AI can't govern what it doesn't understand. Humans can't scale without AI that does.
What counts as "good" data depends on business context that no system captures on its own.
Qualytics brings that context into a shared, governed foundation that serves business teams, data teams, and AI systems equally.
Why build for humans and AI?
Business logic, domain expertise, and institutional knowledge about data live across teams in organizational memory: in the heads of SMEs, in undocumented exceptions, in business rules that shift as requirements change. Data catalogs capture part of this through business definitions, metadata, and lineage. But definitions without quality signals are incomplete, and quality signals without business context are hard to trust. Qualytics closes that gap.
Governed Organizational Memory
Organizational memory is where most business knowledge about data lives: the undocumented exceptions, informal rules, and institutional context that only certain people carry. When teams document exceptions, resolve anomalies, or refine business rules in Qualytics, that knowledge becomes governed context that persists, evolves, and serves both humans and AI systems.
- Business logic documented and maintained alongside quality rules
- Exception handling captured with full resolution context
- Business rules versioned and auditable as requirements evolve
- Institutional knowledge preserved and explainable, independent of team turnover
Connected Context Layer
Data catalogs provide essential context: business definitions, metadata, and lineage, but that context is incomplete without quality signals. Qualytics extends that context with real-time quality signals, connecting what data means with whether it can be trusted.
- Integration with existing data catalogs to enrich business definitions with quality signals
- Quality scores and anomaly context available alongside catalog metadata
- Business definitions connected to active rules and validation outcomes
- Lineage context enriched with data quality status at each stage
Shared Ground Truth
Business and data teams share visibility into rules, workflows, and outcomes without needing to translate between business requirements and technical implementation. One view of what's being enforced, what's failing, and what's been resolved.
- Shared dashboards across business and technical stakeholders
- Quality scores at field, container, and datastore levels
- Anomaly trends and resolution history accessible to all teams
- No translation layer between business meaning and technical enforcement
From Human Knowledge to AI Context
Copilots and agents access the same governed context as human users, with no separate infrastructure required. The business logic, quality rules, exceptions, and resolution history that teams build over time are the same context AI systems operate on.
- Copilots and agents access the same rules, exceptions, past resolutions, and definitions as humans
- No separate data quality infrastructure for AI consumption
- Governed context available through MCP for copilots and the Qualytics API for agents
- AI systems inherit the same business logic and governance that human teams define
See how trusted signals reach copilots and agents
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The Results
Why enterprises choose Qualytics for data quality
50+ business users resolving anomalies alongside data teams
4x faster remediation through cross-team collaboration
4 interaction models on one governed foundation (platform UI, AgentQ, MCP, API)
Hear from Our Customers
Don't wait for a major failure before investing in a data quality tool. Qualytics has empowered our business teams, reduced engineering strain, and become an integral part of our fabric.
- Norma Anderson, Director of Data Governance, MAPFRE USA
Qualytics has enabled us to go faster and do much more than we imagined. When you can build faster, you can do so much more for the business.
- Vishal Saxena, CTO, Octus
Alerts don't matter if we're not getting the right people to proactively handle the anomaly and remediate the issues.
- Enterprise Data Governance Lead, Healthcare Benefits Provider
Frequently Asked Questions
Which datastores does Qualytics support?
Qualytics supports any SQL datastore and raw files on object storage. This includes modern platforms like Snowflake, Databricks, BigQuery, and Redshift, relational databases like MySQL, PostgreSQL, and Microsoft SQL Server, and file formats like CSV, XLSX, and JSON on AWS S3, Google Cloud Storage, and Azure Data Lake Storage. Qualytics also integrates with streaming data sources through our API.
How scalable is Qualytics?
Qualytics is built on Apache Spark and deployed via Kubernetes, with vertical and horizontal scalability designed for enterprise volumes. Customers run tens of thousands of rules across billions of rows across SaaS, on-prem, and hybrid environments.
How is organizational context captured and maintained?
Every rule definition, anomaly resolution, documented exception, and business logic update is captured as governed context within Qualytics. This context persists, evolves over time, and is accessible to both human users and AI systems operating on the same foundation.
How do copilots and agents access the same context as human users?
Copilots and agents operate on the same governed foundation as human users. They access the same rules, exceptions, past resolutions, and definitions without requiring separate infrastructure or parallel workflows. The details of how they connect are covered on our Trusted Signals page.
How quickly can we get started?
New data sources can be onboarded in minutes. Automated rule inference delivers broad coverage from day one. Customers like MAPFRE USA gained thousands of inferred rules in a single day, coverage that would have taken months of engineering effort to build manually.
Can business and technical teams share the same workspace?
Yes. Qualytics provides a single governed foundation where business SMEs and data teams share visibility into rules, workflows, and outcomes. Business users don't need technical skills to participate — the platform is built with low-code interfaces and natural language through AgentQ.
Does Qualytics store my raw data?
No. Raw data is pulled into memory for analysis and subsequently destroyed. Anomalies and metadata are written to an enrichment datastore maintained by the customer. Highly regulated industries can deploy Qualytics within their own network where raw data never leaves their environment.
How does Qualytics work with data catalogs?
Qualytics has bidirectional integrations with Atlan, Alation, Collibra, Datahub, and Unity Catalog. Qualytics reads business definitions, metadata, and lineage from your catalog, and pushes quality scores and anomaly context back in. This connects what data means with whether it can be trusted, directly inside the tools your teams already use.
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