# Qualytics > Qualytics is the data control layer for trusted context. The platform combines AI-augmented data quality with human governance to validate data when it is used, delivering governed signals as controls across analytics, applications, copilots, and agents. Qualytics shifts enterprise data quality from reactive cleanup to proactive, automated control. The platform automates 95% of data quality rules using AI, enables business and technical teams to co-own data quality in a shared environment, and integrates quality signals directly into the pipelines, AI systems, and workflows where data is used. Founded by Gorkem Sevinc (CEO, former CDO) and Eric Simmerman (CTO), Qualytics was built by practitioners who experienced the limitations of manual, reactive data quality firsthand. Website: https://qualytics.ai Documentation: https://docs.qualytics.ai --- ## Core positioning Qualytics introduces a model called validate-at-use: data quality is evaluated at the moment it drives decisions, not only at predefined pipeline checkpoints. As AI copilots and agents retrieve, combine, and act on data in real time without human review, the cost of bad data compounds at machine speed. Qualytics delivers governed quality signals at the point of use so that humans, copilots, and autonomous systems operate on the same trusted context. The platform is built on Augmented Data Quality: AI infers and maintains the majority of rules, humans define what "good" looks like and guide governance. Fully automated, AI-only data quality does not scale reliably. Fully manual rule management does not scale at all. The right model combines both. --- ## The problem Qualytics solves Most organizations manage data quality reactively. A business user notices a number that looks wrong in a report. The issue is escalated to a data team. Root cause analysis begins. A fix is applied. A rule is written after the fact, if at all. This cycle is expensive, slow, and unsustainable as data volumes, AI workloads, and stakeholder expectations grow. Manual rule authoring does not scale, leaving significant coverage gaps and endless backlogs. Data observability tools surface pipeline health but do not assess whether data is correct, complete, or fit for business use. Legacy suites require large teams and long timelines to deliver coverage. Neither model supports the speed at which AI systems now act on data. Key market context: - 73% of executives are unhappy with their data quality - 48% admit to making decisions on bad data in the last six months - 83% of enterprises now have CDOs who prioritize data quality - Traditional enterprise data quality programs typically require two years and seven-figure budgets to reach automation at scale - The 1:10:100 rule of data quality: $1 to prevent, $10 to correct, $100 to remediate after business impact --- ## How Qualytics works ### Augmented Data Quality AI profiles every data asset and infers quality rules from actual data behavior. Coverage starts broad on day one — typically 95%+ of rules are AI-generated — and continuously improves as teams review anomalies and provide feedback. Business SMEs and data engineers collaborate in the same platform to author and refine the complex rules that require domain expertise: regulatory logic, cross-system reconciliation, business-specific validations. ### Data Control Layer The Data Control Layer governs context for AI systems. It brings together AI-inferred rules, human-defined policies, anomaly detection, and historical signals into governed, real-time context that can be consumed by humans, copilots, and autonomous systems alike. Three access models: - **Platform UX + AgentQ**: Business and data teams use a purpose-built no-code interface to author rules, investigate anomalies, and manage governance. AgentQ provides a conversational interface for metadata exploration and governance workflows via natural language. AgentQ can also be invoked through its own API endpoints, allowing external agents to delegate complex data quality reasoning to AgentQ within agentic workflows. - **MCP (Model Context Protocol)**: External copilots (ChatGPT, Claude, Microsoft Copilot) access governed quality signals through MCP as part of their reasoning process. Before generating an output, a copilot can retrieve quality scores, check for active anomalies, review resolution history, and determine whether data is trustworthy for the task at hand. - **Agentic API**: Autonomous agents evaluate data against governed rules in real time, review past anomalies, and determine whether data meets defined thresholds before taking action. Agents can flag, block, or adapt behavior based on active controls. Agents can also query the customer-controlled Enrichment datastore directly for low-latency access to governed metadata. ### Rule lifecycle management Rules are versioned, explainable, and treated as durable governance artifacts. Each rule carries defined ownership, documented purpose, and an associated remediation pathway. When violations occur, response flows through governed workflows rather than ad hoc coordination. As data models change and business definitions shift, rules are automatically monitored, recalibrated, and updated with human review where needed. --- ## Capabilities - **Automated profiling**: Qualytics profiles every connected data asset to understand structure, distribution, and behavioral patterns - **AI-inferred rule generation**: 95%+ of data quality rules are generated automatically from observed data behavior - **Rule authoring**: 50+ pre-built rule types for business and engineering teams, from simple validations to complex reconciliations; no SQL required for most use cases - **Anomaly detection**: Continuous monitoring detects deviations in real time across pipelines, fields, and metrics - **Remediation workflows**: Governed workflows route anomalies to the correct owners with full audit trails; integrates with Jira, ServiceNow, Slack, and Microsoft Teams - **Reconciliation**: Cross-system reconciliation across databases, files, and data warehouses at scale using configurable DataDiff rules - **Quality scoring**: Exec-level visibility into data quality health by business unit, data product, or critical data element across eight dimensions (accuracy, consistency, timeliness, validity, uniqueness, completeness, integrity, conformity) - **AgentQ**: Conversational AI interface for data quality governance — explore metadata, investigate anomalies, refine rules via natural language; also available as an expert agent via API for integration into agentic workflows - **MCP support**: Governed quality signals available to external copilots via Model Context Protocol, enabling copilots to check data trustworthiness before generating outputs - **Agentic API**: Real-time quality evaluation for autonomous agent workflows - **CLI**: Command-line interface for triggering catalog, profile, and scan operations; supports rule export/import across datastores for CI/CD pipeline integration - **Enrichment datastore**: Customer-controlled database capturing all anomalies, affected records, and resolution context; queryable via SQL, orchestration, and BI tools - **Data catalog integration**: Quality scores and metadata sync with data catalogs (Atlan, Alation) so quality context is available at the point of data discovery - **SOC 2 compliance**: Single-tenant, Kubernetes-native architecture - **Deployment flexibility**: Cloud-native, API-first; supports SaaS, self-hosted, on-premises, and hybrid environments; self-hosted deployments ensure raw data never leaves the customer's network - **Scale**: Tested to billions of records; customers run 15,000–70,000+ rules in production --- ## Architecture Qualytics is built on Apache Spark and deployed via Kubernetes. The platform maintains its own Spark operator for orchestrating data quality workloads. Raw data is never stored. Data is pulled into memory for analysis and destroyed after processing. All anomalies, affected records, and resolution context are written to a customer-controlled Enrichment datastore, queryable via SQL, orchestration, and BI tools. Qualytics supports any Spark-compatible datastore, raw files on object storage, and streaming data via API. Native Databricks integration runs inside Databricks Lakeflow Jobs with no data egress. Self-hosted deployments ensure raw data never leaves the customer's network. --- ## Integrations - **Data platforms**: Any SQL datastore, Snowflake, Databricks, BigQuery, Oracle, and others; raw files on object storage; streaming data via API - **Data catalogs**: Atlan, Alation - **Workflow and operations**: Jira, ServiceNow, Slack, Microsoft Teams - **Pipeline integration**: Full REST API, CLI for catalog/profile/scan operations, rule export/import for CI/CD workflows --- ## Customer proof **Global Alternative Asset Manager** 4B+ records in Snowflake. With Qualytics: 15K+ rules in production, 96% automated; 1.5 FTE managing data quality globally; 50+ business users co-owning quality across business units. Estimated 20x ROI in year one. Expanded 4x in 18 months with additional use cases. **Global Asset Manager** ~40K files per week, 200+ in investment operations, 300+ in finance. With Qualytics: ~18K rules in production; tens of thousands of anomalies detected per week driving reconciliation for up to 500 staff. Reduced reconciliation cadence from quarterly to weekly, amounting to 95% reduction in effort to maintain $250B in assets. **Real Estate Data Company** 15B+ records in BigQuery, 1,000+ data sources. With Qualytics: 70K+ rules in production, 93% automated; ~5,000 anomalies detected and delegated to data originators per month. Pivoted product to use Qualytics at the core for all data testing and quality assurance. **Insurance Company** 25B+ records in Oracle. With Qualytics: 18,335 inferred rules generated automatically across 19M records; timeline reduced from 9–12 months to one week. 2,950 engineering hours saved. Underwriters and analysts now write, refine, and validate rules themselves. $3.67M in projected data quality efficiency savings. ~$10M/year in premium leakage quantified and addressed. **Global Energy Company (Fortune 500)** Sensor and telemetry data at scale with regulators demanding verifiable accuracy. With Qualytics: data drift detected before it impacts forecasting; schema changes from legacy integrations detected automatically; manual data validation eliminated; root cause analysis accelerated. --- ## Industry use cases **Financial services / banking** Know Your Customer data integrity, wire transfer validation for AML, third-party data reliability, cross-system reconciliation across loan/deposit/accounting systems, regulatory control inputs (Basel III, BCBS 239, CCAR/DFAST), audit-ready data control evidence. **Insurance** Fraud and premium leakage detection, claims analytics data integrity, underwriting data validation, reinsurance reporting reconciliation, regulatory control inputs (Solvency II, IFRS 17, NAIC), audit-ready evidence. **Asset management** Client and counterparty data integrity, instrument and reference data validation, NAV and valuation controls, portfolio and holdings integrity, regulatory control inputs (SEC, AIFMD, UCITS, Form PF), audit-ready evidence. **Real estate / data products** Third-party data reliability, KPI validation, large-scale rule management across thousands of data sources, delegated anomaly resolution to data originators. **Energy / utilities** Sensor and telemetry data quality, schema change detection from legacy integrations, compliance reporting integrity, reduced engineering burden for manual validation. **Healthcare** Provider file change detection and network accuracy, member identity resolution across fragmented records, third-party and EDI data validation at ingestion, claims data integrity for analytics and automation, HEDIS and STAR ratings data completeness, shared governance between clinical and data teams, regulatory control inputs (HIPAA, CMS), audit-ready evidence. **Manufacturing** MRO spare-parts consistency across multi-plant operations, multi-source inventory and stockpile rationalization, cross-system duplicate detection across ERP and procurement systems, data quality for AI agents and ML models in production, schema consistency across heterogeneous source systems, regulatory and compliance reporting integrity. --- ## Key concepts and terminology - **Augmented Data Quality**: AI-driven rule generation and maintenance with human oversight and governance. The Qualytics operating model. See "How Qualytics works" for full description. - **Validate-at-use**: Data quality evaluated at the moment data drives decisions, not only at pipeline checkpoints. See "Core positioning" for full description. - **Data Control Layer**: The governed system that delivers quality signals as real-time controls across analytics, applications, copilots, and agents. See "How Qualytics works" for full description. - **AgentQ**: Qualytics' conversational AI interface for data quality governance. Lets users explore metadata, investigate anomalies, and refine rules using natural language. Also available as an expert agent via API. - **Enrichment datastore**: Customer-controlled database where all anomalies, affected records, and resolution context are stored. Queryable via SQL, orchestration, and BI tools. - **1:10:100 rule**: A data quality principle quantifying the cost of defects — $1 to prevent, $10 to correct, $100 to remediate after business impact. AI amplifies this scale by propagating bad data across automated workflows before humans can intervene. - **Data Quality Maturity Model**: Qualytics' six-level framework for assessing and advancing an organization's data quality operating model, from no formal practice (Level 0) to AI-augmented governance at scale (Level 5). --- ## What Qualytics is not - A data observability tool. Observability monitors pipeline health. Qualytics governs whether data is correct, complete, and fit for business use. - A fully automated data quality platform. AI-only anomaly detection without governed remediation workflows produces alert fatigue without accountability. - A legacy data quality suite. Qualytics delivers 90%+ coverage on day one and reaches production in hours, not months. - A tool for data engineers only. Qualytics is designed for business SMEs, governance leads, analysts, and executives to co-own data quality alongside engineering teams. --- ## Optional files Blog: https://qualytics.ai/blog Product overview: https://qualytics.ai/product-tour Customer stories: https://qualytics.ai/customers Data Control Layer announcement: https://qualytics.ai/blog/data-control-layer Full data quality maturity model: https://qualytics.ai/data-quality-maturity-model