Guru

What is Guru?

Guru is an AI knowledge assistant built around a company wiki. A worker asks a question in plain language, and Guru answers from content the team has verified, then shows the source card so the person can see where the answer came from. The aim is trusted answers on demand, so staff stop searching scattered documents or interrupting colleagues to find what they need.

Guru makes the product. The company focuses on company knowledge: storing it in a wiki, keeping each card current through a verification cycle, and delivering answers where people work. The AI assistant sits on top of that verified base, so answers reflect knowledge the team owns rather than guesses from the open web.

The audience is teams that field repeat questions and want consistent answers. Support, sales, operations, and people teams all fit, because each one relies on policies, processes, and product facts that change over time and need a single trusted source.

Key features

Guru centers on a set of features that turn a company wiki into a trusted answer engine:

• ▸Verified knowledge: each card carries a verification status and an owner, so staff know whether a piece of knowledge is current before they act on it.
• ▸Slack answers: people ask questions inside Slack and Guru replies in the same thread, so answers reach staff without a context switch.
• ▸Source cards: every answer surfaces the card it drew from, so a person can open the source and confirm the detail for themselves.
• ▸Company wiki: knowledge lives in a structured wiki with cards, collections, and tags, giving teams one place to write and organize what they know.
• ▸Verification workflow: owners are prompted to review cards on a schedule, which flags stale content and keeps the knowledge base current.
• ▸Knowledge analytics: usage data shows which questions come up, which cards get read, and where gaps sit, so teams know what to write next.

The verification model is central. Because each card has an owner and a review status, Guru can tell staff whether an answer rests on current knowledge or content that needs a fresh look. That trust signal is what separates a verified answer from a search result, and it is why teams can hand the assistant to staff and expect consistent replies.

How well does it work?

Guru performs well when a team keeps its wiki current. The verification workflow gives every answer a trust signal, and the source card lets staff confirm a detail rather than take the assistant on faith. Because it answers inside Slack, adoption tends to hold: people ask questions where they work, so the assistant fits into the day instead of adding another tab.

The limits track the content. Guru answers from what the team has written and verified, so a gap in the wiki shows as a gap in the answer, and a stale card can produce a stale reply until an owner reviews it. Standing up the initial knowledge base and assigning owners takes effort up front, and the verification cycle needs someone to own it over time. The assistant is strong on curated company knowledge, less suited to open-ended research beyond the wiki.

A practical way to raise Guru's value is to seed the wiki with answers to your highest-volume questions first, then assign each card an owner and a review date. Coverage of the questions staff ask most drives adoption faster than breadth for its own sake.

Guru pricing

Guru uses per-seat pricing that starts from $18 per user per month. The published rate covers the AI assistant, the wiki, verified knowledge cards, and Slack answers. Larger deployments move to a custom Enterprise quote that adds admin, security, and onboarding support.

The tiers below describe how a deployment is scoped:

The per-seat model favors teams rolling Guru out to staff who ask and answer questions each day, since value grows as more people rely on a shared, verified base. Because the rate scales with seats, budget the total against the time your teams lose to searching and re-answering the same questions today, and ask how seats are counted and what the Enterprise tier adds before you commit.

Who should use Guru?

Guru fits teams that field repeat questions and want trusted, consistent answers. It suits these groups in particular:

• ▸Support teams that answer the same product and policy questions and need current, verified replies to stay consistent.
• ▸Sales teams that pull pricing, positioning, and competitor facts mid-conversation and cannot afford stale details.
• ▸Operations and people teams that maintain policies and processes that change and require one trusted source.
• ▸Companies that run on Slack and want answers to reach staff in the tool they use, without a separate portal.

Guru is a weaker match for teams with little written knowledge to draw on, since the assistant answers from the wiki, and for organizations that cannot commit someone to own the verification cycle that keeps cards current.

Alternatives and how it compares

Guru competes with a field of knowledge and workplace assistant tools. The right comparison depends on how much you value verified, curated knowledge against broad search across every app.

• ▸Glean: an enterprise assistant that searches across every connected app and answers with citations, a fit for organizations that want breadth over a curated wiki.
• ▸Notion: a workspace and wiki with AI features, strong for teams that want one place to write and organize, with lighter verification than Guru.
• ▸Slack AI: native search and summaries inside Slack, useful for finding past messages, though it lacks Guru's verified card model.

Guru's edge is verified knowledge paired with a source card on every answer, delivered where staff already work. If your priority is trust and consistency from a curated base, Guru is built for that. If you need to search across many disconnected apps, a broad enterprise assistant may cover more ground, so weigh curation against reach before you decide.

Limitations and getting started

Be honest about the trade-offs before you commit. Guru answers from your wiki, so gaps and stale cards show up as weak answers until an owner reviews them, and the verification cycle needs someone to own it over time. Per-seat pricing scales with headcount, which adds up for large teams, and the assistant is built for curated company knowledge rather than open-ended research beyond the wiki.

Getting started follows a clear path:

1. 1List the questions your team answers most and rank them by volume and impact.
2. 2Seed the wiki with cards that answer those top questions, and assign each card an owner and a review date.
3. 3Connect Guru to Slack so staff can ask questions and get answers in the tools they use.
4. 4Pilot with one team, watch which answers land, and use analytics to spot gaps and missing cards.
5. 5Widen the rollout to more teams as coverage and answer quality hold up, keeping the verification cycle on schedule.

A staged rollout keeps risk low: start with your highest-volume questions and one team, confirm that answers are current and trusted, then add cards and users as the numbers hold. Because Guru's value grows with a well-kept wiki, early wins on common questions build the case for a wider rollout.