Solution by Use Case

Spend Time Where it Matters (not firefighting)

Reclaim Engineering Capacity

Stop playing whack-a-mole with noisy alerts. Reclaim your sprint capacity by automating root-cause analysis and incident triage.

See Sifflet in Action!

Slash MTTR with Context-Enriched Triage

Stop playing detective. Sifflet’s Sage agent centralizes the context you usually have to hunt for, correlating lineage, code changes, and metric drift to provide signal-driven root cause analysis.

Skip the manual detective work and jump directly to the specific job, query, or source that failed.
Reduce incident investigation time from hours to minutes with automated root cause isolation.
Resolve issues faster with the Forge agent, which suggests remediation code and PRs based on your environment's past incidents.

Eliminate Alert Fatigue

First-generation observability created noise; Sifflet creates clarity. Reclaim 30-40% of your sprint capacity by suppressing noise and grouping related alerts into actionable incidents.

Let Sifflet’s Sentinel agent automatically learn the normal behavior of your pipelines, eliminating the need to manually write thousands of unit tests.
Use business context to silence noisy, low-impact alerts, ensuring your team only wakes up for incidents that actually threaten the business.
Group related alerts into a single incident automatically to prevent alert fatigue and streamline engineering workflows.

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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Still have a question in mind ?

Frequently asked questions

How has AI changed the way companies think about data quality monitoring?

AI has definitely raised the stakes. As Salma shared on the Joe Reis Show, executives are being asked to 'do AI,' but many still struggle with broken pipelines. That’s why data quality monitoring and robust data observability are now seen as prerequisites for scaling AI initiatives effectively.

How does Sifflet support data quality monitoring?

Sifflet makes data quality monitoring seamless with its auto-coverage feature. It automatically suggests fields to monitor and applies rules for freshness, uniqueness, and null values. This proactive monitoring helps maintain SLA compliance and keeps your data assets trustworthy and safe to use.

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

SQL Table Tracer (STT) is a lightweight library that automatically extracts table-level lineage from SQL queries. It identifies both destination and upstream tables, making it easier to understand data dependencies and build reliable data lineage workflows. This is a key component of any effective data observability strategy.

What is data distribution deviation and why should I care about it?

Data distribution deviation happens when the distribution of your data changes over time, either gradually or suddenly. This can lead to serious issues like data drift, broken queries, and misleading business metrics. With Sifflet's data observability platform, you can automatically monitor for these deviations and catch problems before they impact your decisions.

Why is data governance important when treating data as a product?

Data governance ensures that data is collected, managed, and shared responsibly, which is especially important when data is treated as a product. It helps maintain compliance with regulations and supports data quality monitoring. With proper governance in place, businesses can confidently deliver reliable and secure data products.

How does the checklist help with reducing alert fatigue?

The checklist emphasizes the need for smart alerting, like dynamic thresholding and alert correlation, instead of just flooding your team with notifications. This focus helps reduce alert fatigue and ensures your team only gets notified when it really matters.

When should organizations start thinking about data quality and observability?

The earlier, the better. Building good habits like CI/CD, code reviews, and clear documentation from the start helps prevent data issues down the line. Implementing telemetry instrumentation and automated data validation rules early on can significantly improve data pipeline monitoring and support long-term SLA compliance.

What makes observability essential for AI governance and ML model reliability?

ML models rely on clean, consistent data. With real-time drift detection and schema monitoring, observability tools catch issues before they impact predictions. One global consulting firm used Sifflet to detect feature drift and schema changes early, keeping their models accurate and their stakeholders confident in the results.

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