Wild Moose helps on-call developers more quickly identify the source of production incidents, by providing a conversational AI trained on their environment. When you’re on-call and everything’s on fire, instead of frantically sifting through logs and going through other people’s code, our moose just gives you the answers. Debugging in production with our moose allows you to solve issues in minutes instead of hours, reducing MTTR 100x. It helps you avoid costly downtime, save time, and keep your SLAs in check.
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Wild Moose reposted this
NYC friends! Come through the snow to SREday tomorrow ❄️ I’ll be speaking with Avner Yaacov from Redis about how Redis and Wild Moose automated investigations across thousands of clusters, taking typical diagnostic time from ~20 minutes to under a minute. If you’re in town and care about reliability as a system, would love to see you there. Mark Pawlikowski, Miko Pawlikowski 🎙️, Anna Andriushchenko, Aleksandra Los
Wix runs 4,000+ microservices handling billions of daily requests — manual incident response couldn't keep up. Together, Wild Moose and ClickHouse turned that into AI-driven investigations at production scale. The results: 90% root cause accuracy, 50% projected MTTR reduction, and 30,000+ alerts enriched per month across hundreds of teams. Proud to partner with the ClickHouse and Wix teams to drive these results. 🫎
When an alert fires at Wix, AI agents now investigate it the way a senior engineer would — querying logs, correlating signals across services, checking recent deployments — and deliver findings to Slack in under a minute. The result: 90% root cause accuracy across thousands of alerts and a projected 50% reduction in MTTR. The foundation? A ClickHouse cluster handling 300M events/min with no sampling, giving AI agents the full-fidelity data they need to reason about failures. Combined with Wild Moose's agentic AI SRE platform, Wix turned tribal knowledge into automated investigations at scale.
Wild Moose reposted this
Excited to speak at SRE Day NYC alongside Yasmin Dunsky, the CEO of Wild Moose. We will share how Redis utilized Wild Moose to develop troubleshooting agents that convert tribal knowledge into structured investigation workflows, automating essential components of the operational flow. Join us to learn about the cultural and organizational shift necessary to effectively scale reliability through AI. We will discuss how we evolved our approach, what it took to gain buy-in across teams, and how this investment, grounded in true partnership and deep collaboration with the Wild Moose team, positioned us as leaders in AI-driven operations. See you in NYC 🏙️ ! #SREDay #AI #SRE #CloudOps #WildMoose #Redis #ReliabilityEngineering #IncidentManagement https://lnkd.in/ePze7qHN
Wild Moose reposted this
Come say hi and hear me talk about how to design AI SRE systems that teams can actually trust in production. Fast, testable, configurable, and transparent. The same standards you’d expect from any observability system.
🤖 Why does applied AI fall short once it reaches production systems? Automation alone rarely changes how teams respond when systems misbehave. 🎙️𝙒𝙝𝙖𝙩 𝙊𝙗𝙨𝙚𝙧𝙫𝙖𝙗𝙞𝙡𝙞𝙩𝙮 𝘾𝙖𝙣 𝙇𝙚𝙖𝙧𝙣 𝙁𝙧𝙤𝙢 𝘽𝙄: 𝘿𝙚𝙘𝙤𝙪𝙥𝙡𝙞𝙣𝙜 𝙛𝙤𝙧 𝙎𝙥𝙚𝙚𝙙, 𝙎𝙘𝙖𝙡𝙚, 𝙖𝙣𝙙 𝙁𝙡𝙚𝙭𝙞𝙗𝙞𝙡𝙞𝙩𝙮 Yasmin Dunsky from Wild Moose explains where AI efforts lose traction and how operational context changes outcomes. 🗓️𝗙𝗲𝗯𝗿𝘂𝗮𝗿𝘆 𝟮𝟲 𝗮𝘁 𝟵:𝟬𝟬 𝗔𝗠 📍𝗛𝗮𝗿𝗻𝗲𝘀𝘀 𝗢𝗳𝗳𝗶𝗰𝗲, 𝗡𝗲𝘄 𝗬𝗼𝗿𝗸, 𝗨𝗦 👉𝗦𝗮𝘃𝗲 𝘆𝗼𝘂𝗿 𝘀𝗲𝗮𝘁: https://lnkd.in/erhygjqg 🔥 Yasmin works on AI systems used by engineering teams in live production environments. 🏆Harness Cribl ilert Imply Wild Moose Xurrent HockeyStick Show🏆
Wild Moose reposted this
🤖 Why does applied AI fall short once it reaches production systems? Automation alone rarely changes how teams respond when systems misbehave. 🎙️𝙒𝙝𝙖𝙩 𝙊𝙗𝙨𝙚𝙧𝙫𝙖𝙗𝙞𝙡𝙞𝙩𝙮 𝘾𝙖𝙣 𝙇𝙚𝙖𝙧𝙣 𝙁𝙧𝙤𝙢 𝘽𝙄: 𝘿𝙚𝙘𝙤𝙪𝙥𝙡𝙞𝙣𝙜 𝙛𝙤𝙧 𝙎𝙥𝙚𝙚𝙙, 𝙎𝙘𝙖𝙡𝙚, 𝙖𝙣𝙙 𝙁𝙡𝙚𝙭𝙞𝙗𝙞𝙡𝙞𝙩𝙮 Yasmin Dunsky from Wild Moose explains where AI efforts lose traction and how operational context changes outcomes. 🗓️𝗙𝗲𝗯𝗿𝘂𝗮𝗿𝘆 𝟮𝟲 𝗮𝘁 𝟵:𝟬𝟬 𝗔𝗠 📍𝗛𝗮𝗿𝗻𝗲𝘀𝘀 𝗢𝗳𝗳𝗶𝗰𝗲, 𝗡𝗲𝘄 𝗬𝗼𝗿𝗸, 𝗨𝗦 👉𝗦𝗮𝘃𝗲 𝘆𝗼𝘂𝗿 𝘀𝗲𝗮𝘁: https://lnkd.in/erhygjqg 🔥 Yasmin works on AI systems used by engineering teams in live production environments. 🏆Harness Cribl ilert Imply Wild Moose Xurrent HockeyStick Show🏆
Wild Moose reposted this
Most “AI SRE” today spends 10+ minutes reasoning, burns a ton of tokens, and then guesses a root cause. We only started seeing real value when we prioritized transparency, predictability, and configurability. The same qualities we expect from any reliability system. Instead of one magical agent, you have a system of expert agents customized to your company. Power users encode company-specific debugging logic into a set of versioned, testable agents that respond to incidents and coordinate in real time. It's not out-of-the-box magic; it's your best way to get robust AI reliability. A system that behave like engineering tools should: deterministic, traceable, and predictable. In the photo: our team building the future of AI SRE. 🫎
Wild Moose reposted this
Our customers expect the best from Redis, and they should. Alongside our software, that expectation is met by the best engineers and operators in the world, supporting Redis and Redis Cloud every day. In Redis Cloud, we chose to integrate Wild Moose into our AI and automation operational framework, powered by Redis, to help us deliver 10x outcomes at scale. https://lnkd.in/ehxTNm6y
Wild Moose reposted this
What happens when observability platforms hit their limits? In 2025, teams stopped asking "are we collecting enough data?" and started asking "can we actually use this data?" Mike Shi's year-in-review covers the shift from volume to cardinality, the rise of trace-first architectures, early wins in AI-driven SRE (shoutout to Traversal, Resolve AI, Wild Moose), and the maturation of the OpenTelemetry ecosystem with tools from Odigos and Bindplane https://lnkd.in/g7FXySvr
Wild Moose reposted this
The Wild Moose team apologizes for moose-flooding your feed… we couldn’t help ourselves. Happy Holidays!
Wild Moose reposted this
2022: Tom, Roei, and I move in together for three months as an experiment. If it works, we start a company together. 2023: We get into Y Combinator. Launch Wild Moose. Write the first line of code. Sign our first design partners. 2024: Iterate. Iterate. Iterate again. AI Agents become a thing, but making them reliable in production is hard. Good thing we’re working with some of the brightest, most ambitious engineers, side by side with customers, to get it exactly right. 2025: First paying customer. First six-figure contract. First enterprise deal. Seed round closed. Dozens of on-site implementation events. Hundreds of demos. Thousands of PRs. Hundreds of thousands of root causes solved. The momentum is real. 2026 is our year.