creation of fields, as well as the availability of bidirectional fields and status ranges, depend on the integration settings.

Is it compatible with ChatGPT, Claude, and Cursor?

Access to customer intelligence is possible through MCP. Different clients have varying connection methods, payment requirements, and administrator permissions; it is necessary to check the settings regarding data retention and sharing before enabling this functionality.

Is an API provided?

The Export API 2.0 is provided for efficient export to data warehouses and downstream tools. It is not an anonymous public interface; enterprise authentication and appropriate permissions are required.

Is Enterpret open source?

It is not open source. The platform, Wisdom, and agents constitute commercial services; making the API and MCP connections available does not mean that the source code or model weights are made public.

How will customer data be protected?

The platform offers SOC 2 Type 2 certification, encryption for data at rest and in transit, logical isolation, role-based permissions, as well as rules for scanning and removing PII. Companies should still review the current audit reports, sub-processors, DPA agreements, data locations, and the terms of use for the models.

Is SSO and automatic user management supported?

It supports OpenID identity providers, and offers SAML configuration for Okta and Azure AD; Okta and Azure also support automatic provisioning via SCIM 2.0. The specific capabilities should be specified in the order and tested before going live.

Summary

Enterpret transforms the diverse feedback from customers into a knowledge graph that includes information on accounts, products, and business impacts; it then uses adaptive classification, Wisdom AI, trend analysis, and proactive agents to help teams identify issues. Jira, Linear, Slack, MCP, and Export API further enable the application of these insights in the actual execution environment.

Its true value lies in high-quality identity mapping, consistent classification methods, and a verification process that allows for feedback to be traced back to its source. Before proceeding with procurement, it is necessary to conduct trials using real data in order to assess the connector’s performance, accuracy, permissions, the models used, the location of the data, as well as the cost of customization, before deciding whether to expand the deployment.