PALO ALTO, Calif., Dec. 10, 2025 (GLOBE NEWSWIRE) — Striim, a global leader in real-time data intelligence for enterprise AI, today announced Validata, a new product for data validation and reconciliation at scale.
Enterprise AI and agentic systems demand trustworthy data. Yet most organizations still rely on homegrown tools and in-house frameworks for data quality checks: solutions that don’t scale to enterprise data volumes, nor support newer data platforms, or keep up with AI-based data validations. These ad hoc approaches were never designed for mission-critical data systems or the demands of AI initiatives.
Validata meets these challenges, especially in high-performance environments—healthcare, financial services, insurance—where missing or corrupted records drive outages, harmful decisions, or violate regulatory findings.
By comparing data across modern cloud-based and legacy data systems, Validata keeps target datasets in sync with their source counterparts, giving enterprises greater trust, transparency, and auditability in their data, resulting in trusted AI.
It does this through an AI-based approach to intuitively design a “validate – fix – verify” loop: a repeatable, systematic process that can be applied across the data ecosystem. Users can run validation jobs, generate reports, and schedule automations in a simple, conversational style through Validata AI: an intelligent AI-based Agent.
“Every executive we meet says their biggest barrier to scaling AI and agentic applications isn’t ambition, it’s confidence in the underlying data,” said Ali Kutay, CEO and Co-Founder at Striim. “With Validata, our team has taken everything we’ve learned about real-time data pipelines and addressed fundamental governance and data validation challenges, so customers can deploy AI with confidence.”
Closing the Data Trust Gap in Modernization, Analytics, and AI
Large-scale data movement, analytics, and AI initiatives all raise the stakes for silent data drift. This is where mismatched records or subtle transformation errors can derail AI/ML programs, degrade model performance, and erode data trust.
Until now, teams have relied on in-house, unstandardized validation frameworks that are complex to manage, difficult to maintain, and impossible to scale. Where these ad hoc programs fall short, Validata provides a modern alternative that works alongside existing data and AI platforms. With its low-code interface and conversational AI engine, Validata turns previously built complex processes into intuitive, user-friendly workflows.
Validata delivers table-level validation across heterogeneous sources, including Oracle, SQL Server, PostgreSQL, MySQL, Snowflake, Google BigQuery, and Databricks. It includes built-in scheduling, alerting, historical tracking, and reconciliation: all without overloading production systems.
Through a simple, conversational request, Validata AI produces rich discrepancy reports that capture record and field-level differences, from extra source or target records to content mismatches. The reports flag schema drift, giving teams immediate visibility into structural and data-level issues before they impact downstream systems, delivering real-time data for Agentic AI and streaming analytics.
With Validata, enterprises can:
- Boost Operational Reliability: Validata gives teams comprehensive data governance through full database validation, partial validation, and custom SQL-based checks that extend beyond built-in validation methods. Validation reports provide clear, actionable insight into what’s out of sync, where discrepancies occur, and when, so teams can resolve issues quickly and maintain reliable, high-quality data across systems.
- Strengthen Governance Controls: Enterprises, especially those in regulated industries, gain defensible, timestamped evidence of data completeness. Each run produces durable, repeatable evidence that stands up to internal audit, external regulators, and modern governance standards for traceability and historical proof of correctness.
- Enhance AI/ML Performance: Custom SQL validations for feature stores, combined with fast, scalable consistency checks, help ensure AI and ML pipelines stay accurate and trustworthy. Validata catches subtle feature drift early, safeguards model performance with incremental drift detection, and improves overall trust in AI and agentic applications.
“Comparing fast-changing objects across data estates is challenging! In Validata, we have added high-performance vector-based math to efficiently identify data mismatches. We find that data and platform modernization initiatives are far outpacing the controls to keep data accurate and trustworthy,” said Alok Pareek, Co-founder and Executive Vice President of Engineering and Products at Striim. “Validata gives data and AI leaders a single, intuitive, AI-based solution to prove critical tables are in sync across their operational systems, data lakes, and agents in real-time — and to repair what’s broken — to ensure trusted data delivery.”
Availability
Validata is now generally available. Contact Striim for more details.
About Striim
Striim pioneers real-time data intelligence for AI by unifying data across clouds, applications, and databases via a fully managed, SaaS-based platform. Striim’s platform, optimized for modern cloud data lakehouses and data warehouses, transforms relational and unstructured data into AI-ready insights instantly with advanced analytics and ML frameworks, enabling swift business action. Striim leverages its expertise in real-time data integration, streaming analytics, and database replication—including industry-leading Oracle, PostgreSQL, and MongoDB CDC technology—to achieve sub-second latency in processing over 100 billion daily events for ML analytics and proactive decision-making. To learn more, visit http://www.striim.com.
Media Contact:
Dianna Spring
Vice President of Marketing, Striim
Phone: (650) 241-0680 ext. 354
Email: press@striim.com
