Should I Build It Should I Build It
14 Jul 2025
SaaS

solving pain points in enterprise search for software engineering ...

...teams

Confidence
Engagement
Net use signal
Net buy signal

Idea type: Swamp

The market has seen several mediocre solutions that nobody loves. Unless you can offer something fundamentally different, you’ll likely struggle to stand out or make money.

Should You Build It?

Don't build it.

Your are here

You're entering a space that appears to be somewhat crowded with mediocre solutions aimed at improving enterprise search for software engineering teams. Our analysis indicates that this area falls into what we call the 'Swamp' category. Five similar products exist, which means some competition is already present. The average engagement is low, suggesting that users are not particularly excited about the existing solutions. Neither positive nor negative use and buy signals were expressed in the discussions for similar products. Ultimately, this suggests that you're facing an uphill battle trying to break through the noise and make an impact.

Recommendations

  1. Begin by deeply researching why existing enterprise search solutions for software engineering teams have failed to gain traction or widespread adoption. Understanding the shortcomings of the current landscape is crucial before investing further into this idea. What are the core pain points that these tools consistently miss or fail to adequately address? Focus on identifying the root causes of dissatisfaction among software engineering teams.
  2. If you decide to proceed, carve out a niche by focusing on a specific subgroup within software engineering that is currently underserved by existing search tools. For example, you could focus on teams working with a particular programming language, framework, or development methodology. By narrowing your focus, you can better tailor your solution to meet their specific needs and differentiate yourself from broader, less targeted offerings.
  3. Explore the possibility of building tools or integrations that enhance the capabilities of existing enterprise search providers rather than directly competing with them. This could involve developing plugins, extensions, or APIs that address specific gaps in their functionality or improve their usability for software engineering teams. Partnering with established players can provide a faster and less risky path to market.
  4. Consider adjacent problems related to software engineering that might present more promising opportunities for innovation. Perhaps focusing on code collaboration, knowledge management, or developer onboarding could offer a more fertile ground for building a successful product. Explore alternative avenues where you can leverage your expertise to create value for software engineering teams.
  5. Based on the feedback from similar products like CodeMate, pay close attention to user experience. Address concerns about direct access to answers and ensure that 'fast mode' provides complete and accurate information. Also, offer multiple sign-in options beyond Google to avoid excluding potential users. Think about ways to stand out from search engines based on a Large Language Model, such as Perplexity. Focus on features specifically useful to software engineers.
  6. Carefully evaluate the profitability of your chosen business model, especially if you plan to offer a free tier. Explore alternative monetization strategies, such as premium features, enterprise subscriptions, or usage-based pricing. Ensure that your revenue model is sustainable and aligned with the value you provide to software engineering teams.
  7. Given criticisms around cloning and originality for similar products, ensure your solution offers unique value and differentiation. Focus on innovative features and approaches that set you apart from existing search engines. Consider consulting the target audience and testing which elements they appreciate most to ensure your solution stands out from the competition.
  8. Take the 'don't build it' recommendation seriously and instead invest your energy into a more promising opportunity. Perhaps the underlying premise for search in this space does not have legs. In the meantime, monitor the market closely and wait for a better opportunity to arise. Sometimes, the best decision is to simply wait for the right moment.

Questions

  1. Given the low engagement with existing enterprise search solutions, what specific, unmet needs or pain points are you addressing that will drive adoption and usage among software engineering teams?
  2. How will you differentiate your solution from existing tools like CodeMate, Perplexity and Phind, and ensure that it offers unique value that justifies its existence in a crowded market?
  3. If initial testing doesn't produce a strong signal, at what point do you pivot away from the idea, and what criteria will you use to determine when to move on to a different opportunity?

Your are here

You're entering a space that appears to be somewhat crowded with mediocre solutions aimed at improving enterprise search for software engineering teams. Our analysis indicates that this area falls into what we call the 'Swamp' category. Five similar products exist, which means some competition is already present. The average engagement is low, suggesting that users are not particularly excited about the existing solutions. Neither positive nor negative use and buy signals were expressed in the discussions for similar products. Ultimately, this suggests that you're facing an uphill battle trying to break through the noise and make an impact.

Recommendations

  1. Begin by deeply researching why existing enterprise search solutions for software engineering teams have failed to gain traction or widespread adoption. Understanding the shortcomings of the current landscape is crucial before investing further into this idea. What are the core pain points that these tools consistently miss or fail to adequately address? Focus on identifying the root causes of dissatisfaction among software engineering teams.
  2. If you decide to proceed, carve out a niche by focusing on a specific subgroup within software engineering that is currently underserved by existing search tools. For example, you could focus on teams working with a particular programming language, framework, or development methodology. By narrowing your focus, you can better tailor your solution to meet their specific needs and differentiate yourself from broader, less targeted offerings.
  3. Explore the possibility of building tools or integrations that enhance the capabilities of existing enterprise search providers rather than directly competing with them. This could involve developing plugins, extensions, or APIs that address specific gaps in their functionality or improve their usability for software engineering teams. Partnering with established players can provide a faster and less risky path to market.
  4. Consider adjacent problems related to software engineering that might present more promising opportunities for innovation. Perhaps focusing on code collaboration, knowledge management, or developer onboarding could offer a more fertile ground for building a successful product. Explore alternative avenues where you can leverage your expertise to create value for software engineering teams.
  5. Based on the feedback from similar products like CodeMate, pay close attention to user experience. Address concerns about direct access to answers and ensure that 'fast mode' provides complete and accurate information. Also, offer multiple sign-in options beyond Google to avoid excluding potential users. Think about ways to stand out from search engines based on a Large Language Model, such as Perplexity. Focus on features specifically useful to software engineers.
  6. Carefully evaluate the profitability of your chosen business model, especially if you plan to offer a free tier. Explore alternative monetization strategies, such as premium features, enterprise subscriptions, or usage-based pricing. Ensure that your revenue model is sustainable and aligned with the value you provide to software engineering teams.
  7. Given criticisms around cloning and originality for similar products, ensure your solution offers unique value and differentiation. Focus on innovative features and approaches that set you apart from existing search engines. Consider consulting the target audience and testing which elements they appreciate most to ensure your solution stands out from the competition.
  8. Take the 'don't build it' recommendation seriously and instead invest your energy into a more promising opportunity. Perhaps the underlying premise for search in this space does not have legs. In the meantime, monitor the market closely and wait for a better opportunity to arise. Sometimes, the best decision is to simply wait for the right moment.

Questions

  1. Given the low engagement with existing enterprise search solutions, what specific, unmet needs or pain points are you addressing that will drive adoption and usage among software engineering teams?
  2. How will you differentiate your solution from existing tools like CodeMate, Perplexity and Phind, and ensure that it offers unique value that justifies its existence in a crowded market?
  3. If initial testing doesn't produce a strong signal, at what point do you pivot away from the idea, and what criteria will you use to determine when to move on to a different opportunity?

  • Confidence: Medium
    • Number of similar products: 5
  • Engagement: Low
    • Average number of comments: 3
  • Net use signal: 1.4%
    • Positive use signal: 7.1%
    • Negative use signal: 5.7%
  • Net buy signal: -5.7%
    • Positive buy signal: 0.0%
    • Negative buy signal: 5.7%

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.

Similar products

Relevance

Perplexity for work that's privacy-focused

09 Jul 2024 Artificial Intelligence

As a developer, I was often frustrated when a PM or HR asked me to fill in random info scattered across Slack, emails, or Jira tickets. I would spend the next 30 minutes toggling between apps and eventually lose focus for the whole day.After talking to hundreds of people who shared this frustration, I decided to build a privacy-focused "Enterprise Search Engine" that does not store users' data, unlike glean.com.After 457 days, 8,213 commits, and the hard work of seven dedicated team members, Findr evolved from a “search engine’ into a ‘Perplexity-style question-answering and content-generating tool’. With Findr, you can:* Answer vague questions like “Are non-US teams working on the 4th of July?”* Handle tasks like “Write documentation for the payment feature.”* Provide solutions to critical issues like “What are the potential reasons for Error code 478?”We have SaaS companies and marketing agencies as customers and support 16 applications.We recently launched a new feature called “collections,” which allows you to create custom GPT bots for your work data. Imagine bots created specifically for each customer, proposals, internal comms, etc.There is still a lot to build. Would love for the community to try it out and share feedback.

A developer has created a privacy-focused enterprise search engine called Findr. One user expressed a desire for such a tool in the banking sector.

Users are frustrated with the scattered information across different apps, making it difficult to find and manage data efficiently.

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Plato – Google-like search for company APIs and docs

20 Jun 2024 Developer Tools

Hi HN, We noticed a common issue working at companies like Amazon and Capital One, searching for documentation relevant to our projects was time-consuming and difficult.After doing some research, we found: - Service catalogs and internal search tools at many companies rely on keyword search (so you can’t just search in natural language for a package that “fetches user favorites”) - Relevant docs can be scattered in many different places - If bugs arise or docs are missing, you have to wait to talk to the right engineer on another teamPlato lets teams create “spaces” for their services/repos with: - An OpenAPI spec plus links to documentation, GitHub, and Slack support. - A chat window that can read your docs using RAG to answer questions about the service and help with debugging issues.The idea is to fit this all into one page, to let devs get the necessary information to start implementing as quickly as possible.We looked at other developer-portal-like tools such as Backstage and Cortex, but didn’t see anything that focuses on making services searchable and making the documentation experience better. We thought that enterprise-search companies like Glean didn’t optimize the workflow of a developer well enough for our liking.That being said, we’re still looking for ways to significantly improve the documentation and API search experience, and are open to feedback. Feel free to sign up, try it out, and email us directly if you have feature requests, suggestions, or other ideas:rohan@platoportal.com arjun@platoportal.comLink: https://platoportal.com

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CodeMate – The Revolutionary Search Engine for Developers

23 Feb 2024 Developer Tools

Users suggested improvements like skipping the landing page and adding Google SSO or GitHub login, with some discussing password-less login methods. There were mixed opinions on the originality, with accusations of copying devv.ai and counter-claims of devv.ai being a clone. Questions were raised about the product's differentiation from competitors like Phind and Blackbox, its profitability with a free model, and the completeness of its 'fast mode'. One comment mentioned using Phind to build a competitor, while another noted the absence of content.

Users criticized the Show HN product for lacking direct answer access and providing incomplete answers in fast mode. There were concerns about the irony of using Phind for competitors and accusations of cloning devv.ai. Users disliked the password system and the exclusive use of Google for sign-in, potentially excluding some coders. There were also doubts about the free model's profitability and a lack of clear differentiation from competitors.

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