The Control Plane for %%Data and AI%%

We catch data issues before they reach the business, show exactly why they happened, and how to fix them. So the data behind every decision is one you can trust.

The premier %%virtual summit%% on data reliability, observability, and the future of trustworthy AI.

What Our Customers Say

See Sifflet in action!

Curious about how Sifflet can transform the way your team works with data?

Join our 30-min biweekly demo to see how data leaders, engineers, and platform teams use Sifflet to detect, resolve, and prevent issues—before they impact the business.

Dynex Capital
Euronext
Dailymotion
Saint-Gobain
ShopBack
Servier
Penguin Random House
Adaptavist
Mollie
Hypebeast
Deuna
BBC Studios
Carrefour
Etam
Auchan

Your pipelines are monitored. Your alerts are firing. %%So why does bad data keep reaching the business?%%

Detection is table stakes. What matters is what happens next: why it broke, what it affects, and how to fix it.

Know What Actually Matters

Not all alerts are equal. Sifflet enriches every issue with lineage, downstream usage, and ownership — so you stop treating schema drift and a broken exec dashboard the same way. Focus on what has real business consequences.

Stop Playing Detective

When something breaks, the context you need is already there: upstream lineage, recent schema changes, historical behavior. The root cause you'd spend hours hunting, surfaced in minutes.

One Control Layer Across Your Full Stack

Incidents don't respect tool boundaries. Sifflet covers the whole chain — warehouses, orchestrators, BI — so nothing falls through the gap between Snowflake and the dashboard your CFO opens on Monday morning.

TRACEABLE

Improve productivity and collaboration between engineers and data consumers

For everyone, working with and finding data becomes intuitive with a simple and automated UI, data discovery is simplified with a data catalog, and it is easy to connect with coding workflows.

Sifflet dashboard features overview
Sifflet dashboard features overview
Data Lineage

Troubleshoot

When data breaks, trace it. Map any issue upstream, downstream, and across layers — field by field. Know exactly where a number came from, what it affects, and how to fix it. A lineage gap is a trust gap. Sifflet closes it.

Data quality monitoring

Monitor

Monitor everything. Miss nothing. Out-of-the-box and custom monitoring across every asset — including the ones you didn't know to watch. AI reduces noise as your stack grows, so your team stays focused on signals that matter, not the ones that don't.

Data reliability is a team sport

The right view for everyone in the buying center: the people who build it, the people who govern it, and the people who depend on it.

Data Leaders

Drive innovation and enable AI. With Sifflet, you can transform your data strategy, governance, and team productivity while ensuring efficient and scalable data infrastructure.

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Data Engineers

Boost your productivity. Sifflet gives you end-to-end visibility into your architecture, assets, and pipelines. Advanced monitoring ensures you get the right alerts and lineage helps you get to resolution faster.

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Data Users

No more data discrepancies. Sifflet ensures the highest levels of data quality. Your teams can make the best possible decisions for your company, unlocking new levels of performance that help you compete in the age of AI.

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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
Dynex Capital
Euronext
Dailymotion
Saint-Gobain
ShopBack
Servier
Penguin Random House
Adaptavist
Mollie
Hypebeast
Deuna
BBC Studios
Carrefour
Etam
Auchan
Still have a question in mind ?
Contact Us

Frequently asked questions

How does Sifflet enhance metadata catalogs with data observability?
Sifflet enriches your metadata catalog by integrating real-time data observability signals like freshness metrics, anomaly detection, and lineage updates. This means your catalog stays current as your data changes, helping you catch issues faster and maintain high data reliability. It's a great example of combining observability tools with metadata management for smarter data operations.
What role does data quality monitoring play in a data catalog?
Data quality monitoring ensures your data is accurate, complete, and consistent. A good data catalog should include profiling and validation tools that help teams assess data quality, which is crucial for maintaining SLA compliance and enabling proactive monitoring.
What role did data observability play in improving Meero's data reliability?
Data observability was key to Meero's success in maintaining reliable data pipelines. By using Sifflet’s observability platform, they could monitor data freshness, schema changes, and volume anomalies, ensuring their data remained trustworthy and accurate for business decision-making.
Can observability platforms help AI systems make better decisions with data?
Absolutely. AI systems need more than just schemas—they need context. Observability platforms like Sifflet provide machine-readable trust signals, data freshness checks, and reliability scores through APIs. This allows autonomous agents to assess data quality in real time and make smarter decisions without relying on outdated documentation.
Is Forge able to automatically fix data issues in my pipelines?
Forge doesn’t take action on its own, but it does provide smart, contextual guidance based on past fixes. It helps teams resolve issues faster while keeping you in full control of the resolution process, which is key for maintaining SLA compliance and data quality monitoring.
How does a data observability platform help improve inventory accuracy?
A data observability platform continuously monitors inventory data using real-time metrics and anomaly detection. It compares RFID scans with POS transactions, flags inconsistencies, and tracks key inventory KPIs. This helps retailers maintain more accurate stock levels and reduce shrinkage or overstocking.
What’s on the horizon for data observability as AI and regulations evolve?
The future of data observability is all about scale and responsibility. With AI adoption growing and regulations tightening, businesses need observability tools that can handle unstructured data, ensure SLA compliance, and support security observability. At Sifflet, we're already helping customers monitor ML models and enforce data contracts, and we're excited about building self-healing pipelines and extending observability to new data types.
How do Subdomains support self-service and reduce platform team bottlenecks?
Subdomains empower each team to manage their own observability setup, from configuring monitors to setting thresholds. This decentralization speeds up time-to-value and reduces the need for constant involvement from the central platform team, making self-service data observability a reality.

More data. %%Less Chaos.%%

If you want a smoother running stack,
let’s talk about what Sifflet can do for you. 

Contact Us