Should I Build It Should I Build It
31 May 2025
SaaS Developer Tools

SERP API tailored for LLMs enabling real time search results

Confidence
Engagement
Net use signal
Net buy signal

Idea type: Run Away

Multiple attempts have failed with clear negative feedback. Continuing down this path would likely waste your time and resources when better opportunities exist elsewhere.

Should You Build It?

Don't build it.

Your are here

The idea of a SERP API tailored for LLMs is entering a space already populated with numerous similar products (n_matches = 19), indicating high competition. While there's medium engagement (avg n_comments = 9) in this space, it's crucial to note that no one explicitly expressed any interest in using or buying such a product, as indicated by the neutral use and buy signals. Many competing products have faced criticism regarding ethical web scraping concerns, output quality, and competition with established solutions like Google's official APIs. Given these challenges and the 'Run Away' category, you should seriously consider re-evaluating this direction. Building in a crowded space without clear positive signals from potential users or buyers might lead to wasted resources.

Recommendations

  1. Carefully analyze the negative feedback from similar products, specifically regarding concerns about scraping ethics, output quality, and competition with established solutions like Google's official APIs. This will help you understand the existing pain points and potential pitfalls in this market. For example, several competitors were criticized for not respecting website policies, cloud-only functionality, or hallucination issues.
  2. Explore related but different problems that leverage your skills in a less saturated market. Consider if there are specific niches within the LLM or data retrieval space where you can offer a unique and valuable solution. Instead of a general SERP API, could you focus on a specific data type or industry?
  3. If you've already built a prototype, assess whether the underlying technology can be repurposed for a different application with higher demand and less competition. Perhaps the crawling and parsing components could be used for internal data analysis or building a specialized knowledge base for a specific domain.
  4. Talk to at least three people who have tried using SERP APIs or similar data retrieval services for LLMs. Understand their specific needs, frustrations, and unmet expectations. This primary research can uncover valuable insights that aren't apparent from online discussions. What are they struggling with today that they would gladly pay to solve?
  5. Based on your research and analysis, pivot to a new idea that addresses a clear market need with a differentiated solution. Focus on building a minimum viable product (MVP) to quickly validate your assumptions and gather user feedback. Ensure your solution is faster, cheaper and better than what's already out there. What are the features that your competitors are missing?
  6. Consider open-sourcing your core capabilities. This approach fosters transparency and community contributions, addressing criticisms around the lack of transparency in some competing products. Could you turn the project into an open-source project on GitHub, accepting and encouraging the community to further improve it?

Questions

  1. Given the existing negative feedback around web scraping ethics, what specific measures will your API implement to ensure compliance with website policies and respect for robots.txt, GDPR, and copyright laws?
  2. How will your API differentiate itself from established solutions like Google's official APIs, and what unique value proposition will it offer to LLM developers to justify switching or using your service instead?
  3. Considering the concerns about output quality and hallucination issues in similar products, what innovative techniques will you employ to ensure the accuracy, reliability, and relevance of the search results provided to LLMs?

Your are here

The idea of a SERP API tailored for LLMs is entering a space already populated with numerous similar products (n_matches = 19), indicating high competition. While there's medium engagement (avg n_comments = 9) in this space, it's crucial to note that no one explicitly expressed any interest in using or buying such a product, as indicated by the neutral use and buy signals. Many competing products have faced criticism regarding ethical web scraping concerns, output quality, and competition with established solutions like Google's official APIs. Given these challenges and the 'Run Away' category, you should seriously consider re-evaluating this direction. Building in a crowded space without clear positive signals from potential users or buyers might lead to wasted resources.

Recommendations

  1. Carefully analyze the negative feedback from similar products, specifically regarding concerns about scraping ethics, output quality, and competition with established solutions like Google's official APIs. This will help you understand the existing pain points and potential pitfalls in this market. For example, several competitors were criticized for not respecting website policies, cloud-only functionality, or hallucination issues.
  2. Explore related but different problems that leverage your skills in a less saturated market. Consider if there are specific niches within the LLM or data retrieval space where you can offer a unique and valuable solution. Instead of a general SERP API, could you focus on a specific data type or industry?
  3. If you've already built a prototype, assess whether the underlying technology can be repurposed for a different application with higher demand and less competition. Perhaps the crawling and parsing components could be used for internal data analysis or building a specialized knowledge base for a specific domain.
  4. Talk to at least three people who have tried using SERP APIs or similar data retrieval services for LLMs. Understand their specific needs, frustrations, and unmet expectations. This primary research can uncover valuable insights that aren't apparent from online discussions. What are they struggling with today that they would gladly pay to solve?
  5. Based on your research and analysis, pivot to a new idea that addresses a clear market need with a differentiated solution. Focus on building a minimum viable product (MVP) to quickly validate your assumptions and gather user feedback. Ensure your solution is faster, cheaper and better than what's already out there. What are the features that your competitors are missing?
  6. Consider open-sourcing your core capabilities. This approach fosters transparency and community contributions, addressing criticisms around the lack of transparency in some competing products. Could you turn the project into an open-source project on GitHub, accepting and encouraging the community to further improve it?

Questions

  1. Given the existing negative feedback around web scraping ethics, what specific measures will your API implement to ensure compliance with website policies and respect for robots.txt, GDPR, and copyright laws?
  2. How will your API differentiate itself from established solutions like Google's official APIs, and what unique value proposition will it offer to LLM developers to justify switching or using your service instead?
  3. Considering the concerns about output quality and hallucination issues in similar products, what innovative techniques will you employ to ensure the accuracy, reliability, and relevance of the search results provided to LLMs?

  • Confidence: High
    • Number of similar products: 19
  • Engagement: Medium
    • Average number of comments: 9
  • Net use signal: -0.5%
    • Positive use signal: 7.9%
    • Negative use signal: 8.4%
  • Net buy signal: -4.7%
    • Positive buy signal: 0.7%
    • Negative buy signal: 5.4%

This chart summarizes all the similar products we found for your idea in a single plot.

The x-axis represents the overall feedback each product received. This is calculated from the net use and buy signals that were expressed in the comments. The maximum is +1, which means all comments (across all similar products) were positive, expressed a willingness to use & buy said product. The minimum is -1 and it means the exact opposite.

The y-axis captures the strength of the signal, i.e. how many people commented and how does this rank against other products in this category. The maximum is +1, which means these products were the most liked, upvoted and talked about launches recently. The minimum is 0, meaning zero engagement or feedback was received.

The sizes of the product dots are determined by the relevance to your idea, where 10 is the maximum.

Your idea is the big blueish dot, which should lie somewhere in the polygon defined by these products. It can be off-center because we use custom weighting to summarize these metrics.

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