Timeseer.AI: Ensuring Reliable, High-Quality Time-Series Data at Scale

Timeseer.AI is a cutting-edge time-series data reliability and observability platform designed for data teams in enterprise and industrial environments. It empowers organizations to proactively monitor, validate, and optimize time-series data—critical for AI and operational decision-making—by delivering over 30 built-in quality metrics, customizable KPIs, and robust data cleaning and augmentation tools. With seamless YAML-based configuration, support for data SLAs and quality gates, and integration into modern data stacks, Timeseer.AI helps reduce data quality incidents by up to 10x. Trusted by data teams across multiple Fortune 5000 companies, it stands at the forefront of data-centric AI solutions, especially in sensor-driven industries like manufacturing, automotive, and IoT. The platform fosters collaboration, enables scalable data segmentation, and ensures compliance with GDPR through secure, transparent data handling.

Key Features:

• Data Reliability Scoring & Profiling: Automatically assesses and scores data quality across time-series datasets.
• Proactive Data Monitoring & Observability: Real-time scanning and alerting for anomalies and data downtime.
• 30+ Built-In Quality Metrics: Includes variance drift, broken correlations, stale data, missing values, persist anomalies, and IQR anomaly detection.
• Custom Metrics & KPIs: Allows users to define and track domain-specific data quality indicators.
• Data SLAs & Quality Gates: Enables setting and enforcing data quality standards across pipelines.
• YAML-Based Configuration: Simplifies setup and orchestration of resources and processing flows.
• Data Quality Optimization & Cleaning: Features automated inputting of missing values, filtering of artifacts, and alignment of data series.
• Data Augmentation Logic: Supports custom logic to enrich and enhance time-series data.
• Data Connectivity & Uniformization: Maps diverse data sources into a unified time-series model with asset templates and series sets.
• Scalable Data Downtime Segmentation: Identifies and isolates data issues for faster troubleshooting and resolution.
• Collaborative Issue Resolution: Facilitates team coordination on data quality problems to improve data effectiveness.
• Integration with Modern Data Stacks: Compatible with a wide range of data sources and platforms for seamless deployment.
• GDPR-Compliant Data Processing: Ensures privacy and security with robust data protection measures and transparency.
• Trusted Advisory Board: Guided by industry leaders from hyperscalers, sensor tech, and industrial IoT sectors.

Pricing: Timeseer.AI offers a free trial for new users, allowing teams to experience its full suite of data reliability and observability features before committing. This trial model enables enterprises to evaluate the platform’s impact on reducing data downtime and improving data quality.

Conclusion: Timeseer.AI is a powerful, enterprise-grade solution that transforms time-series data operations by making data reliability proactive, measurable, and scalable—proving that better data is the foundation of better AI and business outcomes.