Queries – Natural Language Data Analysis from Structured

Hacker News

Hello HN community, We’re excited to share our latest feature at Structured, called Queries. We've focused on making data analysis as intuitive as possible, and are eager to hear your feedback. Key Features: - Natural Language Understanding: Imagine querying your data with simple questions like, "Show recent error logs" or "Summarize last month's sales." Queries translates these into actionable data insights, bypassing traditional query complexities. - Direct Data Connectivity: Connect your datasets easily, whether they're in S3 buckets or uploaded directly. Queries handles various data formats, making it a versatile tool for any data source. - Instant Data Insights: Get real-time answers without the need for intricate database queries. It's about making data exploration quick and accessible, even for non-technical users. - Customizable Output: Tailor your data presentation to suit your specific requirements. Whether it's sorting, filtering, or visualizing, Queries adapts to your needs. Potential Benefits: - Enhance Productivity: By simplifying data interrogation, Queries can significantly speed up data analysis and decision-making processes. - Democratize Data Analysis: It's not just for developers or data scientists. Queries opens up data exploration to a broader range of users, fostering a more inclusive data-driven culture. - Reduce Reliance on Technical Teams: Empower users across your organization to answer their own data questions, freeing up your technical staff for more complex tasks. We believe Queries can be valuable, whether for troubleshooting, reporting, or gaining business insights. It’s aiming to make data analysis more intuitive and less time-consuming. Curious about your thoughts, potential use cases, or any feedback you might have. For those interested, more info here: app.structuredlabs.io Thanks!

Visit product View on Hacker NewsJan 23, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, visual, tasks · Missing: mac, agents, macos

87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: intuitive, users · Missing: plus, platform, reviews

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month, answers, users · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: excited, lua, io · Missing: https docs, just released, exist

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

8%8% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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