Rules-based Anomaly Detection

Find the Unknown of Unknowns

Surface and troubleshoot anomalies without burning precious time

From Alerts to Answers

In modern hardware development, unforeseen interactions and unpredictable scenarios pose major challenges. Anomaly detection often triggers false positives, with traditional dashboards lacking the insights to pinpoint root causes—much like a check engine light provides little guidance. This lack of precision forces teams to spend hours sifting through data. Sift combines real-time observability with automated anomaly detection enabling precise root cause identification.

Testing like you fly

Equip your engineers with the same observability toolkit throughout development and launch to avoid costly blind spots and ensure optimal performance under real-world conditions.

Accelerate anomaly detection

Instantly detect unusual patterns and outliers as they occur. Manage and communicate anomalies through continuously updated rules, ensuring comprehensive and transparent analysis.

Visualize without writing code

Visualize real-time data across multiple sources with pre-configured plots—no coding required. This streamlines anomaly detection without the need for Python scripts or SQL queries.

Capture institutional knowledge

Custom rules store team expertise for future use, while in-line annotations offer valuable context and insights directly within the interface, enhancing collaboration and continuity.

Analyze and act on vast amounts of data

Sift enabled the Reliable team to codify review criteria directly into the platform, facilitating automatic, real-time assessment. This feature has the potential to expedite anomaly detection and will allow engineers to quickly dive into root cause investigations.

$100k+ Cost savings over time

Astrolab’s engineers are employing Sift’s Rules feature to confidently discover issues and avoid the risk of missing anomalies. Sift enables Parallel to understand their vehicle performance over time, with detailed and long-term analysis of each vehicle in just a few minutes.

Sift Difference

Organized exploratory analysis

Configure rules, combine diverse datasets, and capture annotations to correlate logs with time-series data, enabling efficient and thorough exploratory analysis.

Advanced data analysis

Across multiple channels, Sift can compute rolling averages and other aggregates, smoothing data, filtering noise, and enhancing trend analysis for more effective monitoring and issue detection.

Data quality assurance

Sift ensures the accuracy and reliability of the data being analyzed with validation checks and error detection algorithms.

Ready to modernize your  telemetry stack?

Thank you! Your submission has been received!

Oops! Something went wrong while submitting the form.