Data as of Sep 19, 2026 · Based on 367 AI responses from ChatGPT Search and Google AI Mode · See how Parse measures this
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Source | Citation share |
|---|---|
 Buyer’s guide: How to choose an enterprise search platform – Computerworldhttps://www.computerworld.com/article/1620136/how-to-choose-an-enterprise-search-platform.html | 6.6% |
 Essential features for enterprise search platforms in 2025 - Gleanhttps://www.glean.com/perspectives/what-are-the-top-features-to-look-for-in-an-enterprise-search-platform | 6.0% |
Key considerations for selecting an enterprise search toolhttps://www.glean.com/perspectives/how-to-choose-the-best-enterprise-search-solution-for-your-company-needs | 4.7% |
 How To Choose The Best Enterprise Search Solutionhttps://www.coveo.com/blog/pick-best-enterprise-search-solution/ | 4.0% |
 11 Best Enterprise Search Software Tools (2026 Buyer Guide)https://www.gosearch.ai/blog/enterprise-search-software-2026/ | 2.6% |
 16 Best Enterprise Search Software Tools for 2026https://www.stravito.com/resources/best-enterprise-search-software | 2.5% |
 6 Best Enterprise Search Tools (2026 Comparison) - Read AIhttps://www.read.ai/articles/enterprise-search-tools | 2.2% |
 How to choose the right enterprise search solution | Algoliahttps://www.algolia.com/blog/ux/choosing-the-right-enterprise-search-platform-what-s-important | 1.9% |
Enterprise Search Software Guide 2024 - Paillor Bloghttps://blog.paillor.com/post/enterprise-search-software-guide/ | 1.8% |
 15 Best Enterprise Search Tools for 2026 | Atomicworkhttps://www.atomicwork.com/esm/best-enterprise-search-tools | 1.7% |
To pick an enterprise search platform, **start by mapping your data landscape, identifying your security and compliance needs, and matching your technical resources to an out-of-the-box AI tool or a developer-heavy infrastructure layer** **.**…
To pick an enterprise search platform, start by mapping your data landscape, identifying your security and compliance needs, and matching your technical resources to an out-of-the-box AI tool or a developer-heavy infrastructure layer .
Choosing the right system requires a structured evaluation of your company's data sources, user types, and deployment constraints.
| Platform | Best Suited For | Primary Strength |
|---|---|---|
| Glean | Modern cloud-first companies | Out-of-the-box workplace AI search and SaaS connectivity. |
| Coveo | E-commerce & customer service | Personalization, digital experience, and support portals. |
| Elastic | Engineering & technical teams | Highly customizable search infrastructure and data ingestion pipelines. |
| Sinequa | Highly regulated global enterprises | On-premises flexibility, deep security, and complex multilingual NLP. |
If you want to narrow this down, tell me:
The biggest mistake is picking an enterprise search platform by watching the vendor’s AI demo. **Pick it by testing retrieval quality, permissions, integrations, and total cost against your actual data and queries.** Current evaluation guidance consistently emphasizes hybrid retrieval, connectors, permission-aware…
The biggest mistake is picking an enterprise search platform by watching the vendor’s AI demo. Pick it by testing retrieval quality, permissions, integrations, and total cost against your actual data and queries. Current evaluation guidance consistently emphasizes hybrid retrieval, connectors, permission-aware search, and AI/RAG readiness.
Write down the 5–10 things employees actually need to find, for example:
Separate search from AI answers. You may need both, but they have different failure modes.
Make a list of the systems that contain the answers:
Then ask vendors about the specific connectors, not just “How many connectors do you have?” Connector depth and permission fidelity matter more than the headline connector count.
This is arguably the most important enterprise-specific test.
If Alice cannot access a document in SharePoint, the search engine shouldn't retrieve that document and then merely hide it from the UI. Ideally, authorization is enforced during retrieval so restricted content never becomes context for an AI-generated answer.
Test:
You generally want hybrid search: traditional lexical/keyword retrieval plus semantic/vector retrieval. Exact identifiers, product numbers, acronyms, names, and technical terminology can be poorly served by semantic search alone.
Build a test set of perhaps 50–200 real queries covering:
Have people who know the underlying data judge the results.
For an AI-search product, test:
| Dimension | What to measure |
|---|---|
| Retrieval | Did it find the right source? |
| Grounding | Is the answer supported by those sources? |
| Citations | Can the user verify the answer? |
| Permissions | Could it expose restricted information? |
| Freshness | Does it reflect recent changes? |
| Abstention | Does it say “I don't know” when appropriate? |
| Multi-hop | Can it combine information from several systems? |
Don't let a beautiful chatbot UI compensate for mediocre retrieval.
There are roughly three architectures worth considering:
Packaged enterprise knowledge/search product Good when you want employees to search across many SaaS systems without building the search experience yourself.
Search infrastructure/API Examples include platforms such as Elastic, OpenSearch, or Microsoft Azure AI Search. These make more sense when your engineering team wants to build its own search/RAG applications. OpenSearch, for example, emphasizes hybrid retrieval, RAG, relevance tooling, and flexible deployment.
Microsoft-centric enterprise search/AI stack If most of your content and identity infrastructure is already Microsoft-based, Microsoft's current Azure AI Search/Foundry stack provides hybrid/vector retrieval, agentic retrieval, and permission-aware enterprise knowledge capabilities.
There are also dedicated enterprise AI-search products such as Glean and Coveo, among others. The relevant comparison is less “which vendor is #1?” and more “which architecture fits our data, security model, and intended experience?”
I'd start something like this:
| Criterion | Weight |
|---|---|
| Retrieval quality on your test set | 30% |
| Security / permissions | 20% |
| Connector coverage & fidelity | 15% |
| AI/RAG answer quality | 10% |
| Freshness / indexing reliability | 10% |
| Administration & analytics | 5% |
| Deployment / architecture fit | 5% |
| TCO | 5% |
These aren't universal weights; adjust them according to your environment. For example, a highly regulated company might make security and governance substantially more important.
Give each finalist exactly the same:
Don't let each vendor choose its own demo dataset.
Also test failure cases. One recent buyer-testing guide specifically recommends testing deletions, indexing freshness, authorization, connector failures, no-result queries, AI citations, unsupported claims, and pricing dimensions before rollout.
Don't compare just the advertised per-user price.
Ask what happens when you add:
A platform can look inexpensive until you model your actual corpus and query volume.
Phase 1 — Requirements: sources, users, permissions, use cases, compliance, architecture.
Phase 2 — Longlist: 5–8 plausible platforms.
Phase 3 — Technical filter: eliminate anything that can't handle your critical sources, identity model, deployment requirements, or data residency.
Phase 4 — Bake-off: 2–3 finalists against the same real-world test set.
Phase 5 — Pilot: 4–8 weeks with real users and real permissions.
Phase 6 — Commercial evaluation: calculate 3-year TCO and migration/implementation costs.
Phase 7 — Decision: select based on measured performance against your requirements rather than vendor feature lists.
The key principle is: don't buy “enterprise AI search”; buy demonstrably good retrieval over your particular enterprise data, with your particular security model. Modern RAG and agent experiences are downstream of that foundation.
If you tell me company size, main data sources (e.g. Microsoft 365 + Slack + Salesforce), whether this is employee search or an API for your own AI app, and approximate document/user scale, I can turn this into a concrete vendor shortlist and a bake-off scorecard.
Don't let each vendor choose its own demo dataset.
Also test failure cases. One recent buyer-testing guide specifically recommends testing deletions, indexing freshness, authorization, connector failures, no-result queries, AI citations, unsupported claims, and pricing dimensions before rollout.
Picking an enterprise search platform is less about finding the tool with the most features and more about finding the one that fits your **content, users, security model, and future AI plans**. Modern enterprise search is increasingly used as a foundation for knowledge management, AI assistants, and…
Picking an enterprise search platform is less about finding the tool with the most features and more about finding the one that fits your content, users, security model, and future AI plans. Modern enterprise search is increasingly used as a foundation for knowledge management, AI assistants, and retrieval-augmented generation (RAG), not just document lookup.
A practical selection process:
Define what success means before looking at vendors.
Common goals:
Your priorities will differ depending on the goal. A platform optimized for internal knowledge discovery may not be the best fit for a high-volume customer-facing search experience.
Make a list of where your answers live:
Then ask vendors to demonstrate your actual sources, not a clean demo environment. Connectors and ingestion quality are often where enterprise search projects succeed or fail.
Key questions:
The most important test: Can users find the right answer quickly?
Evaluate:
Do a test with 50–100 real employee queries:
Score:
A search engine that finds everything but exposes the wrong things is unacceptable.
Verify:
Permission-aware retrieval is especially important if you plan to use AI-generated answers over enterprise content.
AI features vary widely. Separate marketing claims from real capability.
Ask:
A good AI search system should make it easier to find and verify information, not just generate fluent text.
A platform that works well on day one but requires constant tuning can become expensive.
Evaluate:
Ask the vendor:
“After launch, who owns improving search relevance every month?”
Look beyond license price.
Include:
Pricing models differ significantly across platforms, so clarify what drives cost before committing.
A good PoC uses:
A simple scoring model:
| Category | Weight |
|---|---|
| Search relevance | 30% |
| Security/permissions | 20% |
| Data connectors | 15% |
| AI capabilities | 15% |
| User experience | 10% |
| Administration | 5% |
| Cost | 5% |
Platforms often considered include:
The right shortlist depends heavily on your environment, scale, and whether you are buying a finished employee search product or building a search/AI retrieval platform.
Ask every vendor to show:
If you share:
I can help narrow the evaluation criteria and likely shortlist.
Picking an **enterprise search platform** can feel overwhelming because you're balancing data security, scale, user experience, and a ton of integrations. Here is a structured, step-by-step breakdown to help you choose the right one for your organization: 1. **Audit your data landscape**
Picking an enterprise search platform can feel overwhelming because you're balancing data security, scale, user experience, and a ton of integrations.
Here is a structured, step-by-step breakdown to help you choose the right one for your organization:
To help narrow down the best options for you, tell me:
Picking an enterprise search platform is a major operational decision. `You need a structured, step-by-step approach to ensure it integrates with your tech stack, handles your data security, and actually helps employees find what they are looking for`. 1. **Audit your data landscape** - Identify all data sources…
Picking an enterprise search platform is a major operational decision. You need a structured, step-by-step approach to ensure it integrates with your tech stack, handles your data security, and actually helps employees find what they are looking for.
If you'd like, tell me:
I can help you narrow down which platforms or architectural types (like traditional search vs. modern neural search) fit your environment best.
No sources cited
The best way to pick an enterprise search platform is **not** to start with a feature checklist. Start with the information problems you need to solve, then test vendors against your real data. Today, the major approaches include dedicated workplace-search products such as Glean, search/relevance platforms such as…
The best way to pick an enterprise search platform is not to start with a feature checklist. Start with the information problems you need to solve, then test vendors against your real data.
Today, the major approaches include dedicated workplace-search products such as Glean, search/relevance platforms such as Coveo, and build-your-own platforms such as Elastic. Microsoft also has Copilot Search, which is particularly relevant if you're heavily invested in Microsoft 365.
Separate these use cases:
If you primarily need the first three, you're evaluating enterprise search.
If you want the last two, you're increasingly evaluating an enterprise AI/context platform, not just a search engine.
Create a list of your actual systems:
Then ask each vendor:
"Can you index these systems, preserve their permissions, and keep permissions synchronized when they change?" This matters more than the vendor's AI demo.
For example, Glean currently advertises 275+ connectors and permission-aware, real-time synchronization. Microsoft Copilot Search advertises 100+ Microsoft connectors plus custom and ISV connectors. Coveo's connectors index content and its permissions.
Do not accept "we have a connector." Ask how it handles:
This is probably the most important part.
Build a test set of 50–200 real employee searches. Include ugly searches such as:
"latest SOC2 deck" "what did Jane say about the Acme renewal?" "2025 pricing exception for enterprise customers" "how do I get access to Snowflake?" "Q3 forecast Germany" Then have knowledgeable employees judge the results.
I'd score each query:
| Score | Meaning |
|---|---|
| 0 | Didn't find anything useful |
| 1 | Found vaguely related material |
| 2 | Found the right topic |
| 3 | Found the exact answer/document |
Calculate the average score across vendors.
This is much more valuable than a vendor saying their system has "AI-powered semantic search."
Don't assume good search automatically means good RAG/AI answers.
Test:
Microsoft, Glean and Coveo are all positioning their search products as layers that can feed AI experiences, rather than merely returning keyword results.
I'd explicitly test this scenario:
Alice can see Document A. Bob cannot. Search for Document A as Alice. Search for it as Bob. Change the underlying permission. Repeat the searches. You want to verify that the search index doesn't become a second, more permissive copy of your company's data.
Also investigate:
This is an area where architecture matters. For example, Elastic's documentation explicitly notes that some connector security features, such as document-level security, vary by connector.
This is a major architectural choice.
Think Glean/Coveo/Microsoft.
Good when you want:
Think Elasticsearch or another search infrastructure layer.
Good when you need:
Elastic currently positions Elasticsearch as the supported path for building search experiences; its older Enterprise Search/Workplace Search products have been discontinued in Elastic 9.x.
Ask the vendor to demonstrate—not merely describe:
Then ask:
"How many people will we need to operate this after deployment?" That's often where seemingly similar platforms diverge.
Your real cost is roughly:
licenses + implementation + integrations + search engineering + security work + administration + AI usage + ongoing tuning
A platform that costs more per user but eliminates six months of engineering may be substantially cheaper.
Conversely, if you already have a strong search/ML engineering team, paying a premium for a turnkey workplace experience may not make sense.
I'd weight the decision approximately like this:
| Criterion | Weight |
|---|---|
| Relevance on your real queries | 25% |
| Security / permissions | 20% |
| Connectors & data freshness | 15% |
| AI answer quality / citations | 10% |
| User experience & adoption | 10% |
| Administration / operations | 8% |
| Integration/API flexibility | 5% |
| Total cost | 7% |
And make security and critical connectors gates, rather than allowing a vendor to compensate for failing them with a great demo.
I'd probably evaluate 3–4 architectures, rather than 10 vendors:
The most important advice: don't run a 60-slide vendor bake-off. Give each vendor the same real data sources, same 100 queries, same users, and same security tests, and score the results.
If you tell me your company size, main systems (e.g. Microsoft 365 + Slack + Salesforce + Jira), and whether you want employee search vs. an API/platform for your own product, I can turn this into a concrete vendor shortlist + RFP scorecard + 30-day evaluation plan.
licenses + implementation + integrations + search engineering + security work + administration + AI usage + ongoing tuning
A platform that costs more per user but eliminates six months of engineering may be substantially cheaper.
Conversely, if you already have a strong search/ML engineering team, paying a premium for a turnkey workplace experience may not make sense.
I'd weight the decision approximately like this:
| Criterion | Weight |
|---|---|
| Relevance on your real queries | 25% |
| Security / permissions | 20% |
| Connectors & data freshness | 15% |
| AI answer quality / citations | 10% |
| User experience & adoption | 10% |
| Administration / operations | 8% |
| Integration/API flexibility | 5% |
| Total cost | 7% |
And make security and critical connectors gates, rather than allowing a vendor to compensate for failing them with a great demo.
I'd probably evaluate 3–4 architectures, rather than 10 vendors:
The biggest mistake is choosing an enterprise search platform by its **demo quality or number of connectors**. You should choose it based on how well it retrieves the *right information from your actual systems*, while preserving permissions. Enterprise search is increasingly becoming the retrieval layer for AI…
The biggest mistake is choosing an enterprise search platform by its demo quality or number of connectors. You should choose it based on how well it retrieves the right information from your actual systems, while preserving permissions.
Enterprise search is increasingly becoming the retrieval layer for AI assistants and RAG applications, not just a document-search box.
I’d score candidates across these seven areas:
| Criterion | Weight | What to test |
|---|---|---|
| Retrieval/relevance | 25% | Does it find the right answer, not merely matching documents? |
| Security & permissions | 20% | Does every user see only what they're entitled to see? |
| Connectors & data coverage | 15% | Does it deeply index your important systems? |
| AI/RAG quality | 15% | Citations, grounded answers, hybrid/semantic search, hallucination resistance |
| Administration & governance | 10% | Monitoring, analytics, controls, auditability |
| Integration/API | 10% | APIs, SDKs, workflows, identity, extensibility |
| TCO & vendor risk | 5% | Licensing, implementation, lock-in, support |
The weights should change based on your use case. For example, a regulated company might make security 30%+.
Before looking at vendors, collect 30–100 real queries employees ask today.
Include ugly ones:
For each query, identify the authoritative source and what a good answer looks like.
This becomes your evaluation dataset.
Make a table like:
| Source | Importance | Content | Permissions | Required? |
|---|---|---|---|---|
| SharePoint | High | Docs | Microsoft ACLs | Yes |
| Slack | High | Conversations | User/channel | Yes |
| Salesforce | High | Customer data | Role/record | Yes |
| Jira | Medium | Tickets | Project/user | Yes |
| Google Drive | Medium | Docs | File-level | Maybe |
| Data warehouse | High | Structured data | Complex | Maybe |
Don't be impressed by a vendor saying "100+ connectors." Connector depth matters more than connector count. Can it index comments, attachments, permissions, metadata, versions, custom fields, etc.?
This is the part I would not compromise on.
Ask vendors:
"If Alice loses access to a document at 10:03, when can that document stop appearing in search or AI answers at 10:04?" Then test it.
You want permissions inherited from the source systems and enforced when retrieving information—not merely a periodic permission snapshot. Permission failures in AI search can turn a productivity tool into a data-leak mechanism.
Don't buy into "semantic search solves everything."
Good enterprise retrieval generally combines:
A search for "SOC 2 Type II" may benefit from exact matching, while "how do we handle enterprise renewals?" benefits from semantic retrieval.
Hybrid retrieval is particularly important when you're building AI/RAG applications.
This is probably the single best thing you can do.
Give 3–5 vendors the same representative dataset/use cases and score the outputs without knowing which vendor produced them.
For every query, score:
Don't let vendors choose the queries. You provide them.
The market has several substantially different categories.
If you're primarily a Microsoft shop: start with Microsoft Search/Azure AI Search and see how far they get you. Microsoft is deeply integrated into Microsoft 365, while Azure AI Search is more of a developer-oriented search service.
If you want employee-wide knowledge search/AI: evaluate platforms such as Glean, Guru, Microsoft and similar enterprise-AI-search products.
If you need a highly customizable search/retrieval infrastructure: look seriously at Elastic, Coveo, Lucidworks, Azure AI Search and comparable developer-oriented platforms.
If search itself is part of your application/product: Algolia, Elastic, Coveo and similar search infrastructure can make more sense than an employee-facing knowledge product.
Current enterprise-search evaluations commonly include Microsoft, Elastic, Glean, Algolia, Guru and Coveo, among others.
I'd do it in this order:
One useful way to think about the decision is:
Don't buy "enterprise search." Buy the retrieval layer that makes your employees—and eventually your AI agents—able to reliably find the right information. That's also why I'd treat retrieval quality, permissions, and data coverage as hard gates, rather than letting a slick AI assistant compensate for weaknesses in those areas.
If you tell me your company size, main systems (e.g. Microsoft 365, Google Workspace, Slack, Salesforce, Jira, Confluence), and whether the primary goal is employee search, AI/RAG, or customer-facing search, I can turn this into a vendor shortlist + weighted scorecard for your situation.
You want permissions inherited from the source systems and enforced when retrieving information—not merely a periodic permission snapshot. Permission failures in AI search can turn a productivity tool into a data-leak mechanism.
This is the part I would not compromise on.
Ask vendors:
Picking an **enterprise search platform** is a massive decision. Get it right, and your team finds data instantly across silos; get it wrong, and it becomes an expensive digital paperweight. Here is a straightforward, step-by-step framework to help you evaluate and choose the right one for your organization: 1. Map…
Picking an enterprise search platform is a massive decision. Get it right, and your team finds data instantly across silos; get it wrong, and it becomes an expensive digital paperweight.
Here is a straightforward, step-by-step framework to help you evaluate and choose the right one for your organization:
Before looking at any software, audit what you actually need to search.
An engine is only as good as how people interact with it.
Enterprise search handles sensitive, proprietary, and regulated data.
Traditional keyword matching isn't enough anymore. Look at how modern platforms handle advanced features:
If you'd like to narrow this down, tell me:
I can give you a tailored shortlist of vendors that fit your profile.
No sources cited
Picking an **enterprise search platform** can feel overwhelming because you're balancing security, scale, and a sea of company data. Here is a clear, step-by-step roadmap to guide your evaluation and selection process. 1. Define Your Use Case and Scope - **Data Sources:** List everything the engine needs to connect to…
Picking an enterprise search platform can feel overwhelming because you're balancing security, scale, and a sea of company data. Here is a clear, step-by-step roadmap to guide your evaluation and selection process.
If you'd like, let me know:
I can help narrow down which specific platforms match your exact architecture.
No sources cited
Picking an enterprise search platform is less about finding the vendor with the most features and more about matching the platform to your **information landscape, security model, and business goals**. Modern enterprise search is often also the retrieval layer behind AI assistants and RAG applications, so evaluate it…
Picking an enterprise search platform is less about finding the vendor with the most features and more about matching the platform to your information landscape, security model, and business goals. Modern enterprise search is often also the retrieval layer behind AI assistants and RAG applications, so evaluate it as infrastructure, not just a search box.
Write down the top use cases before looking at vendors:
Different platforms excel in different areas, so avoid starting with a generic “we need Google for the company” requirement.
Make a list of your critical systems:
Then ask:
Connector coverage and permission handling are usually more important than a vendor’s total connector count.
Enterprise search can accidentally become a data exposure tool if permissions are handled poorly.
Evaluate:
Run tests with different user roles during evaluation—not just an admin account.
A platform that finds the right answer beats one with 100 AI features.
Test:
Ask vendors to run a proof of concept using your real data and real queries.
A good evaluation question:
“Show us the 20 searches employees perform most often. How accurate are the top results?”
Common approaches:
The platform copies content into its own index.
Pros:
Cons:
The platform queries source systems directly.
Pros:
Cons:
The platform provides answers, summaries, and citations.
Pros:
Cons:
A shortlist might include:
The “best” choice depends heavily on your environment.
Example:
| Category | Weight |
|---|---|
| Search relevance | 25% |
| Security & permissions | 20% |
| Data connectors | 15% |
| AI capabilities | 15% |
| Administration effort | 10% |
| User experience | 10% |
| Cost/TCO | 5% |
Adjust the weights based on your priorities.
A strong POC should include:
Avoid demos where the vendor controls the data set and search questions.
Include:
Pricing models vary widely, so ask vendors how costs change as content volume and usage grow.
If you tell me your company size, main data sources (Microsoft 365, Google, Slack, Salesforce, etc.), and whether you want employee search vs. AI assistant/RAG, I can help build a more specific shortlist and evaluation scorecard.
Avoid demos where the vendor controls the data set and search questions.
Include:
Pricing models vary widely, so ask vendors how costs change as content volume and usage grow.
