When your product manager asks "What feedback did we get about the onboarding flow last quarter?" they shouldn't spend 45 minutes digging through Slack threads, Zoom recordings, and email chains. Enterprise search tools turn these hunts into seconds-long searches by unifying fragmented information across platforms.
This guide compares enterprise search tools based on what knowledge workers actually care about: cross-platform coverage, setup complexity, security certifications, and whether the tool captures meeting content or only indexes static documents.
These tools represent different approaches to enterprise search, from document-focused platforms to AI assistants that capture meetings automatically.
Read AI is an AI assistant that combines enterprise search with automatic meeting capture, email and calendar support, and messaging synthesis to build a personal knowledge graph from your calls, casual interactions, email threads, chat messages, and connected files and software systems.
You can type a question about product research into Search Copilot and find the exact moment from September's research review when your design lead mentioned friction points, plus all the back-and-forth with customers via email, Zendesk tickets, and other inputs that matter.
Setup takes 20 minutes, and the platform connects to Gmail, Drive, messaging platforms, connected software services, and your calendar, then starts indexing immediately. That 20 hours saved monthly goes back to strategic work instead of hunting through recordings.
Read AI supports bottom-up adoption: teams can start instantly and then expand across the organization while still incorporating an organization’s security and administration’s best practices as value proves out. When employees leave, their captured interactions stay accessible as insurance of intelligence, preserving institutional knowledge that would otherwise walk out the door.
Best for: Organizations of any size using Teams, Meet, Zoom, Drive, OneNote, Slack, Notion, Asana and other popular platforms, where knowledge lives across platforms rather than in a central wiki.
Security: SOC 2 Type II, HIPAA compliant, GDPR compliant, inherits permissions from connected platforms.
Atlassian Confluence combines wiki-style documentation with AI-powered search across pages, comments, and attached files.
Confluence works best when it's already your documentation hub. Search queries return results from Confluence pages plus linked Jira tickets, so project managers find both the requirements doc and the tickets that reference it. Organizations using other Atlassian products (Jira, Trello, Bitbucket) benefit from unified search across the stack.
Best for: Organizations already using Confluence as their central wiki, particularly teams invested in the Atlassian ecosystem.
Security: SOC 2 Type II, GDPR compliant, granular permission controls at space and page level.
Glean connects to 100+ enterprise applications and builds a unified index across all of them. The platform generates AI-powered answers with citations linking back to source documents, so users can verify where information came from.
Implementation requires professional services. Glean consultants work with your IT team to configure connectors, tune relevance, and set up permission mappings. Expect months of setup before employees run searches, plus ongoing maintenance contracts.
Annual costs typically reach six figures, which limits adoption to organizations with dedicated enterprise search budgets.
Best for: Large enterprises (1,000+ employees) with IT resources for managed implementation and budget for multi-year contracts.
Security: SOC 2 Type II, FedRAMP authorized, HIPAA compliant.
Squirro specializes in extracting structured insights from unstructured documents like contracts, research reports, and regulatory filings.
Squirro's strength is handling complex document relationships in regulated industries. A compliance officer searching for exposure to a specific counterparty can find mentions across loan documents, email correspondence, and risk assessments in one query.
Best for: Financial services, insurance, and healthcare organizations with large document repositories and strict audit requirements.
Security: SOC 2 Type II, ISO 27001, configurable data residency for regional compliance.
Guru embeds directly into Slack, Microsoft Teams, and Chrome. Instead of switching to a separate search interface, employees see relevant knowledge cards inside their existing tools.
When a sales rep mentions a competitor in Slack, Guru surfaces the latest battlecard. This contextual delivery drives higher adoption than tools requiring employees to remember to search.
Best for: Organizations where adoption depends on meeting employees in their existing workflows rather than adding another tool to check.
Security: SOC 2 Type II, HIPAA available on enterprise plans.
Elastic provides the infrastructure layer that powers many custom enterprise search implementations. Organizations with engineering resources build tailored search experiences on top of Elasticsearch, configuring relevance algorithms, security rules, and data pipelines to match specific requirements.
The platform handles massive data volumes with real-time indexing.
An e-commerce company might process millions of product updates daily while maintaining sub-second search response times. This flexibility comes with complexity: expect dedicated engineering time for implementation and maintenance.
Best for: Organizations with technical teams who need search customized beyond what packaged solutions offer.
Security: Document-level and field-level access controls, encrypted data in transit and at rest, audit logging for compliance.
Before comparing tools, understand the evaluation criteria that separate enterprise-ready platforms from basic search.
Any enterprise search tool you evaluate should have SOC 2 Type II certification at minimum. Type II means the vendor passed a security audit that tested their systems over several months, not just a single snapshot.
Your industry determines which additional certifications matter. Healthcare organizations need HIPAA-compliant vendors with signed Business Associate Agreements (BAAs).
Without a BAA in place, your organization bears full liability for any data breach involving protected health information.
Financial services firms typically require SOC 2 plus ISO 27001.
If you're evaluating tools that capture meeting recordings (not just index documents), you'll face additional compliance requirements.
Voice recordings can constitute personal data under GDPR and typically require explicit consent, especially when used for identification. In the U.S., California, Illinois, and ten other two-party consent states require agreement from everyone on the call before recording starts.
How quickly employees get value depends on your deployment model. Top-down implementations require IT to configure the system, set up integrations, define permissions, and roll out access before anyone runs a single search.
For Microsoft 365 environments, the Graph API simplifies this by providing a single endpoint for Teams, SharePoint, OneDrive, and Outlook. Even so, mid-market organizations (50-500 employees) typically spend 3-6 months on implementation.
Google Workspace takes longer because each service (Gmail, Calendar, Drive, Docs, Sheets, Keep) requires its own API connection and OAuth flow.
Bottom-up tools skip the IT queue entirely. Individual employees sign up, connect their own accounts, and start searching within minutes. IT gets involved later to add governance, expand access, and consolidate billing. This model works for organizations that want to prove value before committing IT resources.
Bottom-up adoption also reveals shadow IT. When employees can try a tool without IT involvement, you discover which teams already use AI tools you didn't approve. That visibility lets you consolidate fragmented tools into a sanctioned platform before shadow IT becomes a security problem.
Semantic search finds relevant content even when search terms don't match the exact words in the source. Keyword search only returns documents containing the exact words you typed.
This capability is possible using Retrieval-Augmented Generation (RAG), which grounds AI responses in your actual enterprise data rather than generating answers from general training. Read AI's Search Copilot uses RAG to search across recorded calls, email threads, chat history, documents in cloud storage, and connected platforms.
The search tool should inherit permissions from each connected platform automatically, so employees only see what they're already allowed to access.
During vendor evaluation, ask how the tool handles permissions. Answers like "We maintain our own permission database" or vague responses about "enterprise-grade access controls" are red flags.
Cross-platform search queries all your sources simultaneously and connects related information across them. Single-platform search forces employees to check Slack, then Gmail, then Drive, then Zoom recordings separately.
Most enterprise search tools index each platform separately. Read AI's Topics feature is different: it groups related interactions across calls, chats, email threads, and connected documents and database updates by subject matter rather than platform.
Get Started with Read AI
Enterprise search works best when it captures interactions wherever work happens. Read AI builds a personal knowledge graph from your meetings, emails, messages, and documents, then surfaces what you need through Search Copilot when you ask, and through proactive agents like Monday Briefing before you think to ask.
The platform connects to 20+ tools including Zoom, Google Meet, Microsoft Teams, Slack, Gmail, HubSpot, Salesforce, and Notion.
Frame the business case around time reclaimed, not software costs. Track how long employees spend hunting for information before implementation, then measure reduction afterward. Convert those hours into dollar amounts using your organization's average salary.
Indexing time varies by data volume and platform type. Email and calendar index faster than large file repositories. Meeting recordings index as they're captured going forward, though historical recordings require additional processing time.
Most tools let you start searching immediately against whatever content has been processed, with results becoming more complete as indexing continues in the background. Ask vendors for typical indexing timelines during evaluation.
Most enterprise-grade tools index and return results in multiple languages, but cross-lingual search (where an English query finds relevant Spanish or German results) requires more advanced natural language processing that not all vendors support. If your organization operates internationally, test multilingual search during evaluation by running queries in one language against documents in another.
Focus on data handling and failure modes. Ask where your data is stored and whether you can specify regional residency. Ask what happens to your data if you cancel and whether you can export your data in a portable format.
Start with teams who spend the most time searching for scattered information: sales teams hunting competitive intel, support teams digging for previous case resolutions, and project managers tracking decisions across channels, and engineering teams that need context on unfamiliar parts of the codebase. Once these early adopters see value, collect their success stories and use them to expand to adjacent teams. Mandating adoption before proving value creates resistance, but when word spreads that the tool actually saves time, adoption follows naturally.
Disclaimer: Tools evolve quickly. Features described here reflect capabilities at time of writing. Verify current feature sets on each vendor's website before making decisions.
© 2025 Read AI, Inc.
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