how do i pick an enterprise search platform?

Choosing an enterprise search platform can feel overwhelming because these tools touch every corner of an organization—from HR and IT to customer support and legal. To pick the right one, you need a structured evaluation framework.

Here is a step-by-step guide to cutting through the vendor noise and selecting the best platform for your organization:

- Map out where your company's knowledge actually lives (e.g., Salesforce, Confluence, Google Drive, Slack, legacy on-premise databases, GitHub).
- Ensure the platform offers pre-built, robust **connectors** for your specific ecosystem, and check how often they sync or if they support real-time indexing.

- Identify who is searching: Is it internal employees looking for HR policies, customer support agents looking for troubleshooting guides, or external customers using a public-facing help center?
- Determine the required **query complexity** : Do people just need keyword matching, or do they need deep semantic search, natural language processing (NLP), and generative AI answers (like RAG - Retrieval-Augmented Generation)?

- Enterprise search must respect existing access controls. If a user doesn't have permission to view a document in SharePoint, the search results must not show it to them.
- Verify compliance certifications (SOC 2, GDPR, HIPAA) depending on your industry.

- Look beyond basic search to see how the platform handles synonyms, acronyms, and intent.
- Check if administrators have tools to manually boost or bury certain results (curation) and whether the system automatically learns from user clicks to improve relevance over time.

- Look past the initial software licensing fee. Factor in implementation costs, infrastructure (cloud vs. on-premise), ongoing maintenance, and the engineering hours required to manage it.
- Ensure it scales smoothly as your document count grows from millions to tens of millions without tanking query latency.

To help narrow down the best options for you, tell me:

• What are your top 3 main data sources (e.g., Microsoft 365, Salesforce, ServiceNow)?
• Is this primarily for internal employees or an external customer-facing portal?
• Are you looking for a cloud-native AI solution or an open-source search engine you can customize?

Picking an enterprise search platform is less about finding the tool with the most features and more about matching the platform to your data landscape, security model, users, and future AI plans. Modern enterprise search often becomes a foundation for AI assistants and retrieval-augmented generation (RAG), not just a document finder.

A practical selection process:

1. Define the job you need search to do

Start with use cases, not vendors.

Common categories:

• Employee knowledge search

• “Find the latest HR policy”

• “What did we decide about Project X?”

• “Who knows about this topic?”

• Customer/support search

• Help-center search

• Agent knowledge tools

• Self-service portals

• AI assistant / RAG foundation

• “Answer questions from our internal knowledge”

• “Summarize documents with citations”

• “Power internal agents”

• Application search

• Search inside your product, website, marketplace, or app

Your top use case changes what matters most. A customer-facing search engine has different needs than an internal employee assistant.

2. Inventory your data sources

Make a list of where knowledge actually lives:

• Microsoft 365 / SharePoint
• Google Workspace
• Slack or Teams
• Confluence
• Jira
• Salesforce
• ServiceNow
• GitHub
• File shares
• Databases
• Internal applications

Then ask vendors:

• Do you have a native connector?
• Does it sync permissions?
• How often does content update?
• Can we build custom connectors?

A platform with 200 connectors is not useful if it misses your five critical systems.

3. Treat security as a first-class requirement

The biggest enterprise search risk is exposing information users should not see.

Evaluate:

• Permission-aware search results
• Identity integration (SSO, groups, roles)
• Audit logs
• Data encryption
• Data retention controls
• Tenant isolation
• Compliance certifications

Run a test:

Create a restricted document. Give access to one user. Confirm that another user cannot discover it through search. Do this during the evaluation, not after purchase. Permission handling is a core differentiator.

4. Test search quality with your own data

Vendor demos are usually optimized. Bring your own test set.

Create 50–100 real queries:

• Exact terms:

• “Contract ABC-123”

• “SOC 2 renewal date”

• Natural language:

• “What is our refund policy?”

• “Who owns the billing migration?”

• Ambiguous queries:

• “migration plan”

• “security review”

Score:

• Did it find the right answer?
• Did it rank the best result first?
• Did it cite sources?
• Did it avoid hallucinating?

Look for hybrid search (keyword + semantic/vector search). Enterprises still rely heavily on exact terms like product IDs and legal references, while semantic search helps with natural language.

5. Decide your architecture preference

Most platforms fall into a few models:

6. Evaluate AI features carefully

If AI answers matter, ask:

• Does it provide citations?
• Can users click back to the source?
• Does it respect permissions during retrieval?
• Can administrators control which sources are used?
• Can it integrate with your LLM stack?
• Can it support agents/workflows?

AI quality is limited by retrieval quality; a chatbot on top of poor search will still produce poor answers.

7. Compare vendors by fit, not rankings

Create a weighted scorecard. Example:

Then run a proof of concept with 2–4 finalists.

8. Ask about the hidden costs

Enterprise search pricing can vary by:

• Users
• Indexed documents
• Data volume
• Queries
• Compute
• Connectors
• AI usage

Ask:

• What does year-two pricing look like?
• What happens if data volume doubles?
• Are connectors extra?
• Are AI features separately metered?

Shortlist by situation

A rough starting point:

• Microsoft-heavy organization: evaluate Microsoft’s search/AI ecosystem first.
• Google Workspace-heavy organization: evaluate Google’s ecosystem first.
• Many SaaS systems across the company: consider dedicated enterprise search platforms.
• Customer-facing search: prioritize relevance tuning, analytics, and merchandising.
• Highly regulated environment: prioritize governance, auditability, and deployment controls.

A good final selection process is:

1. Define 5–10 business outcomes.
2. Map your data sources.
3. Create a test query set.
4. Shortlist 3 vendors.
5. Run a proof of concept using real permissions and real content.
6. Choose based on measured retrieval quality, not the demo.

If you share your environment (e.g., Microsoft 365 vs Google Workspace, number of employees, main data sources, internal vs customer search), I can help narrow the shortlist and build an evaluation matrix.

8. Ask about the hidden costs

Enterprise search pricing can vary by:

• Users
• Indexed documents
• Data volume
• Queries
• Compute
• Connectors
• AI usage

Ask:

• What does year-two pricing look like?
• What happens if data volume doubles?
• Are connectors extra?
• Are AI features separately metered?

Shortlist by situation

A rough starting point:

• Microsoft-heavy organization: evaluate Microsoft’s search/AI ecosystem first.
• Google Workspace-heavy organization: evaluate Google’s ecosystem first.
• Many SaaS systems across the company: consider dedicated enterprise search platforms.
• Customer-facing search: prioritize relevance tuning, analytics, and merchandising.
• Highly regulated environment: prioritize governance, auditability, and deployment controls.

A good final selection process is:

The best way to pick an enterprise search platform is to evaluate it as a retrieval and security layer, not as a prettier search box. In 2026, the important questions are hybrid retrieval, permissions, connectors, AI/RAG quality, and how much control you need over the underlying infrastructure.

1. Start with the use case

First decide which of these you're actually buying:

• Employee/workplace search — “Find the document, Slack thread, person, or answer.”
• Customer-facing search — website, product catalog, support portal.
• Enterprise knowledge/RAG — search becomes the retrieval layer for an AI assistant or agents.
• Custom search infrastructure — your developers build the search experience and need APIs/control.

This distinction matters a lot. For example, Glean is positioned strongly around permission-aware workplace search across SaaS applications, while Algolia is more API-first and suited to building search/discovery experiences into applications.

2. Weight the criteria

I'd start with this scorecard:

The weighting is deliberately biased toward whether it actually finds the right thing. A good-looking AI demo isn't a substitute for retrieval quality. A recent buyer's guide similarly puts retrieval first and emphasizes hybrid search, permissions, connectors, and AI readiness.

3. Test your real data—not vendor demos

This is probably the single most important step.

Give each vendor a representative corpus containing:

• messy documents
• old and new versions
• PDFs
• Slack/Teams conversations
• tickets
• wiki pages
• structured records
• acronyms and internal terminology
• duplicate/conflicting information
• documents with different access permissions

Then create 50–100 real queries from your employees.

Score things like:

• Was the correct result in the top 3?
• Was the answer actually supported by the retrieved content?
• Did it understand synonyms and internal terminology?
• Did it handle exact-match queries?
• Did it distinguish old from current information?
• Did it provide useful citations?
• How often did it hallucinate?
• How quickly did newly changed information become searchable?

Don't let vendors choose the questions. That's how you end up selecting the platform with the best demo rather than the best search.

4. Treat permissions as a hard requirement

For enterprise search, “the UI hides documents you're not allowed to see” isn't sufficient.

Ask exactly how authorization works during retrieval:

If Alice has access to a document but Bob doesn't, can Bob's search query, embedding retrieval, ranking, generated answer, logs, or citations ever expose information about that document? You want permissions enforced at the retrieval layer, not merely after search results have been generated. OpenSearch, for example, explicitly describes document- and field-level access control at retrieval time, while Glean describes permission-aware results based on connected systems.

Also test permission changes: revoke access to a document and measure how long it takes before it disappears from search/AI answers.

5. Don't count connectors—test them

“100+ connectors” sounds impressive, but the real questions are:

• Does it support your systems?
• Does it ingest permissions?
• Is indexing incremental?
• How quickly are changes reflected?
• Does it capture metadata?
• Can it handle custom/internal applications?
• What happens when the connector breaks?
• Can you build your own connector?

Glean, for example, advertises 100+ connectors and real-time, permissions-aware search.

But your evaluation should be based on your 10 most important systems, not the vendor's connector count.

6. Decide how much infrastructure control you want

There are roughly three approaches:

Turnkey workplace search: You buy the experience, connectors, relevance system, permissions integration, and AI layer. Less engineering; potentially less control.

Search platform/API: You get search infrastructure and APIs and build more of the experience yourself. Algolia is an example of an API-first approach.

Build/control the retrieval layer: Platforms such as OpenSearch/Elastic give you substantially more architectural control, but you're taking on more engineering, relevance tuning, operations, and integration work. OpenSearch, for example, supports on-prem, cloud, and hybrid deployment.

The mistake is buying a turnkey product when you really need a programmable retrieval layer—or building your own when your real requirement is simply “make our employees' knowledge searchable.”

7. Evaluate the AI separately from search

Ask vendors to demonstrate:

Query → retrieval → ranking → answer → citations

not just:

Question → impressive chatbot answer

For every generated answer, you should be able to determine:

1. What sources were retrieved?
2. Why were they retrieved?
3. Were they authorized for this user?
4. Did the answer stay within those sources?
5. What happens when the search system doesn't know?

This is particularly important because RAG quality is fundamentally constrained by retrieval quality.

8. Compare a shortlist, not 15 vendors

I'd probably put 3–4 materially different architectures through the same bake-off rather than doing endless feature comparisons.

A reasonable starting shortlist could include:

• Glean — particularly interesting for internal/workplace search and broad SaaS knowledge integration.
• Coveo — strong option to investigate for enterprise search/relevance and generative answering.
• Algolia — worth considering when search is something you're embedding into products/apps and want an API-first platform.
• Elastic/OpenSearch — worth evaluating when architectural control, customization, or deployment flexibility is especially important.

That's a starting point, not a recommendation; your use case should determine the final shortlist.

9. Calculate the real cost

Don't compare license prices alone.

Your 3–5 year TCO should include:

licenses + ingestion/indexing + implementation + connectors + customization + relevance engineering + security/compliance work + ongoing administration + AI/LLM consumption + migration/exit costs

Also ask whether pricing is based on users, indexed documents, queries, compute, connectors, AI usage, or some combination.

10. The final decision rule

I'd choose the platform that wins this equation:

Best retrieval quality × security × adoption ÷ total operational complexity rather than the one with the longest feature list.

And I'd make permissions/security and retrieval quality gates, rather than allowing a vendor to compensate for poor performance there with a great chatbot UI.

If you're buying for a particular environment—e.g. 500–5,000 employees, Microsoft 365 + Slack + Salesforce + Jira, internal knowledge search, with an AI assistant as the end goal—I can turn this into a vendor-by-vendor scorecard and shortlist with the specific tests I'd run.

To pick an enterprise search platform , you must evaluate your data sources, security requirements, and AI capabilities to match your organization's specific workflow.

Key Steps to Choose a Platform

1. Audit Data Sources and Connectors
   • Inventory systems: List where your data lives (e.g., Salesforce, Google Drive, Slack, Confluence, local servers).
   • Check native support: Ensure the platform offers ready-to-use connectors for your critical tools without requiring custom development.
2. Define Security and Governance Needs
   • Access controls: Verify that the platform respects employee roles and permission hierarchies from source systems.
   • Deployment model: Decide if you require SaaS, cloud-hosted, or on-premises infrastructure due to compliance rules.
3. Evaluate AI and Relevance Features
   • Natural language processing: Look for tools that understand conversational queries and user intent rather than just keyword matching.
   • Generative answers: Consider whether you need cited, direct summaries or traditional document lists.
4. Assess Scalability and Performance
   • Index volume: Test how the platform handles large, unstructured data repositories.
   • Query speed: Measure retrieval latency across distributed global teams.

To help narrow down options, tell me:

• What are your top 3 content repositories or apps?
• Do you require an on-premises or cloud-based deployment?
• Is this search mainly for internal employees or external customers?

Picking an enterprise search platform is less about finding the "best" tool on the market and more about finding the one that fits your company's data ecosystem, security requirements, and user habits.

Here is a structured, step-by-step framework to guide your evaluation:

1. Assess Your Data Landscape

• Data Sources: Map out where your data lives (e.g., O365, Google Workspace, Salesforce, Jira, local file shares, databases). Ensure the platform has out-of-the-box connectors for your mission-critical repositories.
• Data Volume & Scale: Estimate the total number of documents, records, and concurrent users. Make sure the architecture can scale without a massive latency penalty.
• Unstructured vs. Structured: Determine if you are searching mostly text documents (PDFs, wikis, emails) or structured data (CRM fields, database entries).

1. Define Core Security and Governance Requirements

• Access Controls: The search must respect existing permission models. If a user doesn't have access to a folder in SharePoint, it shouldn't show up in their search results (security trimming).
• Deployment Model: Decide whether you need a SaaS/Cloud solution, an on-premises installation, or a hybrid approach based on your compliance regulations (e.g., GDPR, HIPAA, SOC 2).

1. Evaluate AI and Relevance Capabilities

• Semantic Search & NLP: Look for Natural Language Processing (NLP) and vector search capabilities so users can type natural questions instead of just keyword matching.
• Generative AI / RAG: Many modern enterprise search tools integrate Retrieval-Augmented Generation to summarize search results or provide direct answers. Check for hallucination controls and source citations.
• Machine Learning Ranking: The platform should learn from user clicks and behaviors to continuously improve result relevance over time.

1. Consider Administration and Extensibility

• Analytics & Insights: Look for dashboards that show what users are searching for, what they can't find (zero-result queries), and popular content.
• Customization & APIs: Ensure developers can build custom UIs, integrate search into existing internal portals via APIs, and fine-tune relevancy weights.

1. Total Cost of Ownership (TCO)

• Licensing Model: Understand whether costs scale per user, per volume of data indexed, or per query.
• Maintenance Overhead: Factor in the internal engineering or IT resources required to maintain connectors, monitor index health, and manage permissions.

To help narrow down the right vendors, tell me:

• What are your top 3 data sources (e.g., Salesforce, Confluence, SharePoint)?
• Do you require a cloud-native or on-premises/hybrid deployment?

If you're picking an enterprise search platform, I'd avoid starting with a vendor feature matrix. Start with the job you need search to do, then force vendors to prove performance against your actual data.

The market has also shifted: modern enterprise search increasingly combines keyword + semantic/vector retrieval, permission-aware retrieval, and AI-generated answers/RAG.

1. Define your search use case first

There are several quite different products hiding under "enterprise search":

• Employee knowledge search: “Where's the latest pricing policy?”
• Customer/support search: “What are the known causes of this error?”
• Developer search: code, docs, tickets, incidents.
• Commerce/product search: finding products, SKUs, categories.
• AI/RAG infrastructure: retrieval that feeds your own agents and applications.
• Universal workplace assistant: search + answers + actions across SaaS apps.

Don't evaluate all of these with the same criteria.

For example, Glean is positioned heavily around workplace knowledge search, while platforms such as Elastic and OpenSearch can be attractive when you want to build/customize the underlying retrieval layer. Microsoft's offering is particularly compelling when Microsoft 365 is already the center of gravity.

2. Weight the criteria

I'd use something roughly like this:

The exact weights should change with your use case.

The big three

1. Relevance

Don't be impressed by a demo where the vendor searches its own pristine dataset.

Give each vendor 50–200 real questions your employees ask, including ambiguous queries, acronyms, outdated terminology, misspellings, and questions whose answer is distributed across several documents.

Measure:

• Precision@5/10
• Recall
• “Did the correct answer appear?”
• Time to answer
• Citation/source accuracy
• Hallucination rate
• User-rated usefulness

Hybrid keyword + semantic search is generally worth demanding rather than relying exclusively on vector similarity.

2. Connectors

Ignore the vendor's claim that it has “100+ integrations.”

Ask:

Does your connector actually understand our data? For every important system, test whether it handles:

• permissions/ACLs
• metadata
• comments
• attachments
• versions
• deleted content
• incremental updates
• near-real-time changes
• structured records
• custom fields
• deep links back to the source

Connector depth matters much more than connector count.

3. Security

This should be a hard gate, not a weighted feature.

A user must never receive a result merely because the search engine indexed it.

Test deliberately:

User A searches for something User A absolutely cannot access. Then test permissions changing in the source system and see how quickly search reflects the change.

Permission-aware retrieval/security trimming is a fundamental enterprise-search capability.

3. Decide whether you want a product or a search engine

This is one of the most important decisions.

Buy a workplace search product

Look at things like Glean, Microsoft 365 Copilot Search, and similar products.

Best when you want:

• fast deployment
• employee-facing search UI
• lots of SaaS connectors
• minimal search engineering
• personalized results
• AI answers out of the box

Glean, for example, emphasizes permission-aware search across 100+ tools.

Microsoft 365 Copilot Search is particularly interesting if you're already deeply invested in Microsoft 365 because it searches Microsoft and third-party sources through connectors and integrates with the broader Copilot experience.

Buy/build a retrieval platform

Look at things like Elastic, OpenSearch, Azure AI Search, etc.

Best when you need:

• control over ranking
• custom retrieval pipelines
• your own UI
• your own agents
• unusual data sources
• sophisticated metadata/filtering
• deployment control
• lower-level search APIs

OpenSearch, for example, supports hybrid/vector search and document- and field-level permissions at the retrieval layer.

The tradeoff is that you become the search product team.

4. Run a bake-off with your own data

This is the step I'd consider non-negotiable.

Pick 3–5 vendors and give them the same:

• 10–20 critical data sources
• representative users/roles
• 100+ real queries
• security scenarios
• freshness scenarios
• AI-answer scenarios

Then score them blind if possible.

A particularly good test set includes questions like:

“What's our policy for X?” “What did the engineering team decide about X?” “Find the latest version of the architecture document.” “Who knows the most about X?” “What changed between the old and current process?” “What customer commitments have we made regarding X?” “Show me everything related to project X that I'm authorized to see.” The last category is especially important because it exposes both retrieval quality and permission correctness.

5. Don't let the AI demo fool you

Ask vendors to show you the retrieval underneath the answer.

For every generated answer, you want:

Answer → sources → exact passages → source permissions → source timestamp

If the system gives you a beautiful paragraph but can't reliably show where it came from, that's a warning sign.

Also test questions where the correct answer is:

“I don't know based on the available information.” A good enterprise search system should be comfortable not answering.

6. Calculate TCO, not license price

Your real cost is approximately:

License + implementation + connectors + indexing/storage + AI usage + integration work + search engineering + ongoing relevance tuning + admin

A platform that costs 30% more but eliminates two engineers' worth of ongoing search infrastructure may actually be cheaper.

Conversely, a very expensive turnkey platform can be wasteful if your team already has strong search infrastructure expertise.

7. A simple decision tree

I'd narrow it down this way:

Mostly Microsoft 365? → Start with Microsoft 365 Copilot Search and compare alternatives only if there's a significant gap.

Need employee-wide knowledge search across lots of SaaS tools? → Evaluate Glean and comparable workplace-search products.

Need to build your own search/AI experience? → Evaluate Elastic, OpenSearch, Azure AI Search and similar retrieval platforms.

Need sophisticated product/customer search? → Put relevance, merchandising, ranking controls, analytics, and structured-data support ahead of workplace features.

Need maximum deployment/data control? → Give self-hosted/hybrid-capable platforms substantially more weight.

The most important question

Before choosing a platform, I'd answer this:

Are we buying “search for employees,” or are we buying “retrieval infrastructure for our AI applications”? Those can look superficially similar but lead to very different choices.

If you tell me your company size, main data sources (e.g. Slack, SharePoint, Google Drive, Salesforce, Jira), whether this is employee search or an AI/RAG backend, and whether you're primarily AWS/Azure/GCP, I can give you a much tighter shortlist and a vendor scorecard.

Picking an enterprise search platform requires balancing technical capabilities, security, and how well it integrates with your company's existing data ecosystem.

Here is a structured, step-by-step framework to help you evaluate and choose the right platform for your organization:

1. Assess your data landscape and connectors

• Data Sources : List every repository your employees use (e.g., M365, Google Workspace, Salesforce, ServiceNow, Jira, local file shares, databases).
• Pre-built Connectors : Ensure the platform offers robust, out-of-the-box connectors for your specific stack so you don't have to build and maintain custom APIs.

1. Define security and access control requirements

• Permissions Inheritance : The search tool must respect existing security ACLs (Access Control Lists). If a user doesn’t have permission to view a file in SharePoint, it should never appear in their search results.
• Compliance & Governance : Check for data residency options, GDPR/HIPAA compliance, and role-based access management.

1. Evaluate AI and search capabilities

• Semantic Search & NLP : Look for modern capabilities that understand natural language, user intent, and context rather than just keyword matching.
• Generative AI / RAG : Many modern tools integrate Retrieval-Augmented Generation to summarize documents and answer questions directly, rather than just pointing to links.
• Relevance Tuning : Check if administrators can manually boost, bury, or curate results for high-priority internal terms or departments.

1. Consider deployment and scalability

• Cloud vs. On-Premises : Determine whether your company requires a SaaS cloud solution, a hybrid model, or a fully on-premises deployment due to data sensitivity.
• Performance at Scale : Evaluate indexation speed, query latency, and how the platform handles millions of enterprise documents as you grow.

1. Analyze total cost of ownership (TCO)

• Pricing Model : Understand how they charge—is it per user, per volume of data indexed, per query, or a flat infrastructure fee?
• Maintenance Overhead : Factor in the engineering or IT hours required to manage schemas, troubleshoot connectors, and monitor query performance.

1. Run a proof of concept (PoC)

• Real-world Test : Feed a subset of your messiest, most unstructured data into a trial environment.
• User Feedback : Have actual employees from different departments (e.g., HR, Engineering, Sales) test the relevance and speed of the results.

If you'd like, let me know:

• What are your top 3 data sources (e.g., Salesforce, SharePoint, Confluence)?
• Do you prefer a cloud-native or on-premises solution?

I can provide a shortlist of top enterprise search platforms that match your environment.

If you're picking an enterprise search platform, I’d treat it less like a software feature comparison and more like a knowledge-access architecture decision.

The big mistake is choosing based on “number of connectors” or a flashy AI demo. The things that matter most are permission fidelity, connector depth, search relevance, answer quality, and total cost.

1. Start with the problem you actually want to solve

There are roughly three categories:

Modern platforms increasingly combine all three. For example, Microsoft describes Copilot Search as a universal search layer across Microsoft 365 and connected third-party systems, while Glean positions its product around AI-powered workplace search across many enterprise applications.

2. Make your data sources the first hard gate

Create a list of your actual systems:

• Microsoft 365 / SharePoint
• Google Drive
• Slack / Teams
• Confluence
• Jira
• Salesforce
• ServiceNow
• GitHub
• file shares
• databases
• proprietary/internal applications

Then ask vendors exactly what their connector does, not merely whether they "support Salesforce."

For each connector, ask:

• Does it index attachments?
• Comments?
• Permissions?
• Metadata?
• Version history?
• Groups?
• Deleted/changed content?
• How quickly do changes propagate?
• Can we build custom connectors?

This matters because a vendor can advertise hundreds of integrations while having a shallow implementation for the particular system you care about.

3. Treat security as a deal-breaker, not a scoring category

This is probably the most important criterion.

Your search system should never answer a question using information the requesting employee isn't authorized to see.

Test:

Alice can access document X. Bob cannot.
Bob searches for information that would cause document X to be retrieved.
What happens?

You want permissions to follow the source system's authorization model, including when permissions change.

Also ask specifically about:

• SSO / identity provider
• document-level permissions
• group permissions
• permission-change latency
• encryption
• data residency
• retention
• audit logs
• model-training policies
• tenant isolation
• deletion propagation
• admin access to indexed data

Permission-aware retrieval is particularly important once generative AI is involved, because unauthorized content must be excluded before it reaches the model, not merely hidden from the search UI.

4. Test search quality with your own queries

Don't accept a vendor's demo.

Build a benchmark of perhaps 50–200 real employee questions, such as:

"What's our policy for customers requesting refunds after 30 days?"

"What did engineering decide about the authentication migration?"

"Which customers are affected by the API deprecation?"

"Who owns the pricing model?"

Include deliberately difficult searches:

• synonyms
• acronyms
• incomplete information
• questions spanning multiple documents
• stale vs. authoritative documents
• conflicting information
• Slack discussions vs. formal documentation

Score:

Retrieval

• Did it find the right sources?
• Were the top results actually useful?
• Did it find information across multiple systems?

Answer

• Is the answer correct?
• Are claims supported by citations?
• Does it distinguish facts from inference?
• Does it identify conflicting sources?

Security

• Did it expose anything unauthorized?

This is much more informative than a feature checklist.

5. Compare the major approaches

I'd put your shortlist into four buckets:

Microsoft-centric organization → Microsoft 365 Copilot Search

Particularly attractive if most knowledge already lives in Microsoft 365. Microsoft says Copilot Search can incorporate third-party data through Copilot connectors, including systems such as Salesforce, ServiceNow, Confluence, Jira and GitHub.

Cross-SaaS knowledge / dedicated workplace search → Glean

Worth evaluating if your information is distributed across lots of SaaS applications and you want a dedicated enterprise knowledge layer. Glean says it connects sources including Google Workspace, Microsoft 365, Slack, Salesforce, Jira and ServiceNow.

Knowledge/workspace-centric organization → Notion Enterprise Search

Interesting if Notion is already central to your knowledge workflow and you want connected-app search inside that environment. Notion documents permission-aware search across connected applications such as Slack, Teams and Jira.

Build/control-your-own → Elastic/OpenSearch-style architecture

Consider this when you have unusual data, strong engineering capability, strict deployment requirements, or want control over the retrieval/AI stack. OpenSearch, for example, supports on-prem, cloud and hybrid deployment and retrieval-layer access controls.

Those aren't necessarily direct substitutes—you should first decide whether you want a managed workplace-search product or a search infrastructure platform.

6. Don't forget economics

Calculate 3-year TCO, not just license price.

Include:

• licenses/users
• connector costs
• indexing/storage
• implementation
• custom connector development
• security work
• migration
• admin/headcount
• AI/LLM consumption
• premium support
• future data-source additions

This is particularly important with Microsoft because Copilot Search itself may be included with eligible Copilot licensing while some connectors/additional capacity can have separate costs.

7. I'd use this weighting

For a typical 1,000+ employee organization:

And I'd make security failures automatic disqualification, rather than allowing a vendor to compensate for them with a higher UX score.

The evaluation I'd actually run

I'd narrow to 3–4 vendors, give each the same representative data sources and ~100 real queries, and run a 2–4 week pilot.

Then measure:

Search success rate × employee adoption × time saved, while separately verifying zero unauthorized retrieval.

That gives you a much better answer than “Vendor A has 200 connectors and Vendor B has 150.”

If you tell me your company size, Microsoft vs. Google environment, main systems (e.g. Slack/Confluence/Salesforce), and whether you want search only or an AI employee assistant, I can turn this into a concrete vendor shortlist + evaluation scorecard.

Picking an enterprise search platform is less about choosing the vendor with the longest feature list and more about matching the platform to your data landscape, security model, users, and AI roadmap. Modern enterprise search is increasingly used as a retrieval layer for AI assistants and RAG systems, so you should evaluate it as infrastructure, not just a search box.

A practical way to evaluate:

1. Start with the use case

Different problems favor different platforms:

Do not start with “which search platform is best?” Start with “what decisions or workflows should search improve?”

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2. Inventory your data sources

Make a list of where knowledge lives:

• Microsoft 365 / SharePoint
• Google Workspace
• Slack / Teams
• Confluence / Jira
• Salesforce
• ServiceNow
• GitHub
• file shares
• data warehouses
• internal applications

A platform that supports 200 connectors is not useful if it lacks your critical systems or cannot preserve permissions. Connector quality and access-control handling matter more than the raw connector count.

Ask vendors:

• Does the connector preserve source permissions?
• How quickly are changes reflected?
• Does it support custom connectors?
• What metadata is indexed?
• How are deleted documents handled?

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3. Test search quality with your own queries

Vendor demos are usually optimized. Bring your own evaluation set.

Create 50–200 real searches such as:

• “What is our refund policy for enterprise customers?”
• “Who approved the Q3 pricing change?”
• “Find the latest security review for Project X.”
• “Show contracts mentioning renewal exceptions.”

Score:

• Did it find the right answer?
• Was it fast?
• Did it rank the best result first?
• Did it explain why?
• Did it respect permissions?

Search relevance should usually be your highest-weight criterion.

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4. Evaluate AI capabilities separately from search

Many platforms now advertise “AI search,” but capabilities vary.

Look for:

Retrieval

• hybrid search (keyword + semantic)
• vector search
• metadata filtering
• relevance tuning

Generative AI

• citations back to sources
• grounded answers
• hallucination controls
• prompt customization
• model choice flexibility

Agent readiness

• APIs
• structured retrieval
• permissions-aware context
• workflow integration

A good AI answer is only as good as the retrieval layer underneath it.

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5. Make security a gate, not a score

Enterprise search can expose sensitive information if permissions are mishandled.

Require:

• identity integration (SSO)
• group/user permission enforcement
• audit logs
• data encryption
• compliance certifications relevant to you
• admin controls
• tenant isolation if SaaS

A search result should never appear to someone who could not access the original document.

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6. Compare platform approaches

You will usually see a few categories:

Suite-native search

Examples: Microsoft or Google ecosystem search.

Good when:

• most content already lives in that ecosystem
• you want simpler procurement

Tradeoff:

• may be weaker across many external systems

Enterprise AI search products

Examples include platforms such as Glean, Coveo, and Sinequa.

Good when:

• knowledge is fragmented
• employee productivity is the goal
• you want a packaged experience

Search infrastructure

Examples include Elastic and similar developer-oriented platforms.

Good when:

• you need customization
• engineering teams will build experiences

Tradeoff:

• more implementation effort

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7. Run a proof of concept (POC)

A good POC should answer:

Data

• Can it ingest your real sources?

Relevance

• Does it outperform your current search?

Security

• Are permissions correct?

AI

• Are answers accurate and cited?

Operations

• What does it take to maintain?

Avoid a POC that only shows a polished demo environment.

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8. Build a weighted scorecard

Example:

Adjust weights based on your situation.

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9. Watch for common mistakes

Avoid:

• choosing based on AI features alone
• ignoring permissions until late
• testing only clean documents
• underestimating connector maintenance
• buying before defining success metrics

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If you are selecting for a large company, the shortlist often changes depending on whether you are primarily a Microsoft shop, Google Workspace shop, customer-facing search team, or building an internal AI assistant. The right evaluation criteria differ substantially.

Picking an enterprise search platform requires balancing technical capabilities, security, and how well it integrates with your company's existing data silos.

Here is a structured framework to guide your selection process:

1. Assess your data ecosystem and connectors
   • Source coverage : Make sure the platform has out-of-the-box, secure connectors for your specific repositories (e.g., Microsoft 365, Salesforce, ServiceNow, Google Drive, Jira, and internal databases).
   • Data types : Verify it can handle unstructured text, structured data, PDFs, images (via OCR), audio, and video files.
2. Define security and access control requirements
   • Permission inheritance : The search engine must respect original file-level and role-based permissions (ACLs) so users only see results they are authorized to access.
   • Compliance and deployment : Decide whether you need a SaaS/cloud solution, on-premises , or a hybrid model based on your industry's data residency and regulatory needs (e.g., HIPAA, GDPR, SOC 2).
3. Evaluate search capabilities and AI/ML features
   • Relevance tuning : Look for semantic search, natural language processing (NLP), and vector search capabilities rather than just traditional keyword matching.
   • Generative AI / RAG : If you want AI-generated summaries or Q&A over your internal data (Retrieval-Augmented Generation), evaluate how accurate, secure, and grounded the platform's LLM integrations are against hallucinations.
   • Personalization and analytics : Check if it tracks user search behavior, zero-result queries, and click-through rates to help administrators improve relevance over time.
4. Consider scalability, performance, and maintenance
   • Index volume and speed : Test how quickly the platform indexes new documents and how fast query latency is under peak loads (N concurrent users).
   • Administration overhead : Look at how intuitive the management console is for setting up synonyms, featured results, and managing connectors without heavy developer intervention.
5. Calculate Total Cost of Ownership (TCO)
   • Licensing models : Understand if pricing is based on the number of users, volume of documents indexed, number of queries, or a core-based infrastructure fee.
   • Implementation costs : Factor in professional services, custom connector development, and ongoing maintenance.

If you'd like, tell me more about your environment:

• What are your primary data sources (e.g., Microsoft 365, Salesforce, custom databases)?
• Are you looking for a cloud-hosted (SaaS) or an on-premises solution?
• Do you need advanced Generative AI/LLM summaries or standard keyword/semantic search?

I can help you narrow down or compare specific platforms that fit your criteria.