DQLabs PRIZM

DQLabs, Inc. is a data quality and observability software company led by founder and CEO Raj Joseph, with an office in Pasadena, California and an AI Center of Excellence in Hyderabad, India opened in 2025. It helps enterprises establish data trust through continuous visibility into data health, quality and business context, and reports users in more than 25 countries and 15 industries.

PRIZM by DQLabs combines three capabilities in one AI-native platform. Data observability provides agentless monitoring of freshness, schema, volume, cost and performance with ML anomaly detection, lineage-aware alerts and root-cause guidance. Data quality offers no-code rule authoring with AI recommendations, quality scores at asset, domain and contract level, SLAs and executive scorecards. Enterprise context adds automated discovery, classification and tagging, a business glossary and lineage. Logos on the site include Wipro, Deloitte, Tokio Marine and DXC.

• Agentless observability: Monitors freshness, schema, volume, cost and performance across sources.
• ML anomaly detection: Learns normal patterns and clusters related alerts with root-cause guidance.
• No-code data quality rules: AI recommends rules that business users can author without code.
• Quality scoring and SLAs: Scores at asset, domain and contract level with executive trust scorecards.
• Enterprise context: Automated discovery, classification, tagging, glossary and lineage.
• Workflow integrations: Alerts and tickets through Slack, Teams, Jira, ServiceNow and BigPanda.

An insurer in Vietnam can score policy and claims data quality and report trust metrics to management. A manufacturer running SAP HANA can monitor master data and pipeline freshness feeding its BI dashboards. A bank that already uses a data catalog can add quality scores and alerts to the assets it governs.

Data quality, observability and cataloguing context come in one package instead of three tools. Pricing is a single quote with no per-seat, per-row or per-asset fees, and the base package includes unlimited assets, users and quality rules for one source type. AI-recommended rules and alert clustering reduce manual rule writing and alert noise. More than 150 integrations, including Alation, Atlan and Collibra, let it fit alongside an existing catalog.

Gartner and other analysts now treat augmented data quality and data observability as converging categories, driven by AI projects that need trustworthy data. Enterprises in Southeast Asia that already run Snowflake, Databricks, SAP or Oracle are looking for tools that score and monitor data quality without large rule-writing projects.

DQLabs, Inc. lists an office at 21 Miller Alley, Pasadena, California, and opened an AI Center of Excellence in Hyderabad in May 2025. It was named a Visionary in the Gartner Magic Quadrant for Augmented Data Quality Solutions in 2025 and 2026, and a Leader in the Everest Group Data Observability PEAK Matrix 2025.