Enterpret

What Enterpret does

Enterpret is a Customer Intelligence Platform that uses custom AI and advanced LLMs to transform customer feedback from various sources into structured context. It helps product and CX leaders understand customer signals, prioritize what to build or fix, and drive retention and revenue. The platform connects support, sales, and market signals, organizing feedback into themes that evolve with the product. It ties answers to customer context, product areas, and business outcomes, enabling teams to track impact and make data-driven decisions. Enterpret aims to provide superintelligence that feels like intuition, ensuring no customer signal is missed.

Products

• Enterpret Customer Intelligence PlatformA platform that connects support, sales, and market signals into structured context for teams and AI to drive retention and revenue. It helps prioritize what affects churn, NPS, CSAT, expansion, adoption, and roadmap decisions in real-time.
• Context GraphConnects customer signals to features, issues, segments, and business outcomes. It attaches segments, LTV, lifecycle stage, usage, and product areas to every signal, preserving customer, product, and business relationships for workflows and AI systems.
• Enterpret MCP (Multi-Channel Platform)Allows creation of tickets, alerts, and workflows directly from findings without copying results or rewriting context. It enables querying and acting on feedback from Claude, ChatGPT, and internal tools.
• Adaptive TaxonomyLeverages AI to automatically classify insights into a 5-level hierarchy, uncovering the 'why' behind every feedback. It's auto-learning and aligns with organizational structure for higher-quality insights and zero maintenance.
• Customer Knowledge GraphConnects feedback to customers, features, and revenue, quantifying the business impact of every feature by linking customer feedback with product and revenue data. It unifies feedback from 50+ sources and performs cross-source entity resolution.
• Data EnrichmentExtracts deeper insights by enriching feedback with AI-powered workflows and standardized fields for deeper customer and product context. It uses custom AI prompts to surface sentiment shifts, competitor mentions, and other signals.

Key capabilities

• AI-accelerated customer analysis
• Structure that stays consistent (feedback organized into evolving themes)
• Customer context that holds (feedback tied to source, product, importance)
• Feedback loop that proves impact (track changes from ticket volume to retention)
• Structure customer signals into shared themes and categories
• Evolve with customer language, products, and use cases
• Reinforce existing understanding instead of rebuilding from scratch
• Create a shared understanding of the customer journey
• Connect insights to outcomes (Context Graph)
• Activate where work happens (MCP)
• Connect feedback across every source (50+ integrations)
• Tie each issue to impact and context
• Defend what to ignore, fix, or build with evidence
• Ask anything about customers and get evidence-backed answers
• Prioritize issues by customer impact
• Drive action on what to fix, escalate or monitor
• Auto-classification on ingestion
• Ground customer feedback with business context (upload product docs, help articles)
• Webhook API for programmatic feedback push
• Data Warehouse Sync
• File Upload for bulk historical data import
• Enterprise-grade governance & compliance (admin controls, PII protection, 24/7 monitoring)
• Adaptive Taxonomy for automatic feedback classification
• Auto-learning taxonomy aligned to org structure
• 5-level theme & tag hierarchy (L1/L2/L3 Keywords, Themes and Sub-themes)
• Assign ownership for insights
• Custom AI Models for terminology recognition
• White-Glove Calibration for day-one accuracy
• Explainable insights (rationale behind classification)
• Intelligent editing with AI-assisted validation
• Proactive maintenance (detect drift, duplicates, emerging terms)
• Cross-source entity resolution
• Knowledge Manager for users, accounts, and connected entities
• Custom object modeling (define business entities)
• Flexible schema evolution
• Business-specific hierarchies (org structures, product portfolios)
• Context-driven analysis (filter by revenue, CSAT, NPS, region, custom attributes)
• Relationship intelligence (surface patterns across connected entities)
• Custom AI prompts for data enrichment
• Ingest & Batch Workflows for enrichment
• Enrichment Function Library
• Compute key metrics from raw feedback via AI prompts and Python functions
• Real-time performance tracking for metrics
• Unify cross-source fields
• Data Clean up & Standardization
• Ingestion blockers for sensitive fields
• Real-time anomaly detection and alerts
• Sentiment analysis on feedback
• Dashboards & Reports
• AI Insights Agent
• MCP Server Integration (Slack, ChatGPT)

Use cases

• Prioritizing what to fix or build next to affect churn, NPS, CSAT, expansion, adoption, and roadmap decisions
• Understanding patterns before churn by user segment and timing
• Identifying what to fix first for highest-value users
• Evaluating if fixing onboarding improved retention
• Identifying issues blocking high-value deals
• Understanding why customers contact support multiple times for the same issue
• Determining what drives low adoption for specific features among premium users
• Turning AI summaries into roadmap decisions you can trust
• Connecting customer feedback to product issues, ranked by impact and backed by evidence
• Unifying every feedback source into one structured view of product issues, usage impact, and evidence
• Making roadmap calls, aligning stakeholders, and avoiding prioritizing on volume
• Building AI workflows & agents on top of customer understanding
• Answering any question about customer feedback with evidence
• Driving action on what to fix, escalate or monitor for CX teams
• Analyzing customer feedback across tickets, calls, surveys, reviews, and other channels
• Identifying recurring, growing, and impactful issues for CX teams
• Reducing churn and repeat support by tracking issues across various sources
• Validating features before building them by connecting user feedback to revenue
• Monitoring issues and trends in real-time to catch problems early
• Improving app store ratings and customer pain by analyzing reviews
• Product launch readiness and tracking success
• Product discovery and investigation
• Root Cause Elimination (RCE) for customer churn or friction
• Self-serve research by combing through call transcripts
• Trend analysis on the voice of customers, tracking keywords, feature requests, and market comparisons

AI approach

Enterpret uses custom AI, advanced LLMs, and machine learning to transform customer feedback into structured, actionable insights. It builds an "Adaptive Taxonomy" that automatically classifies feedback into a multi-level hierarchy, learns from product changes and customer language, and identifies patterns. The platform also uses AI for data enrichment, custom AI prompts to extract signals, and real-time anomaly detection. It connects customer signals to context graphs, unifying user identities across sources and linking feedback to business outcomes like churn, NPS, CSAT, and revenue.

Tech named: Custom AI, advanced LLMs, machine learning, Adaptive Taxonomy, AI prompts, AI workflows, AI agents, AI-powered enrichments, AI-assisted validation, real-time anomaly detection

What it says sets it apart

• Custom AI to transform customer understanding
• Harnesses superintelligence that feels like intuition
• Goes beyond AI summaries to connect root cause, impact & evidence
• Structures feedback by context and impact, not just surface themes
• Connects customer warnings to a live view of issues, evidence, and impact
• Eliminates manual tagging with an adaptive, auto-learning taxonomy
• Grounds customer insights with business context (product docs, help articles)
• Automatically organizes feedback into a 5-level hierarchy
• Continuously learns from product launches, feedback patterns, and language shifts
• The only platform that ties insights to business context to prioritize impact, not volume
• Unifies the same user across multiple sources into a single identity (Customer Context Graph)
• Allows defining custom business entities and relationships
• Transforms qualitative feedback into quantifiable business insight
• Extracts deeper insights with AI-powered workflows and standardized fields
• Quantifies the impact of feedback on key metrics automatically
• Makes feedback data consistent and comparable across sources
• Provides real-time performance tracking for metrics
• Offers enterprise-grade governance and compliance (SOC 2 Type 2, ISO/IEC 27001, ISO/IEC 42001)

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.