B37 Ventures

BIG ENTERPRET LAUNCH DAY! 🚀🙏 We are thrilled to launch, Wisdom, Enterpret's AI Copilot for customer insights. Wisdom can scour your entire customer feedback dataset—sales calls, support tickets, surveys, social media, reviews, slack messages, Snowflake tables—and convert it into genuinely actionable insights, in seconds. All insights are backed with citations from raw customer quotes, and the actual natural language query setup is inspectable and editable to its core to ensure complete transparency. My favorite workflow with Wisdom while working with our beta customers - Step 1 - Asking Wisdom to plot a trendline of all customer feedback reasons from a particular segment. Step 2—Double-click on a particular trend by asking Wisdom to summarize all the sub-reasons behind one trending issue. Step 3 - Spotting one sub-reason that seems like an immediate fix opportunity and ask Wisdom to write a PRD for that one sub-reason, backed with customer feedback citations for full context. For the last 3 years, we've been focused on converting valuable unstructured customer conversations from all channels into a time series database that is SQL queryable, thus creating an extremely valuable dataset for our customers that's updated continuously. With Wisdom, the power of that data set is unlocked for every one in your team through just natural language! Quick 90-second video to demo how exactly :)

Data teams should be the first to know about data issues. Today I’m happy to announce: Metaplane raised a $13.8M Series A to automate that 🚀 When data teams wake up to messages like “why does this number look weird?” and “this dashboard seems broken,” time is lost and trust in data is eroded. Trusted data is the foundation on which companies progress from business intelligence to automation to generative AI. The problem is that data teams are responsible for an expanding surface area of data (entropy), while customers care most about what’s in front of them (asymmetry). We founded Metaplane in 2019 to provide leverage against these forces through automations and metadata. Last year, we shared our plans to widen the aperture of data observability from detection to prevention, and from quality to usage and spend. We built those and more through 50+ product launches. And in parallel, we grew our revenue by 6x and customer base by 3x, ranging from the fastest-growing companies like Ramp and Klaviyo, to large enterprises like Bose and Sotheby’s. Product power and customer growth are two sides of one principle: iron sharpens iron. Solving problems for customers is our compass. Some classic examples: • Shipping Reverse ETL integrations in two days for ClickUp  • Collaborating with SpotOn to extract lineage from Airflow  • Architecting high cardinality GROUP BY monitors to meet Bluecore’s scale So our first thank you is to our customers for giving us the opportunity to assist in humble ways. Customers often describe the product and people of Metaplane as extensions of their teams. That’s exactly how we want it to feel. We’re also grateful to our investors for championing a future where every company trusts their data. Our round was led by Felicis who will be joining our board. Existing investors Khosla Ventures, Flybridge, SNR, Stage 2 Capital, and Y Combinator doubled down, while B37 Ventures joined the round. Lastly, we appreciate our partners, especially folks at Snowflake, Brooklyn Data Co. (a Velir company), dbt Labs, and Sigma . Looking forward, now is the time to elevate data observability from 𝘱𝘰𝘴𝘴𝘪𝘣𝘭𝘦 to 𝘱𝘰𝘸𝘦𝘳𝘧𝘶𝘭 by investing in three product pillars: 1. 𝐎𝐛𝐬𝐞𝐫𝐯𝐞 𝐄𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠. Each piece of metadata from your data systems will be collected and integrated. 2. 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞. How your data systems are monitored should reflect the needs of your business, then evolve as your business evolves. 3. 𝐓𝐫𝐮𝐬𝐭 𝐄𝐯𝐞𝐫𝐲𝐰𝐡𝐞𝐫𝐞. The trustworthiness of data should be known everywhere it is consumed, when it is consumed. How will we traverse the tech tree to reach that future? And how can you get early access? Make sure to read the post linked in the comments below 👇 #dataengineering #dataquality #dataobservability #analytics #data