Monte Carlo

@montecarlodata Launches @salesforce Integrations for AI-Ready Customer Data martech360.com/tech-analytics… #AIobservability #businessworkflows #CRMData #CustomerDataPlatforms #customerprofiles #GoToMarketoperations #martech360 #MonteCarlo

We're excited to be a Launch Partner for the @AtlanHQ App Framework - a new way to build apps on Atlan’s Metadata Lakehouse using APIs, secure runtimes & marketplace distribution. 🚀 Catch the launch at #AtlanActivate: atlan.com/activate/?utm_…

Data downtime holding you back? 📉 Tune in as Data Cloud Now's Ryan Green and @montecarlodata's Tim Osborn discuss minimizing data downtime with AI-driven data observability. Essential for reliable AI & trusted decision-making at scale! bit.ly/3SWJzFE

Sending a huge thank you to our Black Diamond partners at #SnowflakeSummit: @Accenture, @awscloud, @capitalone, @Cognizant, @Deloitte, @EYnews, @hex_tech, @Informatica, @Infosys, @LTIMindtreeOFCL, @montecarlodata. Don't miss out on all the latest and greatest in data, apps, and AI: snowflake.com/en/summit/?utm…

AI-readiness doesn’t have to be buzzword. Learn how you can get AI-ready with @montecarlodata industry-first observability agents, solutions that allow organizations to monitor and troubleshoot data quality issues with the click of a button. 👉montecarlodata.com/product/observ…

Congratulations to team Monte Carlo for getting named 2025 Databricks Data Governance Partner of the Year! It was great to meet @montecarlodata CEO, Barr Moses, @BM_DataDowntime during @databricks #DataAISummit today. @Jas_MonteCarlo, I missed you! Safe travels! _______________ Monte Carlo created the data + AI observability category to help enterprises drive mission critical business initiatives with trusted data + AI

Raise a glass with us at @Snowflake Summit 🍸 Thinking about what’s next for data and AI? This is where your peers will be 👀 Join Atrium, @montecarlodata, and ThoughtSpot on 6/3 for a rooftop happy hour with drinks, food, and real talk with industry pros. RSVP here: bit.ly/45i7AhG #snowflakesummit #thoughtspot #Networking

Data observability specialist @montecarlodata launched #Observability Agents, a set of #GenAI-powered agents, and unveiled agents for monitoring data and troubleshooting anomalies. Analysis from @StewartLBond and @KevinPetrieTech. bit.ly/4dtR7sT

Transformation & Governance: Tools for data modeling, observability, and lineage. • @dbt_labs – Transformation layer for the modern stack • @AtlanHQ – Active metadata platform • @montecarlodata – End-to-end data observability [🧵 4/7]

This might be the biggest news we’ve shared all year—and I’m so freakin’ excited about it. Monte Carlo just announced our first-ever Observability Agents to accelerate reliability workflows for enterprise teams—beginning with our Monitoring and Troubleshooting Agents to drastically accelerate monitor creation and incident resolution. You may have seen automated monitor suggestions before, but not like this. This is the first AI agent that makes recommendations based on: - a data profile AND - Metadata for the larger contextual meaning AND - Query logs to understand how the data is used. The result is more sophisticated, helpful suggestions—and a 60% acceptance rate. But don’t take my word for it. You can take a demo or a self-guided tour of our Monitoring Agent today. Please check this one out and let me know what you think: lnkd.in/gSDPaAQ2

Monte Carlo Brings AI Agents Into the Data Observability Fold ow.ly/TQ0Y50VCHWo

Monitoring & troubleshooting just got even easier. Introducing the world's first-ever Observability Agents, the Monitoring Agent & Troubleshooting Agent, designed to help data + AI teams find and fix issues faster with AI-driven detection and resolution. montecarlodata.com/blog-monte-car…

Bad data used to be a nuisance. Now? It’s a liability. In the AI era, one broken pipeline can cost millions—or worse, make the wrong decision at scale. Bar Moses (CEO @MonteCarloData) lays it out: ✅Mission-critical data needs constant oversight ✅Focus on high-impact pipelines, not one-size-fits-all ✅Detection isn’t enough — fix issues fast AI depends on trustworthy data. Garbage in, chaos out. Your data stack can’t afford to fly blind.

🚗 Most companies treat data like a car they build and immediately drive 120mph — only to realize the door flies off mid-ride. Bar Moses, CEO of Monte Carlo, breaks down why data & AI observability is the missing layer—and how Monte Carlo helps teams actually trust their data. 🎯 From dashboards to generative AI apps, data teams are building products that run on reliability. Monte Carlo ensures they don’t crash. #DataQuality #AIObservability @montecarlodata

Don't miss @BM_DataDowntime and @hugobowne on the High Signal podcast chatting about why most companies aren't actually AI-ready... and what they can do to fix that. #AIready #data Full episode here: high-signal.delphina.ai/episode/barr-m…

I don't care how big your context window is. While it’s true that running a single complex action on a model with a large context window would lead to a more favorable output than a smaller model all other things being equal, that assumes that you actually need to run that complex action as a single task. The reality is, twenty smaller models running in parallel and outputting a smaller number of tokens will almost always be faster than a single large model running on all the data all at once. And I’ll do you one better—small models can even improve the performance of your AI agents too. Strategies like horizontal task splitting minimize the number of input tokens, output tokens, and model size required to complete an operation—reducing runtime, maintaining (or even reducing) costs, and delivering more deterministic responses for their respective tasks. The secret? Curating the right high quality data to make it work.

Data and AI are no longer two separate technologies. It’s time we stopped treating them that way. That’s why I’m ecstatic to announce that Monte Carlo will be extending our partnership with Databricks to bring our vision for data + AI observability to the Databricks’ Data Intelligence Platform. Databricks SVP of Product Adam Conway says it well: “We’re incredibly excited to see Monte Carlo expanding their data + AI observability capabilities to support unstructured data pipelines in Databricks' Data Intelligence Platform. This collaboration empowers our customers to gain deeper insights and trust in their AI-driven workloads, accelerating innovation with reliable, high-quality data.” If you're a Databricks customer, you can expect: - Coverage for structured and unstructured data pipelines - AI-powered alerts and detection - Lineage tracking to identify incident impact - And root cause within AI agents The first step to adopting any new technology is trust. If you can't trust the technology, you can't depend on it. End-to-end data + AI observability is no longer a nice-to-have; it's essential for the reliable operation of AI agents and applications. This partnership is the next big step to get us there. Link to the full announcement: montecarlodata.com/blog-monte-car…

Is your data AI-ready? Check out @lgavish on @DataScienceDojo talking about how to build scalable and reliable AI! 🚀youtube.com/watch?v=pNVLfc… #DataScience #DataAnalytics #DataEngineering

At #HumanX, women in leadership were pushing the bigger conversations about AI. @montecarlodata | @CredoAI | @honeybook | @LatticeHQ | @Get_Writer | @nylas | @TrustVanta | @veseyventures | @2ndTimeFounders

Bad data is coming for your AI models. Poor data quality has wreaked havoc on dashboards and ML models for years. But at the scale of AI, the data quality challenge is bigger than ever. Relying on manual data quality checks to effectively cover the massive volumes of data feeding AI models is like tossing a log into the Amazon and hoping it makes a dam. Without automation and comprehensive root-cause insights to make alerts immediately actionable, we can't hope to protect our data at the breakneck pace of AI development.