an enterprise search platform offering google-like search on company ...

...data, enhanced by AI natural language understanding

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 crowded space for enterprise search platforms, a sector that falls into the 'Swamp' category. This means many have tried and most have delivered mediocre solutions. Our analysis of 20 similar products reveals low engagement (average of 3 comments), suggesting it's tough to capture user attention. The lack of strong positive 'use' or 'buy' signals further underscores this challenge; people aren't overwhelmingly expressing a desire to use or pay for similar products. Standing out will require a fundamentally different approach, given the existing solutions haven't captured hearts or wallets. Your focus should be on addressing underserved needs or pivoting towards adjacent problems within the enterprise data landscape. Before investing heavily, rigorously examine the failures of current solutions to pinpoint genuine opportunities.

Recommendations

  1. First, deeply investigate why existing enterprise search solutions haven't resonated with users. Don't just look at features; understand the underlying frustrations and unmet needs within organizations. Analyze the criticism from similar product launches; for example, many users complain about the poor native search on platforms like Confluence, Slack and Notion. Use this insight to identify gaps you can uniquely fill.
  2. If you proceed, target a very specific niche within the enterprise. General-purpose search solutions often fail. For example, you could focus on search specifically for legal documents, research data within pharmaceutical companies, or even internal communication archives. Being laser-focused allows you to tailor the AI's natural language understanding to the specific jargon and context of that niche, offering a superior experience.
  3. Consider providing AI-enhanced tools to existing enterprise search providers instead of building a whole platform from scratch. Many companies already have search infrastructure in place (e.g. ElasticSearch, Algolia, etc.). Your AI-powered natural language understanding could be integrated as a 'plugin' to improve the accuracy and relevance of results within these existing systems. This reduces the barrier to entry and allows you to leverage established distribution channels.
  4. Explore adjacent problems that are closely related to enterprise search but may be more promising. For example, instead of just search, focus on knowledge discovery or automated summarization of internal documents. You could build a tool that proactively surfaces relevant information to employees based on their current projects or roles, eliminating the need for them to even search in the first place.
  5. Before committing significant resources, create a simple, no-code prototype to validate your core assumptions. Focus on a narrow use case and demonstrate how your AI-powered search delivers significantly better results than existing solutions. Get feedback from your target niche and iterate rapidly based on their input. This minimizes risk and ensures you're building something people actually want.
  6. Carefully consider your go-to-market strategy. Enterprise sales can be complex and time-consuming. Explore alternative channels such as partnerships with existing software vendors, content marketing targeted at your niche, or even building a free, open-source version of your tool to gain traction and build a community. The comments on similar product launches show the poor website design can be detrimental; don't let design shortcomings sink the product.

Questions

  1. Given that many existing enterprise search solutions are perceived as mediocre, what specific technological or UX innovations will differentiate your platform and provide a demonstrably superior experience for users in your chosen niche?
  2. How will you address the 'cold start' problem of training your AI natural language understanding model, especially within a specific industry or domain where labelled data might be scarce or proprietary?
  3. Considering the low engagement observed in similar product launches, what unique strategies will you employ to generate user interest, gather feedback, and build a community around your enterprise search platform?

Your are here

You're entering a crowded space for enterprise search platforms, a sector that falls into the 'Swamp' category. This means many have tried and most have delivered mediocre solutions. Our analysis of 20 similar products reveals low engagement (average of 3 comments), suggesting it's tough to capture user attention. The lack of strong positive 'use' or 'buy' signals further underscores this challenge; people aren't overwhelmingly expressing a desire to use or pay for similar products. Standing out will require a fundamentally different approach, given the existing solutions haven't captured hearts or wallets. Your focus should be on addressing underserved needs or pivoting towards adjacent problems within the enterprise data landscape. Before investing heavily, rigorously examine the failures of current solutions to pinpoint genuine opportunities.

Recommendations

  1. First, deeply investigate why existing enterprise search solutions haven't resonated with users. Don't just look at features; understand the underlying frustrations and unmet needs within organizations. Analyze the criticism from similar product launches; for example, many users complain about the poor native search on platforms like Confluence, Slack and Notion. Use this insight to identify gaps you can uniquely fill.
  2. If you proceed, target a very specific niche within the enterprise. General-purpose search solutions often fail. For example, you could focus on search specifically for legal documents, research data within pharmaceutical companies, or even internal communication archives. Being laser-focused allows you to tailor the AI's natural language understanding to the specific jargon and context of that niche, offering a superior experience.
  3. Consider providing AI-enhanced tools to existing enterprise search providers instead of building a whole platform from scratch. Many companies already have search infrastructure in place (e.g. ElasticSearch, Algolia, etc.). Your AI-powered natural language understanding could be integrated as a 'plugin' to improve the accuracy and relevance of results within these existing systems. This reduces the barrier to entry and allows you to leverage established distribution channels.
  4. Explore adjacent problems that are closely related to enterprise search but may be more promising. For example, instead of just search, focus on knowledge discovery or automated summarization of internal documents. You could build a tool that proactively surfaces relevant information to employees based on their current projects or roles, eliminating the need for them to even search in the first place.
  5. Before committing significant resources, create a simple, no-code prototype to validate your core assumptions. Focus on a narrow use case and demonstrate how your AI-powered search delivers significantly better results than existing solutions. Get feedback from your target niche and iterate rapidly based on their input. This minimizes risk and ensures you're building something people actually want.
  6. Carefully consider your go-to-market strategy. Enterprise sales can be complex and time-consuming. Explore alternative channels such as partnerships with existing software vendors, content marketing targeted at your niche, or even building a free, open-source version of your tool to gain traction and build a community. The comments on similar product launches show the poor website design can be detrimental; don't let design shortcomings sink the product.

Questions

  1. Given that many existing enterprise search solutions are perceived as mediocre, what specific technological or UX innovations will differentiate your platform and provide a demonstrably superior experience for users in your chosen niche?
  2. How will you address the 'cold start' problem of training your AI natural language understanding model, especially within a specific industry or domain where labelled data might be scarce or proprietary?
  3. Considering the low engagement observed in similar product launches, what unique strategies will you employ to generate user interest, gather feedback, and build a community around your enterprise search platform?

  • Confidence: High
    • Number of similar products: 20
  • Engagement: Low
    • Average number of comments: 3
  • Net use signal: 10.9%
    • Positive use signal: 12.3%
    • Negative use signal: 1.4%
  • Net buy signal: -1.4%
    • Positive buy signal: 0.0%
    • Negative buy signal: 1.4%

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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