AI customer intelligence and taxonomy · PUBLIC RESEARCH BRIEF
EnterpretHow can a feedback taxonomy evolve without breaking the trend?
Enterpret describes unifying feedback from more than 50 sources, enriching it through a customer knowledge graph, classifying it with an adaptive taxonomy and supporting source-linked questions through Enterpret Agent or AI Insights. A taxonomy that changes with the business may surface new language and products, but teams still need versioning and review so a reported trend is not created by classification changes.
Updated 2026-10-25 · Simulation results not yet generatedCHANGE ONE THING. LEARN WHAT MATTERS.
Three questions for the GTM team.
When raw feedback volume conflicts with account, revenue or product context in the knowledge graph, which interpretation should be presented first and which sensitivity checks should accompany it?
Set up this study →What source coverage, citation sample and contradiction review should be required before an Agent answer is used in a product or customer-experience decision?
Set up this study →PROPOSED AUDIENCE
Who should weigh in?
North American voice-of-customer leaders, insights teams, product managers, customer-experience teams, support operations, sales operations, data analysts and governance reviewers evaluating Enterpret. Include people who maintain sources, interpret themes and use account or revenue context. Proposed audience only; no Enterpret workspace, source record, taxonomy, customer profile, quote or account data is included.
TWO TIME HORIZONS
Trial today. A habit tomorrow?
Near term · 0–90 days
Over 0–90 days, load a synthetic multi-source corpus with product-name changes, multilingual phrasing, duplicate records, uneven channel coverage and versioned account context. Compare taxonomy changes, historical backfills, trend stability and evidence review for several decision questions.
Longer term · 3–12 months
Over 3–12 months, introduce new products, sources and customer segments into the same synthetic corpus. Study theme continuity, classification drift, graph-linkage errors and whether saved analyses remain reproducible after the taxonomy evolves.
What would make the result actionable?
Maintain immutable source records, labeled themes, known contradictions and a versioned taxonomy benchmark. Measure classification and linkage against ground truth, review cited evidence at source level, and validate business impact only with actual customer and operational outcomes.
A Gather simulation returns hypothetical customer reactions. Quantifying revenue, traffic or retention needs actual business inputs and validation against observed behavior.