(And You Need It More Than You Realize)
An ideal enterprise search system (also known as "cognitive search" or "insight engines") quickly retrieves documents, conversations, and other content relevant to the searcher’s question, across all critical internal knowledge systems. Historically, "What is the PageRank equivalent at work?" has not been well answered. Challenges that historically prevented such a system from actually existing include:
- Building integrations for every knowledge system
- Representing and enforcing fine-grained permissions as they evolve, in real time
- Understanding who the user is, and therefore what knowledge is most relevant to them
But things have changed.
With the dominance of cloud-based collaboration products, modern APIs built into those systems, and universal identity across systems, it's finally possible to solve enterprise search effectively.
Key Takeaways
- Modern enterprise search software has finally overcome the technical limitations that plagued earlier solutions like Google Search Appliance, thanks to cloud-based APIs, unified identity management, and advanced data indexing capabilities
- The best enterprise search engine combines hybrid search technology with real-time collaboration graphs to deliver truly relevant results based on your relationships, projects, and permissions
- Enterprise search tools have evolved from simple keyword matching to sophisticated platforms leveraging AI search, generative search, and cognitive search capabilities
- Security and permissions are no longer obstacles – today's enterprise search platforms like Atolio respect fine-grained permissions across all connected systems, while keeping your data in your cloud
- The market has matured with proven solutions that dramatically improve productivity by connecting siloed knowledge across tools like Slack, Microsoft Teams, Salesforce, and dozens of other connectors
The Last Quarter-Century in Workplace Search
Google's 1999 post-raise business plan focused on three revenue streams:
- Showing ads
- Licensing search technology
- Enterprise search
That last piece ultimately shipped as Google Search Appliance, a product which floundered for years before finally being retired in 2016. Despite a tremendous amount of effort, Google failed to overcome significant obstacles and solve key technical challenges:
- A lot of the content lived in files on people's hard drives (or in the best case, on file shares)
- Usernames differed across disparate systems
- Most systems had no APIs for getting data out
- Most systems also had limited or no ability to export permission data
Even in theory, if an organization successfully deployed agents to collect the data, they couldn't really figure out which information was most relevant to individual employees, what that employee should even have access to, and, most importantly, "What results should go on page 1?"
Google certainly wasn't the only player to try and fail in this space. Millions of dollars have been spent on fundamentally broken solutions.
Enterprise Search Tools: The Evolution of the Market
The enterprise search market has witnessed decades of attempts to solve the fundamental challenge of workplace information discovery. Early platforms focused primarily on document indexing, unaware of the collaborative nature of modern work. Traditional search software relied on basic keyword matching and TF-IDF algorithms that worked reasonably well for web search but failed to capture the nuanced relationships between people, projects, and information in enterprise environments.
Major technology providers, including search capabilities within Microsoft’s Copilot and Google’s Gemini Enterprise, and specialized vendors like Atolio, Coveo and Glean, have invested heavily in this space. According to Gartner research, organizations continue to struggle with information silos despite these investments, with employees spending up to 20% of their time searching for information across disconnected systems.
The challenge wasn't just technical – it was also about understanding what makes enterprise search fundamentally different from web search. While web search can rely on link analysis and popularity signals, enterprise search requires understanding organizational hierarchies, project relationships, security boundaries, and individual work contexts.
The 2020s: A New Era for the Enterprise Search Engine
A confluence of several factors has vastly improved today's landscape for enterprise search:
- Organizations have coalesced on a set of best-of-breed collaboration tools with modern APIs (Slack, Google Workspace, Teams, etc.)
- Common identity across systems (Okta, Azure AD, etc.) has made it practical to understand identity across diverse systems deeply
- Modern hybrid search engines (such as Vespa) offer robust scalability, sophisticated search, and machine learning, which older technologies (such as Elastic or Solr) lacked
- Advancements in GenAI and Large Language Models (LLMs) have enabled semantic understanding and natural language synthesis, moving beyond keyword matching to provide direct yet highly relevant answers, contextually-aware summaries, and instant synthesis of information across multiple sources.
At the same time, some of those same macro trends have dramatically increased the need for great enterprise search:
- The explosion of SaaS tools has tended to silo knowledge and make it increasingly painful to find helpful information at work. Employees frequently run the exact search across multiple knowledge silos.
- The dramatic shift to remote/hybrid work makes it difficult for employees to visualize social connections and intuitively understand "who knows what" in organizations
So now we have the necessary pieces in place, combined with a burning need to solve the core problem: "How do I put the thing the user wants on the first page of their results?"
Data Indexing and Search Capabilities: The Technical Foundation
Modern enterprise search platforms must excel at data indexing across diverse sources while maintaining real-time accuracy. Unlike Elasticsearch, which requires significant technical expertise to configure and maintain, the best enterprise search engines abstract away this complexity while delivering superior results.
The technical architecture of effective enterprise search software includes several critical components:
Unified Data Indexing: Rather than forcing users to search within individual applications, modern platforms index content from all connected sources into a unified search index. This requires sophisticated connectors that can handle the unique data structures, APIs, and update patterns of each source system.
Permission-Aware Search: One of the most challenging aspects of enterprise search is ensuring that search results respect the complex permission models of underlying systems. When comparing enterprise search platforms, this capability is often overlooked but absolutely critical for security and compliance. Rather than caching permissions separately, Atolio validates them in real time against source systems, ensuring users only see content they're authorized to access.
Hybrid Search Technology: The most effective search platforms combine multiple search techniques. Traditional keyword search (like that used in Elasticsearch) misses semantic relationships between concepts. Pure vector search can miss exact matches. The best enterprise search engine uses a hybrid approach that combines keyword matching, semantic understanding, and graph-based relevance to deliver comprehensive results.
Elastic Search vs. Purpose-Built Enterprise Search
While Elasticsearch (often referred to as Elastic) has become popular for custom search implementations, it presents significant challenges for enterprise search use cases. Elasticsearch is a powerful search engine, but it requires extensive development work to build enterprise search features on top of it.
Organizations attempting to build their own enterprise search solution on Elasticsearch face several obstacles:
- Development Overhead: Building connectors, permission systems, and relevance models requires substantial engineering resources
- Maintenance Burden: Elasticsearch clusters require ongoing tuning, monitoring, and optimization
- Relevance Challenges: Elasticsearch provides search capabilities but doesn't include a built-in understanding of organizational context, collaboration patterns, or enterprise-specific relevance signals
- Security Complexity: Implementing fine-grained, real-time permission checking across diverse source systems is extremely difficult
Purpose-built enterprise search tools like Atolio provide these capabilities out of the box, allowing organizations to focus on using search rather than building and maintaining it. When you compare Atolio to a custom Elasticsearch implementation, the difference in time-to-value and total cost of ownership is dramatic.
Cognitive Search and AI Search: The Intelligence Layer
Cognitive search represents the evolution beyond traditional keyword search to systems that understand intent, context, and meaning. AI search capabilities enable enterprise search platforms to deliver increasingly intelligent results through several mechanisms:
Natural Language Understanding: Modern enterprise search software can interpret natural language queries, recognizing that "Q4 sales deck" and "revenue presentation from last quarter" refer to the duplicate content. Generative search takes this further by synthesizing answers from multiple sources rather than just returning a list of documents.
Collaborative Intelligence: Atolio's breakthrough innovation is understanding that relevance in enterprise search is fundamentally social. The platform builds a real-time collaboration graph that understands who works with whom, which projects teams are focused on, and how information flows through the organization. This social signal provides dramatically better relevance than text-only approaches.
Continuous Learning: The best enterprise search engines learn from usage patterns, improving relevance over time. When users select specific results, spend time on particular documents, or collaborate on specific content, these signals feed back into the relevance model.
Personalized Results: AI search enables personalization at scale. Rather than showing the same results to everyone, modern platforms understand individual context – your role, your projects, your team, and your recent focus areas – to surface the most relevant information for you specifically.
Enterprise Search Platform Features: What to Look For
When evaluating enterprise search platforms, several key features distinguish truly effective solutions from legacy approaches:
Comprehensive Connectors: The platform should offer pre-built, permission-aware connectors to all your critical systems – not just file storage, but also Slack, Microsoft Teams, Salesforce, Jira, Confluence, GitHub, and dozens of other tools where knowledge lives. In addition to permission-aware connectors (versus federated search connectors that only let you search over internally public content), Atolio also provides an SDK to enable building custom connectors to index home-grown and legacy systems.
Deployment Flexibility: Security-conscious organizations need control over where their data lives. While some vendors, like Microsoft, insist on their own cloud infrastructure, Atolio deploys entirely within your Azure subscription or Google Cloud environment, ensuring your data never leaves your control.
Real-Time Updates: Search results should reflect the current state of your systems, not a stale snapshot from hours or days ago. Atolio's architecture enables real-time indexing and permission checking, ensuring you always see accurate, up-to-date information.
Intuitive User Experience: Search should be effortless. The best enterprise search engines provide suggestions before you even type a query, offer intelligent filtering and faceting, and present results in context with related people, projects, and documents.
API and Integration Options: While the primary interface might be a standalone application or browser extension, enterprise search platforms should also expose APIs that enable search capabilities to be embedded in other tools and workflows.
Comparing Enterprise Search Solutions: Atolio vs. The Market
The enterprise search market includes several established players, each with different strengths and limitations. Let's compare the leading options:
Atolio vs. Microsoft Copilot: Microsoft Copilot and its search capabilities are tightly integrated with Microsoft 365 applications, which is both its strength and limitation. While it works reasonably well for organizations exclusively using Microsoft tools (though challenges with strength of search capabilities and relevance of results are common), it struggles with the diverse ecosystem of SaaS tools most companies actually use.
Atolio vs. Coveo: Coveo has been in the enterprise search market for years, primarily focusing on customer-facing e-commerce and knowledge base search experiences. Their platform is powerful but complex, typically requiring significant implementation services and ongoing customization. Coveo's pricing model can also be prohibitively expensive for mid-market organizations. Atolio provides comparable functionality with dramatically simpler deployment and more transparent pricing, making enterprise search accessible to organizations of all sizes.
Atolio vs. Glean: Glean represents a newer generation of enterprise search tools that recognized many of the same problems Atolio set out to solve. However, Glean's architecture still relies on its own hosted infrastructure, meaning your index lives in your cloud (if you choose to host yourself) but the front end lives in Glean's cloud. For heavily-regulated industries or security-conscious organizations, this poses risk. Atolio's unique architecture keeps all your data in your own cloud environment while still delivering intelligent, real-time search results.
Atolio vs. Custom Elasticsearch Solutions: Some organizations have attempted to build custom enterprise search solutions on top of Elasticsearch. While Elasticsearch provides powerful search capabilities, creating a complete enterprise search platform requires solving numerous challenges: connector development, permission modeling, relevance tuning, user interface design, and ongoing maintenance. Organizations that compare the total cost and timeline of custom development versus deploying Atolio consistently find that purpose-built solutions deliver better results faster, and at lower total cost.
Why Atolio Offers the Best Enterprise Search Engine
Atolio's approach addresses the fundamental limitations that have plagued enterprise search for decades. Unlike competitors that treat enterprise search as an information retrieval problem, Atolio recognizes that relevance in the workplace is primarily social. The platform's core innovation is its real-time collaboration graph, which understands:
- Who you work with – Your immediate team, frequent collaborators, and extended network
- What you work on – Your active projects, assigned tasks, and areas of focus
- How information flows – Which documents, conversations, and resources are connected to your work
This collaborative intelligence transforms search results from a generic list of keyword matches to a personalized knowledge feed tailored to your specific context and needs.
Security Without Compromise: Atolio's architecture deploys entirely within your cloud environment (Azure, Google Cloud, or AWS), ensuring your sensitive data never leaves your control. Unlike SaaS-only competitors, Atolio gives you complete control over data residency, encryption, and access controls while still delivering powerful search capabilities.
Proven Results: Organizations using Atolio report dramatic productivity improvements. In a recent study with Cengage, sales teams reduced the time spent searching for information by 15% – a 60% reduction in search inefficiency, which translates to a 17% increase in team productivity. As Cengage CEO Michael Hansen noted on their earnings call: "We are investing in automation to drive internal efficiencies like an AI-powered search with products such as Atolio to improve the availability of product and customer information to Sales and Marketing teams."
Relevance Today: The Breakthrough Insight
All sorts of attempts to solve search relevance at work—TF-IDF, BM25, the semantic web, PageRank, and even modern semantic search—have been largely unsuccessful. Those approaches tried to solve the problem from an "information-only perspective": take all the text, put it in an index, and use the text data in isolation to create a relevance model across all the content.
The biggest problem with this approach is that it ignores the clearest signal that a user might care about a given document or conversation: that it involves them or closely connected coworkers! It turns out that enterprise content creation and distribution closely resembles social networks—so what if we apply social network techniques to enterprise search?
Globally important content (all-hands meeting notes, crucial company-wide policy documents) needs to be highly visible when relevant, in the same way that viral, popular social posts do. But most of the time, people really care more about things directly relevant to them personally:
- their own stuff
- their team's stuff
- stuff directly related to their day-to-day work
This breakthrough insight (marrying a dynamic social graph to modern hybrid search techniques) is the final piece of the puzzle, turning this immense challenge into a tractable set of problems.
What should be shown on the home page, before the user even types a search query? The social media model suggests a crisp answer: show the results most relevant to the user.
The Future of Enterprise Search Software
The evolution of enterprise search continues to accelerate. Generative search capabilities will enable even more sophisticated ways to interact with organizational knowledge – asking complex questions that require synthesis across multiple sources, generating summaries of lengthy discussions, and even proactively surfacing relevant information before you know to search for it.
The integration of generative AI with enterprise search presents both opportunities and challenges. While large language models can generate impressively fluent responses, they also risk hallucination and misinformation. The best enterprise search engines will combine the fluency of generative AI with the accuracy of retrieval-based search, always grounding generated responses in actual organizational content with proper citations and source links.
Atolio's roadmap includes expanded generative search features while maintaining the platform's core commitment to accuracy, security, and permission-aware results. The goal is not to replace human decision-making, but to dramatically accelerate access to the information needed for those decisions.
Frequently Asked Questions
What makes enterprise search different from web search like Google?
Enterprise search faces unique challenges that don't exist in web search. While Google can rely on link analysis and popularity signals, enterprise search must understand complex permission models, organizational relationships, and individual work contexts. The best enterprise search engine needs to answer not just "what documents match these keywords?" but "what information is relevant to this specific person's work right now?" Additionally, enterprise search platforms must maintain strict security, respecting the fine-grained access controls of underlying systems while still delivering fast, comprehensive results.
How does Atolio compare to using Microsoft Copilot or Gemini Enterprise?
Microsoft Copilot and Google’s Gemini Enterprise are tightly coupled to their respective ecosystems. While they work reasonably well if you exclusively use Microsoft 365 or Google Workspace, most organizations use a diverse set of tools: Slack or Teams, Salesforce, Jira, GitHub, Confluence, and dozens of others. Atolio connects to all of these systems through comprehensive connectors, providing a unified search across your entire knowledge base. More importantly, Atolio's collaboration graph technology delivers superior relevance by understanding organizational relationships and work patterns, not just keyword matching. Finally, Atolio deploys in your own cloud environment, giving you complete control over your data – something neither Microsoft nor Google offers.
Is Elasticsearch sufficient for enterprise search, or do I need a specialized platform?
While Elasticsearch is a powerful search engine, it's a low-level infrastructure component rather than a complete enterprise search solution. Building enterprise search on Elasticsearch requires developing connectors to all your data sources, implementing sophisticated permission checks, creating relevance models that understand organizational context, designing user interfaces, and maintaining the entire system on an ongoing basis. Organizations that compare the total cost and timeline of custom Elasticsearch development versus deploying a purpose-built solution like Atolio consistently find that specialized platforms deliver better results faster at lower total cost. Atolio provides all the enterprise search capabilities organizations need out of the box, allowing your team to focus on using search rather than building and maintaining it.
What should I look for when comparing enterprise search tools?
Key evaluation criteria include: comprehensive connectors to all your data sources (not just file storage but also communication tools, project management systems, CRM, code repositories, etc.); real-time permission checking that respects the security models of underlying systems; deployment flexibility allowing you to keep data in your own cloud environment; intelligent relevance that understands organizational context and relationships; ease of deployment and ongoing maintenance; and proven results with measurable ROI. According to Gartner research and market analysis, organizations should be wary of vendors that require data to live in their cloud, offer limited connector libraries, or rely solely on keyword matching without understanding collaborative context. Atolio excels across all these dimensions, which is why it offers the best enterprise search engine for organizations serious about knowledge management.
How do cognitive search and AI search improve upon traditional keyword search?
Traditional keyword search suffers from several limitations: it misses relevant results that use different terminology, it can't understand query intent, and it treats all users the same regardless of their role or context. Cognitive search and AI search use natural language understanding to interpret query intent, semantic understanding to find conceptually related content even when keywords don't match, and personalization to deliver results relevant to each user's specific work context. Atolio's implementation of cognitive search goes further by incorporating collaboration graph intelligence, understanding that you're most likely interested in content involving you, your team, and your current projects. This social signal provides dramatically better relevance than text-only approaches. The platform's AI search capabilities continue learning from usage patterns, improving results over time while maintaining the security and permission controls enterprises require.
Conclusion: Enterprise Search Has Finally Arrived
It's safe to say that everyone is looking forward to a world where the wisdom that a company knows is useful in the hands of everyone (who has access to it). After all, as HP CEO Lew Platt famously said:
If HP knew what HP knows, we'd be three times more productive.
For decades, this vision remained elusive despite massive investments by technology giants and specialized vendors. The technical obstacles seemed insurmountable: fragmented data sources, inconsistent identities, lack of APIs, complex permissions, and most fundamentally, the challenge of determining relevance in organizational contexts.
Today, those obstacles have been overcome. The convergence of cloud-based collaboration tools with modern APIs, unified identity systems, and breakthrough approaches to relevance modeling has finally made effective enterprise search possible. But not all solutions are created equal.
The market includes legacy vendors still using outdated approaches, infrastructure components like Elasticsearch that require extensive development, and SaaS-only platforms that force you to replicate sensitive data to vendor clouds. Gartner research and market analyses consistently show that organizations need purpose-built enterprise search platforms that combine comprehensive connectors, intelligent relevance, robust security, and deployment flexibility.
Atolio represents the culmination of decades of enterprise search evolution. By combining modern hybrid search technology with collaboration graph intelligence, deploying entirely in your cloud environment, and respecting fine-grained permissions across all connected systems, Atolio has solved the challenges that defeated previous generations of enterprise search tools.
The result is a platform that finally delivers on the promise of enterprise search: putting the correct information in front of the right person at the right time, securely and efficiently. Organizations using Atolio report dramatic improvements in productivity, faster onboarding, better decision-making, and increased employee satisfaction.
The future of work depends on effective knowledge sharing. As organizations grow more complex, tools proliferate, and remote work becomes permanent, the need for excellent enterprise search has never been greater. With Atolio, that future is here.
To learn more about how Atolio can power enterprise search within your organization – while maintaining complete data sovereignty and full permissions – visit our website or book a demo.
David Lanstein is co-founder and CEO at Atolio - workplace search for the modern company.
Atolio is the first good enterprise search tool I've seen, among dozens of failed attempts over two decades. By taking a fresh approach using the collaborative graph inside organizations, Atolio is finally doing for enterprise search what Google did for the web: finding what you want.
Aaron Rankin
Co-founder and CTO, Sprout Social
