Dimensionlabs provides a platform that automatically ingests and enriches unstructured customer conversation data—such as chats, calls, tickets, and surveys—and links it to structured business sources like CRM, purchases, and financials via a knowledge‑graph based meaning layer. This enables causal correlation analysis, change detection, and on‑demand intelligence reports that reveal why key metrics move, giving customer experience, product, and analytics teams evidence‑based insights.
Funding
Funding not disclosed
Founders
Product
Problem
Companies collect large volumes of unstructured customer conversation data (chat, calls, tickets, surveys) but lack tools to integrate this information with their business metrics, leading to decisions based on assumptions rather than evidence.
Solution
Dimensionlabs offers a platform that automatically transforms raw customer interaction data into a structured “meaning layer” using knowledge graphs and enrichment pipelines. By linking enriched conversation records to structured sources such as CRM, purchase history, usage logs, and financials, the system enables causal correlation analysis that reveals why key metrics move. Users can explore millions of records instantly, detect emerging trends or anomalies, and generate on‑demand intelligence reports with evidence‑based recommendations. The platform also provides business‑native categorization, allowing organizations to map their own terminology to the underlying data model for consistent analysis across sales, support, product, and analytics teams.
Target Audience
Primary users are customer experience, product, data analytics, and customer success teams that need to turn conversational data into actionable business insights.
Features
- Automated ingestion and enrichment of diverse data sources (live chat, phone calls, voice agents, surveys, tickets, email, CRM, purchases, usage, financials)
- Knowledge graph engine that creates interconnected records and relationships for instant exploration
- Causal correlation engine that connects customer sentiment and topics to changes in business metrics such as churn, revenue, and product usage
- Business‑native categorization that maps natural language to custom business logic and taxonomy
- Change detection module that surfaces new trends, anomalies, and emerging risks automatically
- On‑demand report generation with evidence, visualizations, and actionable recommendations
- API and SDK support for integration with Node.js, Python, and other data pipelines