A few months ago we noticed a pattern. Every GTM, product, and marketing team we talked to had the same problem. They were drowning in external data from Reddit, Discord, Slack communities, competitor sites, and social channels. But turning all of that noise into something structured and useful took an enormous amount of time.
We watched people spend days copying screenshots into spreadsheets, tagging posts, and checking competitor websites by hand. We were doing the same thing ourselves and it was obvious that none of this should be manual. So we built Sushidata, a system of AI agents that collect, organize, and summarize messy external data into a searchable, structured view of a market.
Here's a demo you can check out. it's not in prod so excuse the bugs and issues. Feel free to make your own sheets. Happy to help there too.
Demo link: https://tinyurl.com/sushidata
What it does:
- Pulls in competitor updates, customer sentiment, complaints, feature requests, and more Normalizes everything into a single datasheet
You can ask questions such as:
- "What issues are trending for our competitors this week?"
- "What is the summary of all the products from my competitors"
Why we built it:
- We wanted to eliminate the repetitive parts of research. Early users told us the system saved them weeks of manual work, so we kept building.
What’s in the demo
- A real competitive intel dataset, competitor monitoring, a spreadsheet style interface powered by AI agents
Feedback welcome We would love thoughts from the HN community, especially around the data pipeline, agent behavior, and ways to simplify the interface. Happy to answer questions about how it works under the hood.
Victor, co-founder Sushidata