How to Optimize for Enterprise AI Search and Internal Copilots

Enterprise buyers now research vendors inside Glean, Microsoft 365 Copilot, and custom internal RAG assistants, not just in Google. Win that surface by making your docs, integration pages, and trust center publicly crawlable, structuring product facts so retrieval systems can extract them cleanly, and tracking how you show up when a buying committee asks its copilot to compare you against competitors.

*By Josh Bernstein, Co-Founder | August 19, 2026*

Eight in ten B2B technology buyers are already using AI agents somewhere in their purchasing process, according to IDC research published in August 2026, and it is not a future-state prediction. It is what is happening right now inside the procurement teams evaluating your category. A security lead types a question into Microsoft 365 Copilot before a call with your AE. A buying committee member asks Glean to summarize what your competitors said in a shared Slack channel last quarter. An IT architect points a homegrown RAG assistant at five vendor websites and asks it to build a comparison table. None of that happens in a browser tab you can track with a pixel.

This is a different battlefield than consumer GEO. ChatGPT and Perplexity answer questions for anyone with a browser. Enterprise AI search tools like Glean and Microsoft 365 Copilot sit inside a company's own tenant, blending your public content with the buyer's internal Slack threads, SharePoint files, and CRM notes, then handing the buying committee a synthesized answer that decides whether you make the shortlist. If your public content is not built for that retrieval layer, you are invisible at the exact moment the decision is being framed.

What Counts as "Enterprise AI Search" and Who You Are Actually Optimizing For

Enterprise AI search covers three overlapping categories, and each retrieves your content differently. Glean is a dedicated Work AI platform that indexes over 100 SaaS applications, internal wikis, and (optionally) public websites into a single permissioned index, then answers employee questions through its Assistant and autonomous Agents. Microsoft 365 Copilot is built into Word, Teams, Outlook, and SharePoint, grounding its answers in a company's Microsoft Graph data and, when an admin enables it, in live public web results pulled through Bing. Internal RAG assistants are the catch-all: custom chatbots that IT or data teams build on top of a vector database, sometimes wired only to internal tickets and wikis, sometimes explicitly pointed at a shortlist of vendor domains during an active evaluation.

The scale here is no longer niche. Glean crossed $300 million in annual recurring revenue in May 2026, tripling from the $100 million mark it hit just 15 months earlier, a pace industry analysts peg at roughly 89% year over year growth, and the company says it nearly doubled its Fortune 500 customer count over the same period, with named enterprise customers including Databricks, Reddit, and Pinterest. Microsoft disclosed 20 million paid Microsoft 365 Copilot enterprise seats at its FY26 Q3 close in April 2026, up from 15 million the quarter before, with adoption reaching roughly 41% of Microsoft 365 enterprise customers and close to 70% of the Fortune 500 using Copilot in some form. Every one of those seats is a potential research session about your product that never touches google.com.

What ties the three categories together is that they are retrieval systems, not crawlers optimizing for backlinks. They pull a chunk of your page into a context window, rank it against internal documents by semantic relevance, and hand a synthesized answer to a buyer who never has to click through. The rest of this piece treats each layer, retrieval, content structure, and trust signals, as a distinct problem to solve.

How Glean, Copilot, and Internal RAG Assistants Actually Retrieve Your Content

You cannot optimize for a system whose retrieval mechanics you do not understand, and the three enterprise AI surfaces do not all reach your public site the same way.

Glean's Website connector crawls a site recursively, either from a sitemap or from a set of seed URLs, and indexes what it finds as a dedicated searchable source alongside a company's Slack, Confluence, and Salesforce data. According to Glean's own connector documentation, the Website connector does not enforce per-document permissions the way its SaaS connectors do: any page reachable with the crawler's credentials gets indexed and made discoverable to everyone in that Glean tenant. That is good news for public-facing GEO work. A properly structured docs site, pricing page, or integrations directory that Glean's crawler can reach gets pulled whole into the same semantic index an employee queries when they ask "what does this vendor's SSO setup look like."

Microsoft 365 Copilot works differently. Its web grounding feature, when an admin turns it on, sends a short, stripped-down query (no user or tenant identifiers) to Bing, retrieves public results, and blends them with the tenant's own Graph data before composing an answer. Microsoft's own documentation confirms that users can inspect both the citations and the exact search keywords Copilot generated, which means the same fundamentals that win a Bing or Google AI Overview placement, clean headings, extractable answers, structured comparisons, carry directly into what a Copilot user sees cited inside Teams or Word. For a deeper breakdown of that specific mechanism, see our guide on how to optimize for Microsoft Copilot and Bing generative search.

Internal RAG assistants are the least predictable of the three because there is no standard architecture. Some are wired only to a company's own systems and will never see your website at all unless someone manually uploads a PDF or pastes in a URL. Others run a scheduled crawl of a defined vendor shortlist the moment a request-for-proposal opens. The practical implication is that you cannot "SEO" your way into every custom RAG deployment, but you can make sure that whenever one of them does reach your domain, whether through a live crawl or a rep manually feeding it your one-pager, the content it retrieves is complete, current, and structured for extraction rather than buried in a PDF or a marketing carousel.

*How public content moves from your website into a buying committee's copilot answer.*

The Public Content These Systems Are Actually Retrieving

The instinct to write more blog posts misses where enterprise buyers actually spend their attention once a copilot has surfaced you. Four content types do the heaviest lifting, and the honest, well-verified breakdown of who these systems trust matters more than most GEO advice admits: no single source type dominates. Reddit is the single most-cited domain across major AI engines, showing up in roughly 40% of citations across models and around 24% of Perplexity's citations as of January 2026, while Wikipedia accounts for about 13% of ChatGPT citations and LinkedIn is one of the most-cited sources specifically for B2B and brand queries. Community discussion and earned media routinely outweigh anything living on your own domain, which is exactly why the public content you do control needs to work twice as hard.

Product documentation and integration pages are the first thing a technical evaluator's copilot pulls when someone asks "does this connect to our stack." If your integrations page is a logo wall with no detail on auth methods, data flow, or API scope, there is nothing for a retrieval system to extract beyond a name.

Pricing and packaging pages get quoted constantly because buying committees ask copilots to build comparison tables before they will agree to a call. A pricing page with vague tiers and "contact us" gates gives an AI assistant almost nothing concrete to cite, while a page with explicit feature-to-tier mapping gives it a table it can lift directly. We cover this in more depth in our guide to optimizing pricing pages for AI-assisted buyer research.

Security, compliance, and trust documentation increasingly function as procurement content in their own right, a shift covered in the next section. Case studies and customer proof round out the set, because a copilot asked "who else in our industry uses this" needs a named, specific, quotable example rather than a generic testimonial, which is why the structure of that content matters as much as its existence, something we break down fully in how to optimize case studies and testimonials for AI citation.

Structuring Product Facts So Retrieval Systems Can Extract Them

Retrieval systems do not read a page the way a human does. They chunk it, embed it, and rank each chunk by relevance to a query, which means a fact buried in a paragraph of marketing copy is far less retrievable than the same fact stated plainly in a labeled table or a short, declarative sentence. Research from Princeton and Georgia Tech, presented at KDD 2024 across roughly 10,000 queries, found that adding concrete statistics to a page increased its visibility in generative answers by about 41%, adding direct quotations added about 28%, and citing authoritative sources boosted visibility by as much as 115% for pages that were not already ranking at the very top. The effect was strongest for content that was not already dominant, which is precisely the position most B2B vendors are in in enterprise search, where the "index" is not the open web but a smaller, more literal-minded internal one.

Practically, that means product pages should state facts the way you would want a Slack bot to quote them back: "Supports SAML and SCIM provisioning, SOC 2 Type II audited annually, 99.95% uptime SLA on the Enterprise tier," not "we take security seriously and offer flexible enterprise-grade integrations." Feature comparison tables should exist as actual markup, not screenshots or interactive widgets that a crawler cannot parse. Schema.org structured data still matters here even though its visual payoff on Google has shrunk: Google removed HowTo rich results back in 2023 and fully removed FAQ rich results from search on May 7, 2026, but FAQPage and Article schema remain valid, actively maintained schema.org types, and they continue to help AI systems parse the relationship between a question and its answer during extraction, even with no SERP snippet attached anymore. Our guides on using schema markup to win AI citations and on making your site crawlable and parseable for AI engines go through the implementation details.

A few structural habits consistently improve extractability across Glean, Copilot, and custom RAG pipelines:

• Put the direct answer to a likely buyer question in the first sentence of a section, then expand with detail. Retrieval systems favor chunks that are self-contained.
• Use real HTML tables for anything comparative: pricing tiers, feature matrices, integration lists, compliance certifications.
• Keep integration and API documentation on crawlable, unauthenticated pages wherever the content itself is not sensitive. Gated PDFs are invisible to a website crawler.
• Name specific numbers, certifications, and version details rather than adjectives. "SOC 2 Type II, last audited March 2026" retrieves; "enterprise-grade security" does not.
• Maintain a single current source of truth per fact. Conflicting numbers across a pricing page, a one-pager, and a help doc actively degrade an AI system's confidence in citing any of them.

Security, Integration, and Compliance Docs Are Now Procurement Content

The most underinvested asset in most B2B marketing stacks is the trust center, and that gap is becoming visible in a very literal way. Procurement and security teams are increasingly piloting AI agents that scrape a vendor's public security posture, parse SOC 2 and ISO 27001 status, and pre-fill internal risk assessments before a human reviewer ever opens the vendor's website. That shift means whether an AI assistant can actually read your trust center is turning into its own evaluation criterion, separate from whether the certifications exist at all. A trust center locked behind a gated PDF download or an NDA request form is functionally invisible to the same copilot a security architect is using to build their shortlist.

The fix mirrors the structured-facts principle from the previous section, applied specifically to compliance. Publish subprocessor lists, data residency details, SSO and SCIM support, and audit dates as plain, crawlable text, not scanned documents. State your certifications by name and date rather than badge images alone, since a badge conveys nothing to a text-based retrieval system. This matters even more if your category faces heavier scrutiny, healthcare, finance, legal, or public sector, where a buying committee's copilot is often explicitly instructed to check for named certifications before a vendor advances.

This is also where the two-sided nature of retrieval becomes obvious. Glean's Website connector, by its own documentation, does not enforce per-document permissions when it crawls a public site: whatever it can reach with its crawler credentials becomes visible to everyone inside that Glean tenant. If your security documentation is genuinely public and well-structured, that lack of granularity works in your favor, since a single crawl pulls your entire trust posture into the buyer's internal knowledge graph in one pass, right next to the vendor comparison notes their own team already wrote.

Winning the Buying Committee Before You Ever Get a Meeting

The research phase now happens largely before a seller is in the room, and the data on how buyers use AI during that phase is specific enough to act on. In a Gartner survey of 645 B2B buyers fielded in late 2025 and published in May 2026, buyers reported consulting an average of seven information sources during a recent purchase, and 45% said they used generative AI, primarily to gather information on vendors and products. The same survey found that 69% of buyers still turn to a sales rep to validate what the AI told them, which means an AI-generated shortlist is not the final word, but it is very often the first word, and it shapes which vendors get a validation call at all.

That sequencing has a direct implication for content strategy: your job is not to win the sale inside the copilot, it is to survive the shortlist the copilot builds. A buying committee member who asks Glean or Copilot to compare three vendors on integration depth, security posture, and pricing structure is generating an internal document that gets shared with the rest of the committee before anyone from your team is looped in. If your public content cannot answer those three questions clearly, you may never make that document, which is exactly why the content types below need to be mapped to a specific stage of the buying process rather than treated as one undifferentiated pile.

*What a buying committee's copilot needs at each stage of enterprise procurement.*

Measuring Your Enterprise-Surface Visibility

You cannot pull a rank-tracker report for what a private Glean tenant said to a Fortune 500 security architect, which is the uncomfortable part of this channel: most of the signal is genuinely dark. A few measurement approaches still work. Prompt testing against the public-facing versions of these tools is the most direct: Microsoft 365 Copilot's consumer and Bing Chat surfaces run largely the same grounding logic as the enterprise product, so testing how your brand appears when a Copilot-style assistant answers competitive and comparison questions gives you a reasonable proxy, especially paired with the tactics in our Microsoft Copilot optimization guide. Self-reported attribution closes some of the remaining gap: adding a "how did you hear about us" field that includes options like "an internal AI tool" or "our security team's AI assistant" to demo request and RFP forms is one of the only ways to directly hear from a buyer that a copilot shaped their shortlist, a method we detail fully in how to attribute pipeline to AI search with GA4 and self-reported data.

The tooling landscape for tracking AI visibility more broadly is also consolidating fast, which is worth knowing when you are building your own measurement stack. Sitecore acquired the GEO monitoring startup Scrunch for roughly $225 million in June 2026 to fold AI-visibility tracking directly into its digital experience platform, and Profound, a dedicated AI-visibility monitoring company, raised a $96 million Series C in February 2026 at a $1 billion valuation, reportedly serving more than 10% of the Fortune 500. Platforms like Promptive sit in that same category, running structured prompts across ChatGPT, Claude, Gemini, and Perplexity daily to track how often and how accurately a brand gets mentioned, which is the closest proxy available for the enterprise-copilot layer until those tools expose citation data of their own. At minimum, build a monthly cadence: run the same 15 to 20 buyer-style prompts every month, track whether your brand appears, whether the facts cited are current, and whether competitors are being recommended over you for reasons your public content could actually fix.

A 90-Day Plan to Get Surfaced Inside Enterprise Copilots

Most teams trying to tackle this end up boiling the ocean. A phased rollout gets you real coverage without stalling on scope.

1. Weeks 1 to 2: Audit crawlability and permissions. Confirm your docs, pricing, integrations, and trust center pages are reachable without login, check your robots.txt and sitemap for accidental blocks, and verify nothing critical to a buying decision sits behind a gated PDF.
2. Weeks 3 to 5: Rebuild pricing and comparison content as extractable tables. Replace screenshots and interactive pricing widgets with real HTML tables that state tiers, features, and limits in plain text.
3. Weeks 5 to 7: Publish structured security and compliance documentation. Move SOC 2 status, subprocessor lists, SSO/SCIM support, and data residency details out of sales-gated decks and onto crawlable public pages with explicit dates.
4. Weeks 7 to 9: Strengthen case studies with named, specific proof. Replace generic testimonials with named customers, specific metrics, and direct quotes, since quoted material measurably improves extraction in generative systems.
5. Weeks 9 to 10: Add FAQPage and Article schema across key pages. This will not restore a Google rich result, but it gives every retrieval pipeline, Glean's crawler, Copilot's grounding, and any custom RAG assistant, a cleaner structural signal for question-answer extraction.
6. Weeks 10 to 12: Stand up monthly prompt testing and self-reported attribution. Add an AI-sourced option to every demo and RFP form, and begin running a consistent set of buyer-style prompts against the public AI assistants each month to catch drift before it costs you a shortlist.

Frequently Asked Questions

What is enterprise AI search, and how is it different from ChatGPT or Google?

Enterprise AI search refers to tools like Glean and Microsoft 365 Copilot that run inside a company's own tenant, blending public web content with a buyer's internal Slack messages, SharePoint files, and CRM notes into one synthesized answer. Unlike public AI search, the results are private to that organization, which means you cannot rank-track them directly, only optimize the public content those systems are able to retrieve.

Can I influence what Glean or Copilot says about my product if I never get access to a buyer's tenant?

Yes, indirectly. You cannot control the internal documents a buyer's own team has written about you, but you can control whether your public docs, pricing, security, and integration pages are crawlable, current, and structured clearly enough that when the system does reach your domain, it retrieves accurate, citable facts instead of nothing at all.

Does Glean's Website connector respect my robots.txt or gated content?

Glean's connector documentation states that it crawls pages reachable with the credentials it is given and does not enforce per-document permissions the way its SaaS connectors do, so anything genuinely public and unblocked is likely to be indexed. Content behind a login wall, gated PDF form, or blocked by robots.txt will not be reachable unless an administrator explicitly configures credentialed access.

Should I still add FAQPage and HowTo schema if Google removed those rich results?

Yes. Google removed HowTo rich results in 2023 and fully removed FAQ rich results from search on May 7, 2026, so neither will produce a visual snippet in Google anymore. Both remain valid, actively supported schema.org types, and they continue to help AI systems, including enterprise retrieval pipelines, parse the relationship between a question and its answer during extraction.

How is this different from optimizing for ChatGPT or Perplexity citations?

The underlying content principles overlap heavily: clear structure, concrete facts, real tables, and named proof all help in both contexts. The difference is distribution and measurement. Public AI search draws from the open web and can be prompt-tested directly, while enterprise copilots draw from a private blend of your public content and the buyer's own internal documents, which makes measurement indirect and dependent on proxies like public-Copilot testing and self-reported attribution.

What's the fastest way to check if we already show up inside these tools?

Start with the public-facing equivalents you can actually test: run comparison and vendor-evaluation prompts through Microsoft Copilot's consumer surface and Bing Chat, since they share grounding logic with the enterprise product, and manually crawl your own site the way Glean's Website connector would to confirm your docs, pricing, and trust pages are actually reachable without a login.

Do security and compliance docs really get cited by AI copilots, or is that overstated?

Procurement and security teams are increasingly using AI agents specifically to scrape vendor security posture and pre-fill internal risk assessments, which is why whether an AI agent can actually read a trust center has become its own evaluation criterion in 2026. A trust center published as crawlable text with explicit certification dates is far more likely to be retrieved and cited accurately than one locked behind a gated download.

How long before this work shows up as pipeline?

Most teams see measurable shifts in prompt-test results within 60 to 90 days of restructuring pricing, security, and comparison content, since that is roughly the cycle time for crawlers like Glean's Website connector and Bing's grounding index to re-crawl and re-embed updated pages. Attribution to closed pipeline takes longer to confirm and depends heavily on adding the self-reported "how did you hear about us" field early, since that is often the only direct evidence a copilot shaped a specific deal.