Glean Enterprise Search is an AI-based tool developed by Glean Technologies Inc. It integrates with numerous applications and systems within a company and allows users to access company data while respecting data governance models. Glean also ensures that employees only see information they have permission to access.
The tool is designed to solve the problem of scattered information across different platforms by creating a comprehensive search index that facilitates easy access to data, enhancing productivity and efficiency within organizations.
How does Glean work?
Glean search works by indexing and storing copies of content from connected systems. With this comprehensive, permissions-aware indexing, Glean functions as both a search engine and a conversational AI tool for enterprises. It uses RAG technology to sort through internal data, retrieve information, and present quick answers, similar to Google and ChatGPT for the business market.
Related: Easy guide to what is RAG in AI search
How does Glean’s indexed search work?
Glean’s indexed search operates by continuously crawling and indexing content from internal tools and applications, creating a centralized, searchable database of company information. This database reflects permissions and access rights, ensuring users can only access data they’re authorized to view.
Glean’s search allows users to retrieve information through pre-processed data quickly but comes with inherent challenges typical for indexing-based search.
What are Glean’s enterprise search common use cases?
1. General document discovery
Indexed AI search works well when information doesn’t change frequently, and everyone needs access to the same documents, such as:
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Employee handbooks.
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HR policies.
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Training materials.
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Process documentation.
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Company guidelines.
2. Basic information retrieval needs
Glean search works well when users search through past, non-critical communications, e.g., to reference past decisions or summarise archived discussions in:
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Old email threads.
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Past meeting notes.
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Historical project discussions.
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Internal announcements.
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Team updates.
3. When comprehensive coverage matters more than real-time accuracy
Glean’s enterprise search is commonly used for general research purposes or looking for patterns or trends. It works well when you’re not making immediate decisions based on data.
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Research projects.
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Background information gathering.
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Learning about past initiatives.
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Understanding historical context.
4. Environments with stable, simple permission structures
It works well when sensitive data is limited, there’s simple role-based access and standard team permissions. Environments like this include:
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Public company information.
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Widely shared documents.
As with any indexed search approach, inherent challenges exist.
Important note: These are usually NOT good use cases for indexed search:
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Critical business decisions requiring AI access to live data.
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Sensitive customer or financial information.
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Compliance-regulated data.
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Complex permission scenarios.
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Time-sensitive operations.
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High-value data assets you want to apply AI to.
For these more critical scenarios, you'd want an AI search tool like Qatalog that skips indexing data altogether to deliver value from day one.
What are Glean's AI search limitations?
1. Lengthy implementation and high costs
Glean’s AI search depends on building a comprehensive index of your company’s data, which can be time-consuming to set up and refine before delivering value. The process requires significant effort to integrate tools and organize data. Additionally, maintaining the index infrastructure incurs ongoing storage and operational costs. Solutions like Qatalog avoid indexing, providing faster deployment and cost efficiency by directly retrieving live data.
Related: Deep dive into real-time RAG (Qatalog) vs indexed RAG (i.e. Glean search)
2. Security considerations
When you create an index, you're essentially making a copy of your company's data in another location, which introduces security vulnerabilities:
- Hackers can target the index instead of the original data sources.
- Risk of permission drift: Sensitive data deleted from the source may still reside in the index.
- Additional compliance challenges for regulated industries managing indexed copies.
3. Data quality
Another challenge with Glean’s indexed data is that it can become outdated or lose critical context:
- The index may get out of sync with real-time data between updates.
- Contextual relationships between data points can be missed during the indexing process.
- Search results often include duplicate or inconsistent content, reducing trust in insights.
4. Performance
As your company's knowledge grows, maintaining search performance becomes challenging:
- Larger indexes require more storage and computing power, driving up costs.
- Over time, the system’s research performance and responsiveness can degrade, leading to poorer results.
5. AI reliability
AI can get confused when processing a large, mixed index:
- Responses may include outdated information or hallucinations, where the AI combines unrelated data.
- Domain-specific terminology can be misinterpreted, resulting in inaccurate answers or irrelevant results.
How does Qatalog RAG architecture differ from Glean search?
Qatalog isn't just an alternative to Glean search—it's an AI platform built for companies that have identified specific high-value data they want to use AI on. Rather than trying to index everything, Qatalog connects directly to your key business systems. Here’s how it works:
1. Instant setup
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No need to index or duplicate data.
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Minimal upfront costs.
2. Always fresh data
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Access information in real-time.
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No lag between updates and availability.
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Perfect for dynamic, frequently changing content.
3. Enhanced security
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No data duplication or storage.
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Leverage existing permission systems.
- No security vulnerabilities from indexes.
4. Native integration
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Works with existing search APIs.
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Preserves existing workflows.
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Preserves data context and relationships.
Comparison table
Many organizations choose Qatalog for its fast deployment and minimal upfront costs as compared to Glean’s universal search solution.
|
Aspect |
Indexed Search (Glean) |
No-Index Serch (Qatalog) |
|
Use Case Fit |
General document discovery; Basic information retrieval; When coverage matters more than accuracy; Non-critical data access |
Specific valuable data discovery use cases; When accuracy is critical; Real-time analysis needs; Sensitive/regulated data |
|
Data Processing |
Creates and maintains separate search index; Processes all content upfront; Stores processed content; Uses pre-computed embeddings; Applies AI to indexed content; May lose original context |
Connects directly to data sources; Processes data on-demand; Maintains full original context; Generates fresh embeddings when needed; Applies AI directly to source data; Preserves complete data relationships |
|
Security |
Additional attack surface through index; Separate permission management; May retain deleted data; Needs periodic security audits; Complex compliance for regulated data; Permission drift risks |
No additional data copies to secure; Inherits source system permissions; No retained data concerns; Direct compliance with governance; Real-time permission enforcement; Single source of truth |
|
Performance |
Fast for basic retrieval; Degrades as index grows; Requires regular reindexing; Search latency increases over time; Resource-intensive maintenance; Struggles with real-time data |
Optimized for specific use cases; Consistent performance over time; No maintenance overhead; Direct processing of live data; Scales with data warehouse; Better for complex queries |
|
Data Warehouse Integration |
Treats warehouses like any source; Limited data relationship understanding; Basic SQL capabilities; May duplicate data; Cannot use warehouse optimizations; Limited analytics |
Native warehouse connectivity; Deep data model understanding; Advanced SQL generation; Uses warehouse computing power; Leverages built-in optimizations; Full analytical capabilities |
|
Query Process |
1. Searches pre-built index; 2. Returns matching documents; 3. May miss recent updates; 4. Limited to indexed content |
1. Connects to data source; 2. Generates optimized queries; 3. Processes live data; 4. Returns real-time insights |
|
Architecture |
Source → Index Pipeline → Search Index → Query → Results |
Query → Direct Connection → Live Processing → Results |
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Maintenance |
Regular reindexing needed; Index cleanup required; Storage optimization; Fragmentation management; Permission mapping updates |
No reindexing needed; No cleanup required; No storage optimization; No fragmentation; Native permission updates |
What financial milestones has Glean achieved?
Glean, founded by Arvind Jain in 2019, recently raised over $260 million in a Series E funding round, bringing its valuation to $4.6 billion. The company has achieved impressive financial milestones, including reaching $50 million in annual recurring revenue (ARR) over the summer. Glean projects $100 million ARR by the end of this year and expects to reach $250 million ARR by the end of next year.
projecting $100 million ARR by the end of the year, with a further projection of $250 million by the end of next year.
Related: Glean enterprise search pricing explained.
What does it mean for the AI search industry?
It signals strong investor confidence in the search as a service industry and accelerates growth and innovation within the sector. With substantial capital, companies like Glean can enhance their technologies, expand integrations, and scale rapidly, meeting the increasing demand for efficient and intelligent search solutions in enterprises.
This influx of funding also fosters competition, pushing startups and established tech giants to differentiate themselves through advanced features, improved user experience, and more tailored solutions for business needs.
Ultimately, this financial backing is shaping a more competitive, innovative landscape, making AI-powered search tools more accessible and effective for businesses across industries.
What companies are currently competing in the AI search market?
The AI search market is highly competitive, with a mix of well-funded generative AI startups like Qatalog, tech giants like Microsoft (Copilot) and Amazon (Q), and cognitive search tool providers such as Perplexity, Coveo, Sinequa, and Lucidworks, all vying for attention alongside Glean AI search.
Related: Glean vs Guru comparison