Take control of data quality before itcontrols your business
Qualytics infers 95% of your data quality rules from observed data behavior and continuously adapts as that behavior changes. Your teams guide governance with business context, delivering broad coverage without the manual effort that limits most programs.
Why augmented data quality?
Qualytics is augmented, not autonomous: AI infers and maintains the majority of your rules while your teams guide governance with business context. Every capability is built for the people who own data quality, not just the people who write SQL. Low-code interfaces and natural language make the platform accessible across the team.
Profile and Understand Your Data
Qualytics connects to your data sources and profiles how your data behaves, unifying structural and behavioral metadata into field profiles that establish a baseline from day one. Segmented profiling slices results by tenant, region, or any dimension, exposing issues that only appear in a subset of the data.
Automated structural profiling across every field
Behavioral baselines generated from observed patterns over time
Structural and behavioral metadata unified into field profiles with historical trend tracking
Group-by profiling for multi-tenant and segmented views
Generate and Maintain Coverage
Automated rule inference delivers broad coverage from day one, while business SMEs extend it through guided low-code authoring for complex logic like entity resolution or cross-system reconciliation. AgentQ adds a conversational interface, letting teams author rules, investigate anomalies, and manage governance through natural language.
Auto-suggested rules from profiling metadata and observed behavior
50+ prebuilt rule types from simple validations to complex reconciliations
Low-code and no-code rule authoring for business and data teams
AgentQ conversational interface for natural language governance
Reusable templates with centrally governed configuration, applied across datasets
Dry run validation to test rules against production data before promotion
Continuously Monitor Quality
Qualytics monitors structural and behavioral signals as data moves through pipelines and schemas, detecting anomalies closer to the source before bad data spreads downstream. As teams review and resolve anomalies, their feedback refines detection over time.
Anomaly lifecycle management with status, ownership, severity, and resolution tracking
Time-series monitoring for metric and volume trends with deviation alerting
Human-in-the-loop feedback that refines rules and detection over time
AgentQ for conversational anomaly investigation and triage
UI and API-enabled monitoring for collaborative workflows
Act on Quality Signals
Every anomaly includes context that pinpoints what changed and which records are affected. No-code flows route incidents to the right owners and trigger remediation through Jira, ServiceNow, Slack, or Teams. Flows can also halt data propagation by pausing jobs or gating promotion steps. These same quality signals power the controls that copilots and agents rely on.
Clear ownership and accountability for every anomaly
Impact context with anomaly weight, failed rules, and affected source records
Automated remediation through flows and integrations
Enrichment datastore that maintains a governed audit trail from anomaly to resolution
See how this foundation serves humans and AI equally.
The Results
Why enterprises use Qualytics for data quality
20K+
rules in production per average customer
95%+
of rules automated through AI inference
20+
business SMEs actively engaged per customer
Hear from Our Customers
Frequently Asked Questions
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.
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.
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.
AI infers ~95% of your data quality rules from the actual behavior of your data. The platform automatically profiles data, generates and maintains rules, detects anomalies, and initiates remediation workflows. As teams review anomalies and provide feedback, the system learns and improves over time. The remaining rules are authored by business and data teams through low-code interfaces and AgentQ.
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.
No. Qualytics is built with a purpose-built UX designed for business users and governance SMEs. Complex rule types like entity resolution or cross-system reconciliation take a few clicks to configure. AgentQ adds a conversational interface for teams who prefer natural language. Engineers can still write full-code rules for specialized edge cases.
AgentQ is a conversational AI interface within Qualytics. Teams can explore metadata, investigate anomalies, author rules, and refine governance through natural language without sacrificing auditability. AgentQ is BYOK (bring your own key) and works with your preferred LLM behind the scenes, so it fits into your existing AI infrastructure.
Every time a team reviews an anomaly, resolves an exception, or overrides a rule, the system learns from that decision. These actions refine inferred rules and improve detection accuracy continuously. The more your teams engage with the platform, the smarter the coverage becomes.
Ready to take control of your data quality?
