Dimension Labs connects your customer feedback data with product and revenue metrics to prove which issues drive churn and which fixes unlock growth.
Product feedback is everywhere: tickets, calls, surveys, reviews, chats. But without causal structure, you have anecdotes, not evidence.
Unified Data & Enrichment
Your product feedback, support tickets, NPS verbatims, and app store reviews all live in different systems. Dimension Labs connects them and builds the Meaning Layer: every interaction enriched with Dimensions like topic, root cause, feature mention, sentiment, and churn risk.
Causal Intelligence Engine
Ask any product question in plain English. The agent queries across enriched feedback data and structured product metrics, identifies causal patterns, controls for confounders like segment and tenure, and returns findings backed by statistical evidence.
On-Demand Intelligence Reports
Define a reporting objective. The agent builds the plan, executes it against your full dataset, and produces a polished deliverable: executive summary, causal findings, visualizations, and recommendations. Repeatable monthly. Exportable as HTML, PDF, or slides.
System-Level Dimensions
Custom Dimensions per Business Objective
Multi-Dimension Orchestration
Output Validation - "hallucination" or drift check
Internal Vector Embeddings & Semantic Similarity
Dynamic Clustering & Theming
Multi-Dimensional Consolidation
Export ability to data warehouse
Generate causal reports, visualizations, and executive intelligence: on demand or scheduled
Plug into existing B.I tool of choice (Tableau, PowerBI, Hex)
Export via API to any warehouse (Snowflake, Databricks, BigQuery)
I can actually get the specificity that our product managers need — not just 'connectivity issues,' but which type, which product generation, all broken down. That level of detail is what makes this valuable
