The startup offers a web-based business intelligence platform that integrates data collection, transformation, analysis, and machine learning to deliver embedded analytics for enterprise applications. This solution enables organizations to enhance their analytics capabilities, reduce operational costs, and improve decision-making efficiency.
Funding
$21.8M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
SaaS companies often face challenges in providing robust and scalable analytics to their users without incurring significant development costs or diverting resources from their core product. Building an in-house analytics layer can be time-consuming and expensive, requiring specialized expertise in data management, visualization, and security.
Solution
Qrvey offers a full-stack, multi-tenant analytics platform designed to be embedded directly into SaaS applications, enabling companies to deliver comprehensive analytics capabilities to their users with minimal development effort. The platform provides a complete data layer, including a multi-tenant data lake powered by Elasticsearch, a semantic layer for governance, and pre-built connectors and APIs for various data sources. Qrvey's architecture is cloud-native and scales to support large SaaS platforms while ensuring data security and compliance. The platform's embeddable components and scalable data management help SaaS teams save developer hours and reduce cloud infrastructure costs.
Target Audience
Qrvey is designed for SaaS companies looking to enhance their applications with embedded analytics, enterprises seeking to modernize their analytics infrastructure, and developers who need a flexible and scalable analytics platform.
Features
- Full-stack, multi-tenant architecture optimized for SaaS applications
- Embedded data visualizations with white-labeling capabilities
- Self-service reporting and dashboards that users can build within their tenants
- Native API for custom interface design and granular personalization controls
- Unified data pipeline to ingest data into a native data lake, eliminating custom ETL processes
- Multi-tenant data lake powered by Elasticsearch for scalable query performance
- Semantic layer for security controls at the tenant, user, and data levels
- JavaScript widgets for embedding components without iframes
- Workflow automation to connect data via alerts and actions