Sifflet offers a data observability platform that continuously monitors pipelines, assets, and downstream metrics to detect anomalies in real time. Using AI‑driven detection and field‑level lineage, it automatically surfaces the root cause of data issues, shows which reports are impacted, and provides actionable fixes through a unified UI and data catalog. The solution integrates with existing data stacks and supports "monitor‑as‑code" templates, helping data teams prevent bad data from reaching the business.
Funding
$18M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
1OCTFounders
Product
Problem
Organizations often discover data quality or pipeline failures only after they have impacted downstream applications, dashboards, or business decisions, leading to lost trust, delayed insights, and costly remediation.
Solution
Sifflet provides a data observability platform that continuously monitors data pipelines, assets, and downstream metrics to detect anomalies in real time. The system combines AI‑driven anomaly detection with field‑level data lineage, enabling users to see exactly where a problem originated and which downstream reports are affected. Automated root‑cause analysis surfaces the “why” behind alerts, while a unified UI and data catalog make troubleshooting intuitive for both technical and business teams. Integrated with hundreds of existing data tools, Sifflet delivers out‑of‑the‑box monitors and a “monitor‑as‑code” framework, allowing teams to start gaining visibility from day one and reduce manual quality checks.
Target Audience
Primary customers are data leaders, data engineers, and data analysts/scientists who need reliable, end‑to‑end visibility into their data pipelines, as well as business users who require trustworthy data for decision‑making.
Features
- AI‑powered anomaly detection that adapts to evolving data patterns and reduces alert fatigue
- Field‑level data lineage visualizations linking upstream sources to downstream assets for precise root‑cause tracing
- Real‑time monitoring of data freshness, completeness, accuracy, and referential integrity across all assets
- “Monitor as code” templates and customizable rules for scalable data quality checks
- Centralized data catalog with ownership tags, metadata sharing, and governance controls
- Seamless integration with existing data stacks via connectors to hundreds of tools and support for dbt manifests
- Collaborative UI that provides both technical debugging details and business‑focused impact views