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

Discover more title goes here

What Are AI Agents in Data Observability? Sifflet AI Agents Explained

Meet Sentinel, Sage, and Forge, three AI agents built to help data teams monitor smarter, triage faster, and resolve issues with context and confidence. Designed for modern, lean data teams, they bring memory, reasoning, and strategic guidance to every stage of the observability workflow.

Read Now

Agentic Observability As The Solution to the Crisis of Complexity

Agentic observability uses AI agents to detect issues, understand root causes, and guide remediation. Learn why it’s becoming essential for today’s complex data stacks.

Read Now

5 Best Monte Carlo Alternatives for Data Observability

Compare top Monte Carlo alternatives for data observability. Discover modern platforms like Sifflet with business-context monitoring, faster setup, and better pricing for technical teams.

Read Now

Still have a question in mind ?

Frequently asked questions

How does integrating a data catalog with observability tools improve pipeline monitoring?

When integrated with observability tools, a data catalog becomes more than documentation. It provides real-time metrics, data freshness checks, and anomaly detection, allowing teams to proactively monitor pipeline health and quickly respond to issues. This integration enables faster root cause analysis and more reliable data delivery.

Can Sifflet’s dbt Impact Analysis help with root cause analysis?

Absolutely! By identifying all downstream assets affected by a dbt model change, Sifflet’s Impact Report makes it easier to trace issues back to their source, significantly speeding up root cause analysis and reducing incident resolution time.

How can I monitor the health of my ingestion pipelines?

To keep your ingestion pipelines healthy, it's best to use observability tools that offer features like pipeline health dashboards, data quality monitoring, and anomaly detection. These tools provide visibility into data flow, alert you to schema drift, and help with root cause analysis when issues arise.

What makes Sifflet stand out when it comes to data reliability and trust?

Sifflet shines in data reliability by offering real-time metrics and intelligent anomaly detection. During the webinar, we saw how even non-technical users can set up custom monitors, making it easy for teams to catch issues early and maintain SLA compliance with confidence.

How does Sifflet help with data observability during the CI process?

Sifflet integrates directly with your CI pipelines on platforms like GitHub and GitLab to proactively surface issues before code is merged. By analyzing the impact of dbt model changes and running data quality monitors in testing environments, Sifflet ensures data reliability and minimizes production disruptions.

What should I look for in terms of integrations when choosing a data observability platform?

Great question! When evaluating a data observability platform, it's important to check how well it integrates with your existing data stack. The more integrations it supports, the more visibility you’ll have across your pipelines. This is key to achieving comprehensive data pipeline monitoring and ensuring smooth observability across your entire data ecosystem.

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.

What trends in data observability should we watch for in 2025?

In 2025, expect to see more focus on AI-driven anomaly detection, dynamic thresholding, and predictive analytics monitoring. Staying ahead means experimenting with new observability tools, engaging with peers, and continuously aligning your data strategy with evolving business needs.

Read all FAQs