Capalyze
ChatGPT for datavores: scrape → ask → visualize
4.4•14 reviews•1.7K followers
ChatGPT for datavores: scrape → ask → visualize
4.4•14 reviews•1.7K followers
Scrape real data from websites in real-time into Univers (our spreadsheet engine with 27.5k stars on GitHub!). Ask questions, visualize with interactive tables & charts, and then slice, dice, and export — all in one place.
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Launch Team
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Promoted
Hi Leon,
I am Leo, the maker of https://videoweb.ai/
Thanks for sharing Capalyze! It sounds impressive and I’m curious to learn more. How does Capalyze ensure the accuracy of the generated insights, especially when analyzing complex or unstructured data? And is there a way for to verify or trace back the sources used in the analysis?
Looking forward to your reply!
@hello_leo Thank you so much for your interest!
At the moment, Capalyze focuses on processing and analyzing tabular data — for example, identifying tables embedded in web pages and then handling them through our spreadsheet agent. It doesn’t directly feed raw HTML or unstructured data into AI for analysis.
During the analysis, all statistical data is presented as artifacts, so every insight is backed by transparent, traceable evidence.
@videoweb_ai is awesome too!
google sign in bug.
@ernestine_demars We’re very sorry. After investigation, we found that the size of your avatar was too large, which triggered our bug. Our developers will fix it right away.
Love the approach of leveraging a specialized spreadsheet agent orchestration system to tackle the complexities of data scraping and analysis. The focus on eliminating AI hallucinations by providing traceable, real-world data is a massive win for any datavore. This feels like a powerful evolution beyond simple prompt-to-answer models.
How are you handling more complex, multi-step queries that require chaining different agents, like scraping data and performing a sentiment analysis on a specific subset of that data?
@wbfsa
@sahil_shinde1 Thanks!
We use a minimalist multi-agent orchestration approach, similar to Claude Code’s. A central Capalyze agent coordinates sub-agents such as a scraping agent for structured data extraction and sentiment analysis agents. The scraping agent stores results as artifacts for easy access, while the Capalyze agent plans and assigns tasks across all sub-agents.
Capalyze looks super powerful, I like that you’re tackling both data accuracy and transparency. How does it perform on really messy datasets, like social media comments with emojis, slang, or mixed languages?
@sofiia_havryliuk Thanks for your attention! Even messy datasets can be treated as tables. If it’s in table form, with each row corresponding to a user comment, Capalyze can process them row by row for sentiment labeling and keyword extraction.
It is fascinating how Capalyze abstracts the full pipeline — from web scraping to NL query to visualization — in a single interface.
I am impressed by the solution and I believe with more updates, I will become a full time user as well.
Dropped a review - https://www.linkedin.com/posts/supreethere_capalyze-chatgpt-for-datavoresscrape-activity-7378436392462499840-_EOO?utm_source=social_share_send&utm_medium=member_desktop_web&rcm=ACoAADMvxpQBE1vIRDheDTuEKKaFfXdj_KRyqPI
@supreethere Thanks a lot!
First of all congratulations on your launch! It seems promising!
I tried using the product and faced an issue if you could help me around it? As soon as i click "start chat" nothing happened.
Congrats on the launch @Capalyze team
@ritik_singh_panwar Thank you, Ritik!