Show Your Data Stack Who’s Boss.
Sifflet’s platform is powered by AI to tackle the sheer volume and complexity of the modern data stack.
Augmented Assistance
Let AI help you speed things up. Sifflet is designed to reduce tedious tasks - like generating metadata descriptions or correcting SQL - with the click of a button.
No Coding Skills Required
Not an engineer? Not a problem. Describe what kind of monitors you’d like and Sifflet takes care of the rest.
Smart Alerts
Sifflet uses AI to optimize monitoring coverage and avoid alert fatigue by sending you the right alerts at the right time.
Dynamic Monitors
Monitors that get smarter as they go.
- AI that creates monitors based on your prompts
- Monitoring that learns from historical and on-going data
- Detects anomalies in real time, adapts to trends, and sends meaningful alerts
Building Rich Metadata
Say goodbye to creating metadata manually.
- AI-generated column and asset descriptions.
- Automatic classification for the data in your fields.
Easy Monitor Creation
Create monitors, monitor names and descriptions effortlessly.
- Monitor configuration, title and description suggestions.
- SQL correction.
- Regex suggestions
- Monitor of Monitoring Accuracy (MoMA) suggestions
Tame Your Stack. Scale Your Smarts.
Built for Everyone
Sifflet’s AI-powered features help you show your stack who’s boss. Augment your team’s capabilities and make data observability everyone’s business.
Looking for more?
7 Reasons for Data Observability within AI-Powered Organizations
AI's rise makes reliable data essential. Discover seven reasons why data observability is key to driving AI success in 2025.
Sifflet AI Assistant - Automating your data observability at scale
Learn how it streamlines insights, enhances efficiency, and empowers your data teams to focus on what truly matters.
AI and Data Management
Making your way to well-categorized data can be very time consuming, especially when dealing with large volumes of data or complex datasets. Discover Classification tags, designed to help you categorize your data catalog assets according to key data characteristics.
Scale isn't so scary.
Sifflet’s AI-powered features help you wrangle your stack, even as it scales. Augment your team's capabilities today to make
data observability everyone’s business.
"Sifflet has been a game-changer for our organization, providing full visibility of data lineage across multiple repositories and platforms. The ability to connect to various data sources ensures observability regardless of the platform, and the clean, intuitive UI makes setup effortless, even when uploading dbt manifest files via the API. Their documentation is concise and easy to follow, and their team's communication has been outstanding—quickly addressing issues, keeping us informed, and incorporating feedback. "
"Sifflet serves as our key enabler in fostering a harmonious relationship with business teams. By proactively identifying and addressing potential issues before they escalate, we can shift the focus of our interactions from troubleshooting to driving meaningful value. This approach not only enhances collaboration but also ensures that our efforts are aligned with creating impactful outcomes for the organization."
"Having the visibility of our DBT transformations combined with full end-to-end data lineage in one central place in Sifflet is so powerful for giving our data teams confidence in our data, helping to diagnose data quality issues and unlocking an effective data mesh for us at BBC Studios"
"Sifflet has transformed our data observability management at Carrefour Links. Thanks to Sifflet's proactive monitoring, we can identify and resolve potential issues before they impact our operations. Additionally, the simplified access to data enables our teams to collaborate more effectively."
"Using Sifflet has helped us move much more quickly because we no longer experience the pain of constantly going back and fixing issues two, three, or four times."
Frequently asked questions
Data-quality-as-code (DQaC) allows you to programmatically define and enforce data quality rules using code. This ensures consistency, scalability, and better integration with CI/CD pipelines. Read more here to find out how to leverage it within Sifflet
Yes, Sifflet leverages AI to enhance data observability with features like anomaly detection and predictive insights. This ensures your data systems remain resilient and can support advanced analytics and AI-driven initiatives. Have a look at how Sifflet is leveraging AI for better data observability here
AI enhances data observability with advanced anomaly detection, predictive analytics, and automated root cause analysis. This helps teams identify and resolve issues faster while reducing manual effort. Have a look at how Sifflet is leveraging AI for better data observability here
Data observability ensures data governance policies are adhered to by tracking data usage, quality, and lineage. It provides the transparency needed for accountability and compliance. Read more here.
Yes! While smaller organizations may have fewer data pipelines, ensuring data quality and reliability is equally important for making accurate decisions and scaling effectively. What really matters is the data stack maturity and volume of data. Take our test here to find out if you really need data observability.
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