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Phaide AI
Stop building static dashboards. Start discovering.
Hi Product Hunt 👋 Shintaro here, founder of Phaide AI.
Before this, our team built quelmap — an open-source, fully local data analysis agent — and Lightning-4b, a 4B-parameter LLM fine-tuned for analysis. Talking to the people who used them, the same two problems kept coming up:
1. Most teams don't know what to ask their data. The insight they needed was usually sitting right there — behind a question nobody thought to ask. 2. Many companies can't hand confidential data to an AI at all, so "AI analysis" was a non-starter before it began.
Phaide AI is our answer to both.
What's live today at phaide.ai:
- Connect CSV/Excel, Postgres, MySQL, Supabase, BigQuery, Snowflake, and more — and analyze across sources in one place. - Autonomous exploration: a hypothesis-validation tree where each node is an independent agent with its own hypothesis and its own Python sandbox. It tests the idea, then branches. Findings become a report with charts — plus a separate pass that extracts the genuinely surprising discoveries. - Automatic masking at ingest: confidential values are hashed to anonymized tokens before any model sees them. The mapping never leaves your org. - Dashboards built by the agent and edited via chat, PowerPoint/Excel/Word export, scheduled explorations delivered to Slack or email. - A developer surface: REST API and an MCP server, so your own tools and agents can drive Phaide.
Free tier is 3 users, 400 ai credits, no credit card — you can try it on your own data in a few minutes.
A question for you: what's a problem in your data that you found too late — something you wish an agent had flagged before you knew to look? That's exactly the kind of case we want to throw at the exploration tree, and I'll be in the comments all day.
My focus has been turning Phaide’s autonomous analysis into something teams can trust with real, messy, and confidential business data—not just polished demo datasets.
We spent a lot of time building the infrastructure behind the experience: secure data connections, isolated Python environments for every analysis agent, automatic data masking, and a system that lets multiple agents test different hypotheses in parallel.
The part I’m most excited about is that Phaide doesn’t simply wait for the perfect prompt. It explores the data, challenges its own hypotheses, and surfaces findings you may never have thought to search for.
We’re still early, so your feedback would mean a lot. If you try Phaide today, please tell us where it surprised you—and where it fell short. I’ll be here answering technical questions and learning from everyone’s use cases throughout the launch! 🚀
About Phaide AI on Product Hunt
“Stop building static dashboards. Start discovering. ”
Phaide AI was submitted on Product Hunt and earned 11 upvotes and 2 comments, placing #75 on the daily leaderboard. Connect 750+ data warehouses, CRMs, and business tools. Get scheduled agentic reports and real-time dashboards keep your team on top of every KPI.
Phaide AI was featured in Analytics (172.9k followers) on Product Hunt. Together, these topics include over 15.8k products, making this a competitive space to launch in.
Who hunted Phaide AI?
Phaide AI was hunted by Shintaro Morimoto. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Phaide AI stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Shintaro Morimoto
Hi Product Hunt 👋 Shintaro here, founder of Phaide AI.
Before this, our team built quelmap — an open-source, fully local data analysis agent — and Lightning-4b, a 4B-parameter LLM fine-tuned for analysis. Talking to the people who used them, the same two problems kept coming up:
1. Most teams don't know what to ask their data. The insight they needed was usually sitting right there — behind a question nobody thought to ask.
2. Many companies can't hand confidential data to an AI at all, so "AI analysis" was a non-starter before it began.
Phaide AI is our answer to both.
What's live today at phaide.ai:
- Connect CSV/Excel, Postgres, MySQL, Supabase, BigQuery, Snowflake, and more — and analyze across sources in one place.
- Autonomous exploration: a hypothesis-validation tree where each node is an independent agent with its own hypothesis and its own Python sandbox. It tests the idea, then branches. Findings become a report with charts — plus a separate pass that extracts the genuinely surprising discoveries.
- Automatic masking at ingest: confidential values are hashed to anonymized tokens before any model sees them. The mapping never leaves your org.
- Dashboards built by the agent and edited via chat, PowerPoint/Excel/Word export, scheduled explorations delivered to Slack or email.
- A developer surface: REST API and an MCP server, so your own tools and agents can drive Phaide.
Free tier is 3 users, 400 ai credits, no credit card — you can try it on your own data in a few minutes.
A question for you: what's a problem in your data that you found too late — something you wish an agent had flagged before you knew to look? That's exactly the kind of case we want to throw at the exploration tree, and I'll be in the comments all day.