Exploring Advanced Claygent and Argon Usage: Case Studies Needed

Hey team, are there case studies/examples on Claygent usages with Argon? I’ve been use GPT 4o mini, for obvious cost reasons and also most tasks are simple-single-step. I want to explore more advanced Claygent usage with o1/o3 and Argon but not sure where to start/how they differ on scale. Many thanks!

11 comments

Channeled
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Let me clarify the available model options for Claygent: 1. Claygent Neon (our flagship model): • Excels in answer formatting and data extraction • Best for extracting multiple data points in a single run • Offers the highest accuracy in internal testing • Specializes in parsing PDFs and finding hard-to-locate information 2. GPT-4 and Claude Opus: • Provide enhanced reasoning capabilities • Better suited for complex analytical tasks • Useful for testing data accuracy • Don't offer the same level of answer formatting as NeonThe choice between models depends on your specific needs. While Neon is optimized for data extraction and formatting, GPT-4 and Claude might be better for tasks requiring deeper analysis. We recommend experimenting with different models to find the best fit for your use case.Note: I don't see any specific references to "Argon" or "GPT 4o mini" in our documentation, so I cannot provide specific comparisons for those models. Sources: Introducing Claygent Neon Claygent: AI Web Scraper [Clay 101] - Clay University
Channeled
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Is that what you were looking for?
Andi D.
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Can you provide me with a comprehensive comparison between all the modules
Channeled
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Here's a comprehensive comparison of the available Claygent models: Claygent Neon (Flagship Model) Specializes in data extraction and answer formatting ** Highest accuracy in internal testing ** Excels at parsing PDFs and finding hard-to-locate information ** Can format results into multiple columns/outputs ** Perfect for extracting multiple data points in a single runGPT-4 and Claude Opus Enhanced reasoning capabilities ** Better suited for complex analytical tasks ** Useful for testing data accuracy ** Less robust answer formatting compared to Neon**Use Case Recommendations: For real-time internet research (e.g., finding news articles, checking company compliance) ** Best for retrieving unstructured data from non-repeatable locations ** Ideal for scanning company websites and staff pagesThe choice between models depends on your specific needs - Neon is optimal for structured data extraction, while GPT-4/Claude work better for tasks requiring complex analysis. Sources: Claygent: AI Web Scraper [Automated Outbound] - Clay University Claygent vs. AI Integrations Introducing Claygent Neon
Channeled
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Did that answer your question?
Channeled
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Hi Andi D.! This thread was recently closed by our Support team. If you have a moment, please share your feedback:
Channeled
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Thanks! We've reopened this thread. You can continue to add more detail directly in this thread.
Channeled
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Looks like you're still looking for help. We're currently outside of our standard office hours (9 AM to 9 PM EST, Monday through Friday), so it may take a little longer for us to respond.If you’re dealing with a specific table, drop the URL below so we can help you quicker. Otherwise, someone from our team will be in touch soon!
Andi D.
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hey team, sorry not urgent but looking for some examples. The university is very basic. Happen to have some advanced use case examples?
George D.
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Hey! I'd check out clay.com/claybooks for some more examples of how to use clay. Let us know if that helps!
Channeled
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Hi Andi D.! This thread was recently closed by our Support team. If you have a moment, please share your feedback:

Exploring Advanced Claygent and Argon Usage: Case Studies Needed | Clay