MEET YOUR AI AGENT

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.

Talk to an Expert Launch Product Tour

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.

ADAPT

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

ASSIST

Building Rich Metadata

Say goodbye to creating metadata manually.

AI-generated column and asset descriptions.
Automatic classification for the data in your fields.

Label

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

TEAMS

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.

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Data Users

Thanks to AI, there’s no need to wait for the data engineering team to adapt, create or fix a monitor. Your monitors can also adapt to changes in seasonal trends.

Data Engineers

Sifflet’s AI helps reduce manual work on tedious, repetitive tasks and gives your data users self-serve tools instead of requiring engineering time.

Data Leaders

AI features that make your data engineers more efficient and your data users better able to take ownership of their data.

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.

Let's dive in

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.

Talk to our Experts

Sifflet’s AI Helps Us Focus on What Moves the Business

What impressed us most about Sifflet’s AI-native approach is how seamlessly it adapts to our data landscape — without needing constant tuning. The system learns patterns across our workflows and flags what matters, not just what’s noisy. It’s made our team faster and more focused, especially as we scale analytics across the business.

Simoh-Mohamed Labdoui

Head of Data

"Enabler of Cross Platform Data Storytelling"

"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. "

Callum O'Connor

Senior Analytics Engineer, The Adaptavist

"Building Harmony Between Data and Business With Sifflet"

"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."

Sophie Gallay

Data & Analytics Director, Etam

" Sifflet empowers our teams through Centralized Data Visibility"

"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"

Ross Gaskell

Software engineering manager, BBC Studios

"Sifflet allows us to find and trust our data"

"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."

Mehdi Labassi

CTO, Carrefour Links

"A core component of our data strategy and transformation"

"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."

Sami Rahman

Director of Data, Hypebeast

Frequently asked questions

How can I better manage stakeholder expectations for the data team?

Setting clear priorities and using a centralized pipeline orchestration visibility tool can help manage expectations across the organization. When stakeholders understand what the team can deliver and when, it builds trust and reduces pressure on your team, leading to a healthier and happier work environment.

What’s new with the Distribution Change monitor and how does it improve anomaly detection?

The upgraded Distribution Change monitor now focuses on tracking volume shifts between specific categories, like product lines or customer segments. This makes anomaly detection more precise by reducing noise and highlighting only the changes that truly matter. It's a smarter way to stay on top of data drift and ensure your metrics reflect reality.

How does data observability complement a data catalog?

While a data catalog helps you find and understand your data, data observability ensures that the data you find is actually reliable. Observability tools like Sifflet monitor the health of your data pipelines in real time, using features like data freshness checks, anomaly detection, and data quality monitoring. Together, they give you both visibility and trust in your data.

What is data lineage and why is it important for data observability?

Data lineage is the process of tracing data as it moves from source to destination, including all transformations along the way. It's a critical component of data observability because it helps teams understand dependencies, troubleshoot issues faster, and maintain data reliability across the entire pipeline.

How does automated data lineage improve data reliability?

Automated data lineage boosts data reliability by giving teams a clear, real-time view of data flows and dependencies. This visibility supports faster troubleshooting, better data governance, and improved SLA compliance, especially when combined with other observability tools in your stack.

What role does Sifflet’s Data Catalog play in data governance?

Sifflet’s Data Catalog supports data governance by surfacing labels and tags, enabling classification of data assets, and linking business glossary terms for standardized definitions. This structured approach helps maintain compliance, manage costs, and ensure sensitive data is handled responsibly.

How can data observability help with SLA compliance and incident management?

Data observability plays a huge role in SLA compliance by enabling real-time alerts and proactive monitoring of data freshness, completeness, and accuracy. When issues occur, observability tools help teams quickly perform root cause analysis and understand downstream impacts, speeding up incident response and reducing downtime. This makes it easier to meet service level agreements and maintain stakeholder trust.

Why is table-level lineage important for data quality monitoring and governance?

Table-level lineage helps you understand how data flows through your systems, which is essential for data quality monitoring and data governance. It supports impact analysis, pipeline debugging, and compliance by showing how changes in upstream tables affect downstream assets.

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