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Detect the "Silent Killers" of AI and Analytics by catching logic flaws and schema drift before they impact production.

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Detect the Silent Killers of AI and Analytics

AI failures are data reliability failures. Sifflet’s intelligent system catches subtle anomalies and schema drift that traditional tests miss, protecting your downstream models from "Garbage In, Garbage Out" scenarios.

Catch data that "looks" fine but is logically flawed, such as negative prices or sudden drops in average order value.
Identify subtle changes in data types or field definitions that don't break the pipeline but silently corrupt BI tools and AI models.
Ensure your data is audit-grade and trustworthy before it is faithfully executed by automated decision engines or LLMs.

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Guarantee Source Integrity

Ensure absolute confidence in your foundational data. Sifflet monitors your entire stack from source to consumption, reconciling data to verify it arrives exactly as expected.

Detect source data anomalies early to ensure feeds come from where you expect without missing records or duplication.
Monitor third-party data feeds and internal pipelines for latency, drift, and completeness before they reach pricing models or core reporting.
Close the gap between ingestion and consumption to provide end-to-end trust across your modern data stack.

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

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Frequently asked questions

What makes Sifflet different from other data observability tools?

Sifflet stands out as a metadata control plane that connects technical reliability with business context. Unlike point solutions, it offers AI-native automation, full data lineage tracking, and cross-functional accessibility, making it ideal for organizations that need to scale trust in their data across teams.

Can I use Sifflet’s data observability tools with other platforms besides Airbyte?

Absolutely! While we’ve built a powerful solution for Airbyte, our Declarative Lineage API is flexible enough to support other platforms like Kafka, Census, Hightouch, and Talend. You can use our sample Python scripts to integrate lineage from these tools and enhance your overall data observability strategy.

How does Sifflet ensure a user-friendly experience for data teams?

We prioritize user research and apply UX principles like Jacob’s Law to design familiar and intuitive workflows. This helps reduce friction for users working with tools like our Sifflet Insights plugin, which brings real-time metrics and data quality monitoring directly into BI dashboards like Looker and Tableau.

What makes Etam’s data strategy resilient in a fast-changing retail landscape?

Etam’s data strategy is built on clear business alignment, strong data quality monitoring, and a focus on delivering ROI across short, mid, and long-term horizons. With the help of an observability platform, they can adapt quickly, maintain data reliability, and support strategic decision-making even in uncertain conditions.

What future observability goals has Carrefour set?

Looking ahead, Carrefour plans to expand monitoring to more than 1,500 tables, integrate AI-driven anomaly detection, and implement data contracts and SLA monitoring to further strengthen data governance and accountability.

How does Sifflet reduce alert fatigue compared to other observability tools?

Sifflet reduces alert fatigue by using AI agents to prioritize alerts based on business impact and historical patterns. It avoids bombarding teams with irrelevant notifications by tuning its anomaly detection models to focus on what truly matters. This makes your observability dashboards more actionable and less overwhelming.

What is SQL Table Tracer and how does it help with data observability?

SQL Table Tracer (STT) is a lightweight library that extracts table-level lineage from SQL queries. It plays a key role in data observability by identifying upstream and downstream tables, making it easier to understand data dependencies and track changes across your data pipelines.

How can data observability support a strong data governance strategy?

Data observability complements data governance by continuously monitoring data pipelines for issues like data drift, freshness problems, or anomalies. With an observability platform like Sifflet, teams can proactively detect and resolve data quality issues, enforce data validation rules, and gain visibility into pipeline health. This real-time insight helps governance policies work in practice, not just on paper.

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