Viktor vs Glean: Find vs Finish the Work

Key Takeaways

• Glean is an enterprise search and answers layer. It indexes your tools (Slack, Notion, Google Drive, Salesforce, GitHub, Jira), builds a knowledge graph, and answers questions with citations. The job is retrieval and synthesis.
• Viktor is an AI coworker that finishes the work. It lives in Slack and Microsoft Teams, connects to 3,200+ tools through real OAuth, and produces deliverables (PDF reports, drafted emails, updated CRM records, opened Linear tickets) that a human reviews before they ship.
• Glean ends the workflow at the answer. Viktor begins it there. Most enterprise questions are not the goal. They are the prerequisite to a one-page brief, a draft reply, a campaign change, a follow-up email. Glean stops at the citation. Viktor walks the rest of the way.
• Glean wins for enterprise knowledge discovery at scale. Onboarding a new hire, finding the policy doc nobody can locate, surfacing the right Slack thread from six months ago. Read-mostly. Built for thousands of seats.
• Viktor wins for cross-tool operator work that ends in a deliverable. Pull from Stripe, reconcile against HubSpot, draft the customer email, post the brief in Slack for review. Read plus write. Built for the team running the business day-to-day.

The short version

Glean is enterprise AI search, founded by ex-Google search leadership and used by Reddit, Pinterest, Figma, Sony Electronics, and Workday. It indexes your company's tools, builds a permission-aware knowledge graph, and answers questions in chat with citations. In 2024 and 2025 it expanded into "Glean Agents," letting builders compose retrieval-driven workflows. The strength is finding things and summarizing them. The shape is read-first.

Viktor is an AI coworker that lives inside Slack and Microsoft Teams, where your team already works. You @mention it the same way you would a human teammate. It connects to 3,200+ apps through real OAuth, runs scheduled crons, accumulates persistent memory of how your company works, and ships finished work for review. The strength is doing the cross-tool work that ends in a deliverable. The shape is action-first.

Both tools can be valuable in the same company. They occupy different layers and answer different questions. This comparison is about which one to pick when your team is asking, "where do we put our next AI bet?"

What Glean is built for

Glean's core is a unified search index across your enterprise apps. The original product (launched 2019) lets an employee type a question into a search bar and get back the right document, person, or Slack message, ranked by what they have permission to see. Permission-aware retrieval is the technical bar. Glean enforces source-of-truth permissions from each connected system, which matters when a search query could otherwise expose HR documents, board materials, or unredacted compensation data.

Around that index sits a chat layer (Glean Assistant) that answers questions in natural language with citations to the source documents. In late 2024 Glean added Glean Agents, letting customers and partners build retrieval-driven flows, for example "summarize the last week of customer-success Slack channels and post a digest." Agents are still anchored to retrieval. The job is to find, read, and synthesize, not to act in external tools.

Glean's published customer logos skew enterprise: Reddit, Pinterest, Figma, Sony Electronics, Workday, Databricks, Confluent. The deployment pattern is large rollouts where the buyer is IT, knowledge management, or a CIO. The contracts are annual.

That positioning matters for the comparison. Glean is built for "thousands of employees, dozens of tools, find the answer fast, never expose the wrong document." It is excellent at that job. It is not built to be the operator that runs your weekly revenue digest, drafts your customer-success follow-ups, or reconciles three platforms before a board meeting.

What Viktor is built for

Viktor is an AI coworker. The unit of work is a deliverable, not a search result. You @Viktor in a Slack channel and ask for a one-page revenue brief; Viktor pulls Stripe payouts and HubSpot pipeline, drafts a brief in Markdown, posts it in your channel, and waits for the thumbs-up before sending. You set up a cron that runs every Monday at 8 AM; Viktor pulls last week's Stripe invoices, calculates week-over-week MRR delta, and posts a draft for finance review.

Three properties matter:

1. Slack-native and Teams-native. Viktor lives where your team already discusses work. Adoption is "@mention it." Nobody opens a separate app, logs into another dashboard, or learns a new UI.
2. 3,200+ integrations through real OAuth. Stripe, HubSpot, Salesforce, NetSuite, QuickBooks, Linear, Jira, GitHub, GitLab, Notion, Google Drive, Google Ads, Meta Ads, Pylon, Zendesk, Pipedrive, Shopify, Pinecone, PostgreSQL, custom HTTP APIs. Real read and write, not retrieval-only.
3. Persistent, company-specific memory. Viktor remembers how your company works: who owns which channel, which deal stage to monitor, which finance lead approves what, what the standard formatting is. The longer it works for you, the better it gets at it.

The shape is action-first. Viktor finds what it needs to find, but the goal is always a deliverable that ends with a human approving and shipping.

Where they overlap, where they diverge

Both tools have a chat interface, both connect to enterprise systems, both produce summaries with citations. The overlap is the surface. Below the surface, the architectures are different.

The dividing line is read vs ship. Glean ends at "here is the answer with citations." Viktor begins there.

A comparison across six real workflows

This is where the difference lands. The same input, two different shapes of output.

1. New hire asks "what is our refund policy"

Glean. Returns the policy document with line citations, plus a synthesized one-paragraph answer. The new hire has the answer in five seconds, exact source attached. Glean's permission-aware retrieval ensures the new hire only sees policies they are allowed to see.

Viktor. Returns the same answer, but adds the next step: drafts the customer reply, pulls the customer's Stripe history if relevant, opens a Pylon ticket, posts the draft in the support channel for review. Viktor walks the workflow to its end.

Verdict. Glean wins for read-only knowledge lookup. Viktor wins when the answer is the input to a customer-facing action.

2. "Pull last week's Stripe invoices and post a revenue summary"

Glean. Glean does not pull live Stripe data and produce a structured deliverable. This is operator work, not retrieval. Glean Agents can summarize text-based data, but the cross-tool reconciliation, math, and Slack post are not the design center.

Viktor. A standard cron. @Viktor every Monday 8 AM, pull last week's Stripe invoices, calculate WoW MRR delta, post a draft summary in #revenue, tag the finance lead for review. The draft posts every Monday at 8:00 with structured data, the finance lead reviews and edits in three minutes, then approves.

Verdict. Viktor's home turf.

3. "Find the right Slack thread from six months ago about the SOC 2 audit"

Glean. Native strength. Indexes Slack history with permission awareness, returns the thread with the relevant message highlighted, plus the document referenced inside it.

Viktor. Can also search Slack, but if the goal is "find me the thread" Glean's index is purpose-built. Where Viktor pulls ahead is when the next step is "and now draft the SOC 2 readiness update for the audit channel," which Viktor will compose using the thread plus your Vanta and Linear data.

Verdict. Glean wins for the lookup. Viktor wins if the lookup feeds into a deliverable.

4. "Reconcile our HubSpot pipeline against Stripe to find at-risk renewals"

Glean. Cross-tool joining of structured data is not the design center.

Viktor. Standard work. Pull HubSpot deal records, cross-reference with Stripe subscription status, pull recent Pylon ticket sentiment, surface accounts where churn risk is rising. Post a list to the customer-success channel with a draft outreach for each one. The CS lead approves three, edits one, rejects one.

Verdict. Viktor's home turf.

5. "Onboarding: where do I find the engineering README, brand guidelines, customer success playbook"

Glean. Strong. Index across Notion, Google Drive, GitHub, Confluence. Returns the right document with citation in seconds. Permission-aware so the new hire only sees what they have access to.

Viktor. Can pull from the same tools, but if the goal is "find me the doc" Glean is purpose-built and tuned for it. Viktor adds value when the goal is "and now draft my Day 1 plan based on these docs," which involves cross-tool synthesis plus a personalized deliverable.

Verdict. Glean wins for the lookup. Viktor wins for the personalized deliverable that follows.

6. "Customer asks about an invoice mismatch in their portal"

Glean. Returns the relevant policy doc and any related Slack threads.

Viktor. Reads the customer's email, pulls their Stripe billing history (last six invoices, refunds, current subscription), pulls the HubSpot account record (plan, owner, recent activity), checks the Pylon ticket history, drafts a reply with the exact reconciliation, and posts the draft in the support review channel. Three minutes of context-gathering compressed into the moment the email lands.

Verdict. Viktor's home turf. The job is not finding the answer, it is shipping the customer reply.

The decision framework

Pick Glean if your top three problems are:

• New hires cannot find the right internal doc or person, and onboarding is slow as a result.
• Knowledge is fragmented across Slack, Notion, Drive, Confluence, and Salesforce, and search inside each tool is not enough.
• Compliance and permissions matter at the retrieval layer. Search must respect source-of-truth permissions across every connected system.

Pick Viktor if your top three problems are:

• The same cross-tool reporting work eats hours every week (revenue digest, ad performance, churn watch, pipeline review).
• Customer-facing replies need context from three or four tools and currently take 8 to 12 minutes each, ten or twenty times a day.
• Your team lives in Slack or Teams already and wants the AI to live where the work happens, not in a separate browser tab.

Pick both if you are at enterprise scale and your problem stack covers both halves. Glean as the search and answers layer, Viktor as the operator that ships the work. They do not compete for the same job.

Real prompts you can run in Viktor today

These are the kinds of @mentions our customers run on day one. None of them are search queries. All of them end in a deliverable.

@Viktor every Monday at 8 AM, pull last week's Stripe payouts, the
HubSpot pipeline change, and Meta Ads + Google Ads spend. Post a
one-page revenue brief in #leadership for the team to review.
Tag me when the draft is ready.

@Viktor watch our HubSpot pipeline. Any deal over $25K that has
gone 10+ days with no email activity, post a flag in #sales with
the deal owner, last touchpoint, and a draft check-in email I can
edit and send.

@Viktor read the new ticket from acme corp. Pull their Stripe
billing history, their HubSpot account record, and their last 5
Pylon tickets. Draft a reply with the exact reconciliation and
post it in #support-review for me to approve.

@Viktor every Friday at 3 PM, pull the engineering Linear board.
Highlight any issue marked "Customer Impact" that has not moved
in 7 days. Post the list in #eng-weekly with the assignee and
last status comment.

The pattern is the same: pull from the right tools, do the cross-tool work, draft the deliverable, wait for the human to approve. None of it is retrieval-only.

Safety and governance

Both tools take security seriously, in different shapes.

Glean. Permission-aware retrieval is the design center. Glean inherits permissions from each indexed source, so a search query can only return documents the asker has access to. SOC 2 Type II certified, GDPR compliant, supports SSO and SCIM. The risk model is "search results that should not have been returned." Glean's architecture is built to prevent that.

Viktor. Action-first means the risk model is "an AI took a wrong action in a real system." Viktor's answer is review-first by default. Every action is drafted in Slack for a human to approve before it ships. No email is sent, no CRM record is updated, no ticket is opened, no payment is moved without an explicit human "yes." Viktor is SOC 2 certified, GDPR aligned, CCPA compliant, and CASA Tier 3 certified, which is Google's highest application security tier and the standard for Google Workspace Marketplace approval. Most action-taking AI agents do not have CASA Tier 3.

Two practical implications:

1. For sensitive data search, Glean's permission inheritance is the safer architecture. If your blast radius concern is "the wrong person sees the wrong doc," Glean is built for it.
2. For action-taking, Viktor's review-first default is the safer architecture. If your concern is "an AI sends the wrong email or moves the wrong record," review-first is the answer. The Chevrolet $1 Tahoe and Air Canada bereavement-refund incidents both happened because the AI acted without a human checking first. Review-first prevents that class of failure by design.

Where each one still breaks

Glean's limits. It is enterprise-priced, contract-driven, and built for large rollouts. Smaller teams (under 100 people) often find it heavyweight for their needs. The action layer (Glean Agents) is newer than the search layer and has fewer write capabilities than dedicated action platforms. If your problem is "we already know the answer, we just cannot get the cross-tool work done," Glean is the wrong layer.

Viktor's limits. Viktor is a coworker, not a search index. If your top problem is "fifteen thousand employees cannot find anything in our 800,000-document knowledge base," Viktor is not the right shape. We can search across connected tools, but we are not a permission-aware enterprise search index with a knowledge graph. Glean is. Pick the right tool for the right shape of problem.

Where each one fits in the stack

A common pattern at enterprise scale:

• Glean at the knowledge layer. Search, answer, citation. The "where is it" engine.
• Viktor at the operator layer. Pull, reconcile, draft, deliver. The "ship the work" engine.
• Slack as the surface where both surface their output for human review.

If you are a smaller team (10 to 200 people) and you only have budget and attention for one bet, the question is "is our pain in finding things, or in finishing things?" In our experience, most teams under 200 people are bottlenecked on finishing, not finding. Viktor is the move. At larger scale where both pains are real, both tools earn their seats.

Frequently Asked Questions

Is Viktor a Glean alternative?

Not strictly. Viktor solves a different shape of problem (action and deliverables) than Glean (search and answers). For enterprise knowledge discovery at scale, Glean is the better fit. For cross-tool operator work that ends in a deliverable, Viktor is the better fit. Many companies run both.

Can Viktor search across all our internal tools the way Glean does?

Viktor can search across connected tools (Slack, Notion, Google Drive, HubSpot, Linear, etc.) as part of doing real work. It does not maintain a permission-aware enterprise knowledge graph the way Glean does. If pure search at enterprise scale is the goal, Glean is purpose-built for it.

Can Glean take actions in our tools the way Viktor does?

Glean Agents added action capabilities in 2024 and the surface is expanding. It is still anchored to retrieval as the design center. For broad write actions across 3,200+ tools with real OAuth, Viktor is built for that from the start.

We have both Slack and Microsoft Teams. Does Viktor work in both?

Yes. Viktor is native to both Slack and Microsoft Teams. The same coworker, surfaced in whichever channel your team uses.

How does review-first work in practice?

For every action that touches a real system (sending an email, updating a CRM record, opening a ticket, moving money), Viktor drafts the action in Slack, shows you the exact payload, and waits for an explicit approval before executing. You can configure auto-approve for a class of actions (for example, internal Slack posts) but the default is human-in-the-loop.

What is the typical deployment time for Viktor?

A team can be running their first useful workflow inside an hour. Add Viktor to a Slack channel, connect Stripe and HubSpot, ask it to draft a Monday revenue digest. The first few weeks are about discovering which workflows pay back fastest. Most teams have three to five recurring crons running by week two.

Which one should a 50-person company pick?

Most 50-person companies are bottlenecked on finishing, not finding. The cross-tool revenue digest takes someone 90 minutes every Monday. The customer support context-gathering eats 8 minutes per ticket. The pipeline reconciliation does not happen at all. Viktor is built for that shape of problem at that scale. Glean tends to make sense at larger sizes where the search problem is real on its own.