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NeoShift BI – Build AI-analyzed data dashboards in minutes

Hacker News

NeoShift BI – Build AI-analyzed data dashboards in minutes

Hi HN, I'm Donald. I’m a solo developer building NeoShift BI (and its backend companion, NeoShift ETL). Enterprise BI tools like PowerBI or Looker are incredibly powerful, but they usually require dedicated data engineering teams to manage. On the other end of the spectrum, dumping a CSV into ChatGPT is great for a quick question but terrible for building reproducible, interactive, and shareable dashboards. I wanted something in the middle: a lightweight BI tool tailored for indie devs and SMEs that lets you drop in a dataset and get a full dashboard with AI-generated insights in just a few minutes. The Stack & Architecture: Infrastructure: Google Cloud AI Engine: Claude. We don't just use LLMs for text summaries; Claude powers the entire data infrastructure layer. It natively reads and understands your schema to power: Data Studio Assistant: Automatically finds table relationships and generates complex wide views. Chat-to-Data: Query your datasets naturally without writing SQL. AI Chart Builder: Describe what you want to visualize, and the AI constructs the exact chart to drop into your dashboard. Insight Blocks: Reasons directly over the live data to generate executive summaries and strategic takeaways. The Demo (Why Startups Fail): To stress-test this, I dropped a Kaggle dataset of 409 startup post-mortems into NeoShift. The AI immediately highlighted that 75% of startups are actually killed by established Giants, not a lack of funding. You can see the generated dashboard and read the AI's analysis here: https://bi.neoshift.ai/#/public/dashboard/startup-failure-pr... Stress-Test My App (The Open Beta Challenge): I just shipped a new "Public Share Links" feature and I want to test the infrastructure limits. I’m extending our Open Beta to March 27th and running a data storytelling challenge. If you want to break my app or just visualize some cool data, grab a weird dataset from Kaggle, drop it into NeoShift, and share the link. I'm giving a Free Lifetime Enterprise Account (100GB storage, 5 seats) to the dashboard with the best combined score (unique views + feature usage). To track the competition, I built a live Leaderboard using NeoShift BI itself (connected to the Google Analytics API). You can check the exact rules and current standings here: https://bi.neoshift.ai/#/public/dashboard/competition-leader... You can unlock a free share link to test it out with code BETA-320430B4 here: https://bi.neoshift.ai/#/register I’d love your brutal, unfiltered feedback on the UI, the depth of the Claude integration, and how the system handles whatever weird CSVs you throw at it. Happy to answer any questions about the architecture!

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, google, new · Missing: mac, agents, macos
93%93% 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
62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google, visualize, way · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: builder · Missing: plus, platform, intuitive
29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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