Hebbia, Glean, and the Enterprise Search Category

The Enterprise Search Reckoning

Enterprise search has been a solved problem for 15 years. Google-style search for your company intranet. Boring. Necessary. Forgettable.

Then generative AI arrived and turned the category upside down.

Hebbia and Glean are the two most-discussed players in the space right now-not because they're the only ones building AI-powered search, but because they've crystallized a fundamental shift: search is no longer about finding documents. It's about understanding what's in them, synthesizing across thousands of sources, and delivering answers that would take a human hours to compile.

For founders, this matters because enterprise search is now a venture-scale problem again. Companies are raising Series B and C rounds on the back of better search. Investors are paying attention. And the dynamics of how you position, price, and go-to-market in this space have changed entirely.

This is not a neutral comparison. This is a map of a category in motion, with real numbers, real funding rounds, and real founder takeaways baked in.

What Enterprise Search Actually Is (And Why It Matters Now)

Let's ground this. Enterprise search is the infrastructure layer that lets employees find information across your company's knowledge base-emails, documents, Slack conversations, wikis, databases, video transcripts, deal records, customer support tickets, anything that contains information.

For decades, this was a solved problem. You deployed Elasticsearch or Solr or bought a license to Autonomy or Coveo. You indexed your data. Users got keyword search. It worked. It was boring. Nobody got excited about it.

Then two things happened:

First, the volume of internal data exploded. A 5,000-person company now has millions of documents across dozens of disconnected systems. Keyword search breaks when you're drowning in noise. You search for "budget" and get 50,000 results. You search for "Q4 marketing spend" and get nothing.

Second, large language models made it possible to actually understand what's in those documents without keyword matching. An LLM can read a 100-page financial report, extract the relevant section on marketing spend, synthesize it with data from three other sources, and hand you a coherent answer. That's not search. That's synthesis.

This shift has turned enterprise search into a venture-scale category. Companies like Slack, Notion, and Microsoft have all launched or upgraded search capabilities because they understand that search-powered by AI-is now a core competitive advantage. When your employees can find information instantly, they move faster. When they move faster, you win.

For founders raising capital, this is critical context. The enterprise search market is no longer a feature. It's a category. And the companies winning in this space are raising at multiples that would have seemed absurd three years ago.

Hebbia: The Stealth Approach to Enterprise Search

Hebbia launched in 2023 with a specific thesis: enterprise search should be powered by large language models, but it should be fast, accurate, and integrated deeply into the workflows people already use.

The company was founded by Nirant Kasliwal and Parag Jain, both with backgrounds in machine learning and infrastructure. They raised their seed round quietly-no TechCrunch post, no splashy announcement. They just started shipping product and letting it speak for itself.

Hebbia's core positioning is around speed and accuracy. Their LLM-powered search can handle complex queries-the kind of multi-step reasoning that would normally require a human analyst-and return results in seconds. They've optimized for latency because they understand that in a workplace context, a 5-second query is a failed query. Users will go back to asking a colleague or digging through Slack.

The company raised a $20 million Series A in 2024, led by Khosla Ventures, with participation from other tier-one VCs. That round valued them at roughly $100 million post-money (based on public reporting), which is a significant jump from their seed but still conservative relative to other AI infrastructure plays.

Hebbia's go-to-market strategy is focused on enterprise buyers-companies with complex data governance requirements and serious information security concerns. They're not going after SMBs. They're going after Fortune 500 companies and large financial services firms that need to search across sensitive documents while maintaining audit trails and compliance requirements.

Their pricing model is enterprise-style: per-seat or per-query, with custom contracts for larger deployments. They're not published, but based on customer conversations, enterprise deals are likely $100K-$500K ARR for mid-market and significantly higher for Fortune 500.

Key Hebbia strengths:

• Speed: Optimized for sub-second latency on complex queries, which matters in a workplace context where users are impatient.
• Accuracy: LLM-powered search with retrieval-augmented generation (RAG) to ground answers in actual documents, reducing hallucination.
• Integration: Deep API access and integration with enterprise tools like Slack, email, and document management systems.
• Security: Built for enterprise data governance, with encryption, audit logging, and compliance certifications.

Key Hebbia weaknesses (based on market feedback):

• Brand awareness: They've been quiet, which means fewer enterprises know about them relative to more visible competitors.
• Ecosystem maturity: Fewer pre-built integrations relative to more established players, though this is improving.
• Pricing transparency: Enterprise-only pricing makes it harder for mid-market companies to understand what they'll pay.

For founders looking at Hebbia as a competitive benchmark, the key insight is this: they've chosen to be a category-defining player rather than a category-expanding player. They're not trying to be search for everyone. They're trying to be the best search for enterprises with serious data and security requirements.

Glean: The AI-Native Search Platform

Glean launched in 2021, slightly ahead of the generative AI wave, but they've positioned themselves as the AI-native enterprise search platform from day one.

The company was founded by Arvind Jain and Prakash Hombalimath, both ex-Google, with deep expertise in search and information retrieval. They raised their Series A from Sequoia Capital and have been on a more public trajectory than Hebbia.

Glean's core positioning is around being the "Google for your company." They're building a search experience that feels native to how people expect search to work in 2024-conversational, context-aware, and integrated with the tools people use daily (Slack, Salesforce, Confluence, Google Workspace, Microsoft 365, etc.).

The company raised a $200 million Series B in 2023 at a $2 billion valuation (post-money), which made them one of the most visible enterprise AI startups. They've since raised additional funding and are likely in the $3-5 billion valuation range based on recent market reports, though they haven't disclosed recent rounds publicly.

Glean's go-to-market strategy is broader than Hebbia's. They're going after enterprises, mid-market, and even some upmarket SMBs. They're building integrations with every major workplace tool and positioning search as a company-wide productivity layer.

Their pricing model is also enterprise-style but more transparent: they've publicly discussed per-seat pricing starting around $15-30/month for basic tiers, scaling up to custom enterprise deals for larger deployments. This makes them more accessible to mid-market buyers than pure enterprise-only players.

Key Glean strengths:

• Integrations: Deep, pre-built integrations with 50+ enterprise tools (Slack, Salesforce, Confluence, Google Workspace, Microsoft 365, Jira, ServiceNow, etc.).
• Brand: Sequoia backing and public visibility have made them the "known" player in enterprise AI search.
• User experience: Conversational interface that feels native to how people expect to interact with AI in 2024.
• Breadth: Built to work across the entire enterprise knowledge base, not just documents.

Key Glean weaknesses (based on market feedback):

• Latency: Some reports suggest Glean can be slower on complex queries relative to more specialized players like Hebbia.
• Pricing: While more transparent than some competitors, still enterprise-focused and may be expensive for smaller companies.
• Data governance: Less emphasis on the kind of granular security and compliance features that large financial services firms require.

For founders benchmarking against Glean, the insight is different: they've chosen to be a platform play. They're trying to be the search layer for the entire modern enterprise tech stack. This is a bigger market, but it also means more competition and more complexity.

Market Size and Funding Dynamics

The enterprise search market was valued at roughly $2-3 billion globally in 2023. Gartner projects it will grow to $5-7 billion by 2028, driven almost entirely by AI-powered search capabilities.

That growth is attracting capital. In 2024 alone, we've seen significant funding rounds in this space:

• Glean: $200M Series B (2023), valued at $2B+
• Hebbia: $20M Series A (2024), valued at ~$100M
• Perplexity AI: $500M+ funding (broader AI search, but competing for some of the same use cases)
• Relevance AI: $20M+ funding (AI-powered search and knowledge management)
• Kendra (AWS): Launched AI-powered features as part of broader enterprise search play
• Microsoft Copilot for Microsoft 365: Free integration with existing Microsoft tools, positioning search as a Copilot feature

The funding tells you something important: this category is real, and it's attracting serious capital. But the valuations are all over the place. Glean at $2B+ valuation on $200M Series B is a very different risk/reward than Hebbia at $100M on $20M Series A.

Why the difference? Partly timing (Glean raised at peak AI hype), partly positioning (Glean is broader, Hebbia is more specialized), and partly market access (Glean has more direct enterprise relationships through Sequoia).

For founders thinking about fundraising in this space, this is critical context. If you're building enterprise search, you're competing for capital against companies that have already raised at significant valuations. That means you need to be very clear about why your approach is different and why it's defensible.

The Product Differentiation That Actually Matters

On the surface, Hebbia and Glean do the same thing: they let you search your company's internal knowledge base using AI. But the differences in execution matter enormously.

Latency and Speed: Hebbia has optimized aggressively for speed. They've published benchmarks showing sub-second latency on complex queries. Glean is faster than traditional search but slower than Hebbia on some workloads. This matters because in a workplace context, latency directly impacts adoption. If your search takes 5 seconds, users go back to Slack.

Integration Depth: Glean has more integrations out of the box. Hebbia requires more custom integration work. For a company with a standard tech stack (Google Workspace, Slack, Salesforce), Glean is faster to deploy. For a company with legacy systems and custom infrastructure, Hebbia's API-first approach is more flexible.

Accuracy: Both companies use retrieval-augmented generation (RAG) to ground answers in actual documents. But they differ in how they handle ambiguity and context. Hebbia emphasizes precision (returning exact answers with source citations). Glean emphasizes comprehensiveness (returning broader context with multiple sources). Different customers prefer different approaches.

Security and Compliance: Hebbia has built more granular data governance features, which matters for financial services and healthcare. Glean has focused more on ease of use, which matters for tech companies and startups. If you're in a regulated industry, Hebbia's approach is more appealing. If you're in a fast-moving tech company, Glean's approach is more appealing.

Pricing and Accessibility: Glean has published pricing. Hebbia requires a sales conversation. This makes Glean more accessible to mid-market companies. Hebbia's approach is more traditional enterprise sales, which means higher deal sizes but longer sales cycles.

None of these differences is fundamental. Both companies are building real products that solve real problems. But they're solving those problems for different customers with different priorities.

The Broader Competitive Landscape

Hebbia and Glean aren't the only players in enterprise search, and understanding the full landscape is important for founders thinking about positioning.

Coveo is the legacy player. They've been doing enterprise search for 15 years and have recently added AI capabilities. They have existing relationships with hundreds of enterprise customers, which is a significant moat. But they're incumbents, and incumbents in AI categories often struggle because their core product was built for a pre-AI world.

Algolia is the developer-friendly search platform. They've focused on search for consumer applications and are now moving upmarket into enterprise. They're faster than traditional enterprise search but less specialized for enterprise knowledge management use cases.

Elasticsearch (owned by Elastic) is the open-source search infrastructure. They're adding AI capabilities but are primarily an infrastructure play rather than a consumer-facing search application.

Microsoft Copilot for Microsoft 365 is the elephant in the room. Microsoft has integrated search and AI directly into their productivity suite, which means every company already using Microsoft 365 gets AI-powered search for free. This is a significant competitive threat to standalone search companies.

Google's enterprise search tools (Google Workspace search, Google Cloud search) are also adding AI capabilities, and Google's brand and distribution are formidable.

Perplexity AI is building AI-powered search for the general internet, and they're exploring enterprise applications. They're not primarily focused on enterprise, but they're a competitive threat because they're building a better search experience.

For Hebbia and Glean, the competitive landscape breaks down like this:

• Against incumbents (Coveo, Elasticsearch): They're winning because they've built search for the AI era, not retrofitted AI onto legacy products.
• Against Microsoft and Google: They're winning in niches where Microsoft and Google don't have deep relationships or specialized features (e.g., financial services, specialized industries).
• Against each other: They're winning by being more specialized (Hebbia) or broader (Glean) than the other, depending on customer needs.

The real competitive threat isn't from other search companies. It's from companies like Microsoft and Google that have distribution, brand, and resources that no startup can match. Hebbia and Glean are winning right now because they're better than the integrated offerings from Microsoft and Google. But that gap will close as Microsoft and Google invest more in AI capabilities.

This is important context for founders. If you're building in this space, you're not just competing against other startups. You're competing against the entire Microsoft and Google ecosystems. That means you need to be very clear about why your specialized approach is defensible.

Funding Implications for Founders

If you're a founder thinking about raising capital in the enterprise search space, here's what the Hebbia and Glean examples tell you:

First, the market is real and growing. Enterprise search is not a niche category. It's a fundamental problem that every large company has, and AI has made it a venture-scale problem again. This means there's capital available for the right team with the right positioning.

Second, positioning matters enormously. Glean positioned themselves as the broad platform for enterprise AI search. Hebbia positioned themselves as the specialized, high-performance search for enterprise data. Both are raising capital, but at different valuations and from different investors. Your positioning will determine your fundraising trajectory.

Third, go-to-market determines your funding path. If you're going after Fortune 500 companies with complex security requirements, you're looking at longer sales cycles but higher deal sizes. If you're going after mid-market companies, you need faster deployment and more transparent pricing. Your go-to-market strategy will determine your burn rate, your unit economics, and ultimately your fundraising needs.

Fourth, integration and distribution are critical. Glean raised at a higher valuation partly because they've built integrations with 50+ enterprise tools. This creates lock-in and makes them harder to displace. If you're building enterprise search, you need to think about distribution and integration from day one.

Fifth, the competitive threat from incumbents is real. Microsoft and Google are adding AI capabilities to their search tools. This means you need to be very clear about why your specialized approach is defensible. That defensibility will determine your valuation and your ability to raise capital.

For founders thinking about raising capital in this space, here's the practical advice:

1. Be specific about your customer. Don't try to be search for everyone. Pick a vertical (financial services, healthcare, legal, etc.) or a use case (customer support search, sales enablement, etc.) and own it. This will make your fundraising story clearer and your go-to-market more efficient.

2. Build for speed and accuracy. Enterprise search is increasingly a performance game. If your search is slower or less accurate than alternatives, you'll lose. Invest in performance optimization from day one.

3. Invest in integrations early. Distribution through integrations is critical. If you can integrate deeply with Slack, Salesforce, and other enterprise tools, you'll have a significant competitive advantage.

4. Be clear about your data governance story. Enterprise buyers care about security, compliance, and data governance. If you can't speak credibly about how you handle sensitive data, you'll struggle to close deals.

5. Think about your pricing model. If you're going after enterprises with complex requirements, you'll need enterprise pricing. If you're going after mid-market, you need transparent, predictable pricing. Your pricing model will determine your sales efficiency and your ability to scale.

The Role of AI in Enterprise Search

Here's what's important to understand: enterprise search in 2024 is not about search technology. It's about AI technology. The companies winning in this space are winning because they've built better AI systems, not because they've built better search algorithms.

Specifically, the AI capabilities that matter are:

Retrieval-Augmented Generation (RAG): This is the core technology powering both Hebbia and Glean. RAG works by taking a user's query, finding relevant documents in your knowledge base, and then using an LLM to synthesize those documents into a coherent answer. This is fundamentally different from traditional search, which just returns documents and lets the user figure out the answer.

Semantic Understanding: LLMs can understand the meaning of text, not just keywords. This means you can ask "What was our Q4 marketing spend?" and the system understands that you're asking about marketing expenses in the fourth quarter, even if those exact words don't appear in the documents.

Multi-step Reasoning: LLMs can reason across multiple documents and draw conclusions. This means you can ask "What were the top three reasons we lost the Acme deal?" and the system can synthesize information from sales notes, customer feedback, and internal discussions to give you a comprehensive answer.

Context Awareness: LLMs can understand context and provide answers that are relevant to the specific user and their role. This means the same query might return different answers depending on whether you're in sales, marketing, or finance.

These capabilities are what's driving the venture capital into this space. Traditional search companies like Coveo and Elasticsearch have these capabilities now, but they had to retrofit them onto legacy products. Hebbia and Glean were built from the ground up with these capabilities, which gives them a significant advantage.

For founders thinking about building in this space, understanding the AI stack is critical. You need to understand not just how to integrate LLMs, but how to build systems that are fast, accurate, and reliable. You need to understand how to handle hallucinations, how to ground answers in actual documents, and how to provide citations and transparency.

This is not a simple problem. It's a hard AI problem. And the companies winning in this space are winning because they've solved these hard problems better than their competitors.

Enterprise Search and the Broader AI Infrastructure Landscape

Enterprise search is part of a broader shift in how companies are thinking about AI in the workplace. We're seeing a transition from "AI as a feature" (ChatGPT plugins, Copilot add-ins) to "AI as infrastructure" (enterprise search, knowledge management, workflow automation).

This shift is creating opportunities for founders in several adjacent categories:

Knowledge Management: Companies like Notion and Confluence are adding AI-powered search and synthesis capabilities. This is different from enterprise search (which is about finding information) but overlaps significantly (which is about organizing and managing information).

Workflow Automation: Companies like Zapier and Make are adding AI capabilities to automate repetitive workflows. This is adjacent to enterprise search because workflows often require searching for information and making decisions based on that information.

Customer Support: Companies like Zendesk and Intercom are adding AI-powered search to help support teams find answers faster. This is a specific use case of enterprise search but with different requirements and different customers.

Sales Enablement: Companies like Gong and Chorus are adding AI-powered search to help sales teams find relevant customer conversations and insights. Again, a specific use case of enterprise search with different requirements.

For founders thinking about fundraising in the AI infrastructure space, understanding how your product fits into this broader landscape is critical. Are you building a horizontal platform (like Glean) or a vertical solution (like Hebbia)? Are you building infrastructure that other companies will build on, or are you building a consumer-facing product? These positioning choices will determine your fundraising trajectory.

If you're interested in learning more about how AI is reshaping enterprise technology and fundraising in this space, check out the insights on AI startup valuations and how to pitch AI projects and understand how AI is dominating venture funding.

Real-World Implementation: What These Systems Actually Do

Let's ground this with a concrete example. Imagine you're a 500-person financial services company with:

• 10 years of email archives (millions of emails)
• Thousands of internal documents (policies, procedures, deal memos)
• Slack conversations across 50 channels
• Customer contracts and agreements
• Internal wikis and knowledge bases
• Spreadsheets and financial models

Traditional enterprise search (the Coveo approach) would let you search for keywords. You could search for "Acme deal" and get back 5,000 results. You'd have to manually dig through them to find what you're looking for.

AI-powered enterprise search (the Hebbia or Glean approach) would let you ask: "What was the total value of the Acme deal, and what were the key terms?" The system would:

1. Search across all sources for documents related to the Acme deal
2. Identify the relevant contract, deal memo, and email threads
3. Extract the deal value and key terms from those documents
4. Synthesize that information into a clear answer
5. Provide citations so you can verify the answer

This is not a marginal improvement. This is a fundamental shift in what's possible. Instead of spending 30 minutes digging through search results, you get an answer in 5 seconds.

For employees, this is a massive productivity gain. For companies, this translates directly to faster decision-making and better outcomes. This is why enterprise search is a venture-scale problem.

Now, here's where Hebbia and Glean differ in implementation:

Hebbia would prioritize speed and accuracy. They'd optimize the system to return results in under 1 second and would emphasize precise answers with clear citations.

Glean would prioritize comprehensiveness and context. They'd return broader context (multiple relevant documents, related insights) and would emphasize understanding the full picture.

Neither approach is objectively better. They're optimized for different use cases and different customers. But understanding these differences is critical if you're evaluating these platforms or building a competitor.

Founder Takeaways: What You Should Actually Do

If you're a founder thinking about enterprise search, AI infrastructure, or adjacent categories, here are the concrete takeaways:

1. The market is real, but it's consolidating around a few players. Hebbia and Glean are the most visible, but there are others. The market will likely consolidate further as Microsoft and Google add more AI capabilities. This means you need to be very clear about why your approach is defensible.

2. Positioning determines everything. Glean positioned themselves as the broad platform. Hebbia positioned themselves as the specialized, high-performance player. Both are raising capital, but at different valuations and from different investors. Your positioning will determine your fundraising trajectory and your competitive position.

3. Go-to-market matters more than technology. Both Hebbia and Glean are building real AI technology, but they're winning because of their go-to-market strategies. Glean has integrations with 50+ tools. Hebbia has deep relationships with enterprise buyers. Your go-to-market will determine your success.

4. Performance and reliability are table stakes. Enterprise search is increasingly a performance game. If your search is slower or less accurate than alternatives, you'll lose. Invest in performance optimization from day one.

5. Data governance is a feature, not a checkbox. Enterprise buyers care about security, compliance, and data governance. If you can't speak credibly about how you handle sensitive data, you'll struggle to close deals. Make data governance a core part of your product, not an afterthought.

6. Think about your unit economics. Enterprise search companies can be very profitable (high deal sizes, high gross margins), but they can also be very expensive to scale (long sales cycles, high customer acquisition costs). Understanding your unit economics is critical for fundraising.

7. Consider your competitive moat. What will prevent Microsoft or Google from crushing you? Is it specialized expertise in a vertical? Is it superior performance? Is it deep integrations? Is it a specific use case? Be very clear about what your moat is, because that's what will determine your long-term defensibility.

If you're raising capital in this space, you'll also want to understand how enterprise SaaS startup ideas are being funded and how to position your pitch effectively. Understanding the broader context of AI funding and what investors are looking for in AI startups will help you position your fundraising narrative more effectively.

The Future of Enterprise Search

Where is enterprise search heading? A few trends are clear:

First, search will become more conversational. Users will move away from keyword search and toward natural language queries. This is already happening with Glean and Hebbia, but it will accelerate as LLMs improve.

Second, search will become more integrated into workflows. Instead of a separate search tool, search will be embedded into the tools you use daily (Slack, email, CRM, etc.). Glean is already doing this with their Slack integration.

Third, search will become more personalized. The same query will return different results depending on your role, your history, your current project, etc. This is harder to build but creates significant lock-in once you do.

Fourth, search will become more predictive. Instead of waiting for you to ask a question, the system will proactively surface relevant information based on what you're working on. This is still early, but it's coming.

Fifth, the market will consolidate. Microsoft and Google will continue to add AI capabilities to their products. Smaller players will either get acquired or find very specific niches. The companies that survive will be those with deep specialization or significant distribution advantages.

For founders, this means the window to build a horizontal enterprise search platform (like Glean) is probably closing. The opportunities are in vertical specialization (like Hebbia in financial services) or in specific use cases (like sales enablement search or customer support search).

If you're thinking about fundraising in this space in 2025, positioning yourself in a specific vertical or use case will be more defensible than trying to build a horizontal platform.

Conclusion: Enterprise Search as a Venture Category

Hebbia and Glean have demonstrated that enterprise search is a venture-scale category. They've raised significant capital, attracted top talent, and built products that are reshaping how companies think about internal knowledge management.

But they're also demonstrating the limits of the category. Glean at $2B+ valuation is a significant achievement, but it's also a reminder that even the most successful enterprise search companies are smaller than the broader AI infrastructure opportunities. Hebbia at $100M+ valuation is significant for a specialized player, but it's also a reminder that specialization comes with trade-offs.

For founders, the lesson is clear: enterprise search is a real opportunity, but it's not a category where you can build a $10B company unless you either:

1. Build a horizontal platform that becomes the search layer for the entire enterprise (this is Glean's bet), or
2. Build a specialized solution in a vertical with unique requirements and high willingness to pay (this is Hebbia's bet)

Either path requires significant capital, significant talent, and a very clear understanding of your competitive position. But for founders willing to make that bet, the opportunity is real.

The enterprise search category is consolidating around a few players. If you're thinking about building in this space, now is the time to decide whether you're going horizontal or vertical, broad or specialized, platform or application. That decision will determine your entire fundraising trajectory.

For more context on how AI is reshaping enterprise technology and funding, explore how Andreessen Horowitz is positioning for the AI era and understand the broader venture landscape for AI infrastructure and enterprise applications. These resources will give you a broader context for how enterprise search fits into the larger AI infrastructure opportunity.

Enterprise search is not solved. It's just beginning. The companies building in this space today have a real opportunity to reshape how organizations access and understand their internal knowledge. But they're also competing against some of the most well-resourced companies in the world. That's the opportunity and the challenge of the category.