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

What role does MCP play in improving data quality monitoring?

MCP enables LLMs to access structured context like schema changes, validation rules, and logs, making it easier to detect and explain data quality issues. With tool calls and memory, agents can continuously monitor pipelines and proactively alert teams when data quality deteriorates. This supports better SLA compliance and more reliable data operations.

How does Sifflet support proactive data pipeline monitoring?

Sifflet’s observability platform offers proactive data pipeline monitoring through extensive monitoring tools, real-time alerts, and historical performance insights. These features help your team stay ahead of issues and ensure your data pipelines are always delivering high-quality, reliable data.

How does Sifflet support data pipeline monitoring at Carrefour?

Sifflet enables comprehensive data pipeline monitoring through features like monitoring-as-code and seamless integration with data lineage tracking and governance tools. This gives Carrefour full visibility into their pipeline health and helps ensure SLA compliance.

How can integration and connectivity improve data pipeline monitoring?

When a data catalog integrates seamlessly with your databases, cloud storage, and data lakes, it enhances your ability to monitor data pipelines in real time. This connectivity supports better ingestion latency tracking and helps maintain a reliable observability platform.

What are the key features to look for in a data observability platform?

When evaluating an observability platform, look for strong data lineage tracking, real-time metrics collection, anomaly detection capabilities, and broad integrations across your data stack. Features like field-level lineage, ease of setup, and user-friendly dashboards can make a big difference too. At Sifflet, we believe observability should empower both technical and business users with the context they need to trust and act on data.

Why is integration with my existing tools important for observability?

A good observability platform should fit right into your current stack. That means supporting tools like dbt, Airflow, and your cloud infrastructure. Seamless integration ensures better pipeline orchestration visibility and makes it easier to act on data issues without disrupting your workflows.

What makes Sifflet's architecture unique for secure data pipeline monitoring?

Sifflet uses a cell-based architecture that isolates each customer’s instance and database. This ensures that even under heavy usage or a potential breach, your data pipeline monitoring remains secure, reliable, and unaffected by other customers’ activities.

Why is investing in data observability important for business leaders?

Great question! Investing in data observability helps organizations proactively monitor the health of their data, reduce the risk of bad data incidents, and ensure data quality across pipelines. It also supports better decision-making, improves SLA compliance, and helps maintain trust in analytics. Ultimately, it’s a strategic move that protects your business from costly mistakes and missed opportunities.

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