Most enterprises continue to struggle with QA processes that are expensive, manual, and slow to scale — and testing effort grows with every new release.
Expensive and resource intensive
Dependent on manual test case creation
Slow during regression cycles
Unable to scale across reports and dashboards
Reactive rather than proactive
Susceptible to quality leakage
Governed through fragmented tools
The cost compounds fast: QA consumes a growing share of project budgets, releases slip through long validation cycles, and defects that reach production erode stakeholder confidence in enterprise data. Scaling quality ends up meaning adding headcount, not capability—which is exactly the model QAgentic replaces.
QAgentic uses specialized AI agents working together to automate the complete QA lifecycle.
Instead of relying on static scripts and manual testing, QAgentic continuously learns, adapts, validates, and governs enterprise quality assurance across your data and analytics estate.
The Agentic QA Factory: nine specialized agents automate the complete QA lifecycle, from requirement analysis to governance, learning and improving with every release.
Analyzes BRDs, business rules, mappings, and user stories to build complete requirement context.
Automatically generates comprehensive test scenarios and validation rules from requirements.
Creates realistic test datasets to improve coverage across edge cases and business rules.
Executes headless, intelligent regression testing at scale across every release.
Monitors data integrity, anomalies, completeness, and business rule violations continuously.
Validates BI dashboards, KPIs, and reports across environments after every change.
Identifies root causes and clusters defect patterns to speed up resolution.
Continuously improves testing outcomes based on historical results.
Provides command-center visibility, approvals, auditability, and compliance.
Automatically build test cases from:
Eliminate manual documentation bottlenecks.
Execute thousands of validation scenarios
autonomously and gain:
Accelerate testing cycles while maintaining quality and reliability.
Validate reports and KPIs across:
Keep KPIs, calculations, filters, and business logic accurate after every change.
Continuously monitor:
Detect issues before business users experience them.
Accelerate data and analytics migrations by validating:
Reduce migration risk while ensuring complete validation across environments.
Maintain enterprise control while using AI:
Ensure transparency, accountability, and governance throughout the testing lifecycle.
Lower QA cost through autonomous test generation and execution
Test coverage across data platforms, dashboards, and applications
Faster regression cycles with intelligent automation
Data quality issues reaching production
Release confidence backed by proactive, continuous validation
Team productivity as agents absorb repetitive testing work
QAgentic adapts to the validation demands of each industry, from merchandising analytics in retail to regulatory reporting in financial services.
QAgentic plugs into your existing technology ecosystem and deploys without disrupting current workflows.
QAgentic replaces manual, script-heavy QA with adaptive agents that generate, execute, and govern testing autonomously. Here is how the two approaches compare.
Fragmented GovernanceStatic scriptsLimited coverageHuman-driven executionReactive defect detectionManual test designHigh cost
AI-generated test designAdaptive agentsComprehensive coverageAutonomous executionProactive validationUnified control centerOptimized cost model
Fully managed deployment hosted within the Databricks platform.
A dedicated cloud environment managed for your organization.
For highly regulated enterprises with strict data privacy requirements.
Explore how media and entertainment organizations are using data and AI to improve ad revenue performance, strengthen audience intelligence, and build more resilient operations
Case Study
QAgentic automates enterprise quality assurance using specialized AI agents. It generates test cases from requirements, runs regression testing at scale, validates dashboards and data quality, analyzes defects, and governs the entire QA process, helping teams release faster with higher coverage and lower cost.
QAgentic is built for QA leaders, data and analytics teams, engineering managers, and CIOs responsible for release quality across data platforms, BI dashboards, enterprise applications, and APIs. It fits organizations that need to scale testing without scaling headcount.
QAgentic deploys in a phased approach based on your data sources, tools, and integration needs. Most teams start with a pilot on a single platform or migration program, see measurable results within weeks, and then scale across the enterprise.
Traditional automation relies on static scripts that break when applications change. QAgentic uses adaptive AI agents that generate test cases from requirements, learn from historical results, and improve continuously, covering data, dashboards, and applications that scripts alone cannot validate.
Yes. QAgentic runs within your environment, on Databricks, in a private cloud, or on-premises, with role-based access controls, audit trails, and compliance with enterprise security standards. Sensitive data never leaves your governance boundary.
Yes. QAgentic connects to Jira, ServiceNow, Databricks, Power BI, Incorta, Lightup, data warehouses, and CI/CD platforms through prebuilt connectors, plus custom MCP and API endpoints for anything else in your stack.
QAgentic includes human-in-the-loop governance: approval workflows, escalation controls, quality scorecards, and release readiness reviews. Teams set the guardrails, and every agent action stays auditable through the unified control center.
