How to Search Company Knowledge With AI (2026)

Verdict

Start with one team's real questions and a restricted source set. Choose an AI search product only after it retrieves the right evidence for ordinary users, respects access changes and makes unsupported answers visible. Glean is a candidate when useful knowledge already spans many apps; Guru fits a maintained answer layer; Slite fits a docs-centered team. Those are operating-model choices, not measured accuracy rankings.

This guide owns the rollout decision: what to connect first, how to score the pilot and when to expand. The workplace-search shortlist compares vendors. Use the internal knowledge-base guide to establish owners and canonical answers, and the Slack, Drive and Notion guide for source-specific connection boundaries.

1. Turn the search complaint into a question set

Collect 25 recurring questions from one team before a vendor demo. Record the expected source, source owner, permitted audience and date at which an answer was correct. Include several question types rather than 25 easy document-title lookups.

Choose questions whose answers someone can adjudicate. A subjective “helpful” score alone cannot distinguish a fluent summary from a correct one. Keep part of the set unseen during tuning so the pilot does not merely reward memorizing the demo.

2. Pick a search model and budget boundary

Public pricing and limits checked October 23, 2026. Compare the proposed deployment, not one headline price.

Sources: Glean Core, Flex, Guru pricing, Guru source limits, Slite pricing. Credits, questions and seats are different units. Glean lists 100/user/week on each standard-model Thinking and Adaptive row without clarifying shared versus independent accounting; confirm that in the quote. Thinking queries above 30 FlexCredits count as multiple queries, and follow-ups count separately. Ask for the expected invoice at your query mix, including model use, overages, implementation and ongoing owner time. Slite's external sources also have connector-dependent Shared or Mirrored access modes; review the selected mode in its source documentation.

If Glean and Guru remain finalists, use the direct comparison to decide whether distributed retrieval or maintained answers should lead the rollout.

3. Connect the smallest useful corpus

Start with canonical policies, the active project area and a small set of relevant discussions. Inventory duplicate files, unowned pages and source access before granting a connector broad visibility. Do not import a whole drive merely because OAuth succeeds.

Keep a connection register: source, selected scope, connecting identity, permission model, last successful sync, known exclusions and accountable owner. Distinguish indexed content from live tools. Glean's connector guide explicitly says tools and MCP integrations can retrieve or act on live data without making it searchable. A successful tool call is therefore not proof that a source is in the search index.

Budget for freshness. Glean documents supported deletion events clearing results in minutes to hours, with full-crawl fallback intervals ranging from six hours to 28 days when events are missed or unavailable. Its crawl FAQ also warns that initial-readiness percentages can represent sampled priority content. Neither a readiness badge nor one fresh document proves the entire corpus is current.

4. Test access separately from answer quality

Use ordinary employee, restricted-team and removed-access test identities. Repeat the same questions with each. Check result titles, snippets, generated text, citations, previews and any shared answer surface; an inaccessible citation does not excuse revealing its contents in an answer.

Do not accept a platform-wide permission claim as a connector test. Glean's Salesforce documentation says indexed record permissions are mirrored but field-level security is not enforced at query time. If a permitted record includes a restricted field, exclude the sensitive content or reject that configuration. Guru's group-based mode can intentionally expose connected content independently of each reader's source-app permissions. Record that as an explicit access decision.

Set a maximum acceptable edit, revoke and delete propagation delay before the pilot. Use synthetic restricted content, record source-change time and test until the expected state appears. If a connector cannot meet the requirement, narrow the source or stop the rollout; do not hide the delay inside an average relevance score.

5. Score errors so they lead to different fixes

Record one row per question, identity and run. Include retrieved sources, answer, supporting passages, source revision, model/mode, latency, billable usage where available and reviewer decision.

These are suggested diagnostic steps, not findings from our own deployment. Report retrieval success and supported-answer rate separately. Track unsupported answers, permission failures and maintenance hours independently; do not let good document search average away a dangerous disclosure.

6. Expand only after a repeatable acceptance review

Agree the acceptance thresholds with source owners before running the pilot. Require zero unauthorized disclosures in the test set, citations supporting each material answer claim, visible abstention on missing evidence and measured propagation within the agreed window. A finite test is evidence about those cases, not proof that leaks are impossible.

Name the search-service owner and a backup. Give them a recurring queue for failed searches, broken connectors, access changes and expired content. Expand one source or team at a time and rerun the restricted-identity checks. For new-hire questions, continue with the knowledge-assistant onboarding guide.

What practitioner reports add

In a workplace-search evaluation thread, u/sibraan_ described strong known-file retrieval but difficulty synthesizing their organization's cross-system decisions, and a Guru maintenance burden their team did not sustain. Another participant, u/PomeloThomas2665, favored Glean's relevance on their Notion, Slack and Drive corpus. These are anonymous, unverified reports with different environments, not controlled comparisons or current product guarantees. They justify testing retrieval, synthesis and ownership separately; they do not establish a winner.

We reviewed public documentation, pricing and attributed discussion. We did not connect a private tenant, run the proposed pilot, measure accuracy or verify an invoice. The usable output of this guide is a buyer-run evaluation plan with explicit failure conditions.

Sources & further reading

• Glean enterprise search and interface illustration
• Glean connector architecture
• Glean crawl and deletion behavior
• Glean Salesforce field-access boundary
• Glean Core Suite pricing
• Glean Enterprise Flex pricing
• Guru pricing
• Guru source access and sync limits
• Slite pricing and allowances
• Slite pooled questions and credits
• Slite Agent source access modes
• Attributed workplace-search evaluation discussion