Wild Moose
Automating root cause analysis with generative AI
Wild Moose automatically kicks off any root-cause investigation to improve MTTR. Our AI triages issues, analyzes impact, and suggests next steps - all before human intervention is needed.
Active Founders
Yasmin Dunsky, Founder
Yasmin holds an MBA from Stanford, and has a successful track record in founding and leading ventures, including an ecosystem that trains thousands of female software developers across Israel. She served as an analyst for the Israeli Special Forces Intelligence and holds a BSc in Computer Science from The Hebrew University.
Roei Schuster, Founder
I'm the CTO of Wild Moose. Before, I was a post-doc at the Vector Institute of AI, after completing a PhD at Cornell and Tel Aviv University. My research focused on security and privacy of AI, and particularly NLP. Before my that, I did my undergrad at the Technion, and served in IDF's 8200 alongside my cofounder Tom.
Tom Tytunovich, Founder
Building software stuff since 2002. In the IDF's Intelligence Corps, in tiny startups and large corporations as software developer, architect, and engineering manager, as CTO of a nonprofit building technology with people who are homeless in NY, and now as co-founder of Wild Moose.
Company Launches
Problem: Production chaos is real, and interferes with fast delivery
These days, there's immense pressure for organizations to constantly innovate. To achieve this, you need to aggressively minimize distractions. It's crucial to eliminate the constant stream of alerts, often leading to full nights of stressful debugging.
Solution: AI agents can knock down issues before they get to engineers
Our AI agent kicks off every investigation by querying logs, metrics, DBs, and code to surface information and resolve issues. The initial steps in investigations are predictable and automatable, but they take time for a human to perform. We save that time. In many companies this simple step already cuts MTTR by 50%.
▶️▶️ Chat with us to join the beta.
How it works:
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Collects data from monitoring data sources
When a new production issue is found, the moose kicks off by collecting data from your monitoring data tools, such as Datadog, Snowflake, New Relic, Cloudflare, etc. -
Generates an enrichment report
The moose runs an investigation in real-time, analyzing the data collected to present its findings. -
Concludes and recommends next steps
The moose presents a summary of its findings along with recommended next steps, focusing on impact and mitigation. -
Improves over time
You can indicate what information was useful or ask the moose to fetch more. Based on this feedback and an underlying model of your system, our model learns over time the actions it should take in different scenarios.
Try it out and tell us what you think!
▶️▶️ Let’s chat and get you on our beta.
We’re looking to work with companies that are making an effort to improve their reliability, preferably companies that already measure MTTR or are interested in starting to do so.
- You don’t have to prepare your data or monitoring tools. We know things are often messy - our model works just as well with data that is unstructured
- You do need to have your logs and/or metrics stored in a SaaS platform (Datadog, New Relic, Coralogix, etc.).
Other Company Launches
🔥 Wild Moose: Solve production incidents faster with Gen AI
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