Answers ranked by how your company actually works
Atolio's Collaboration Graph maps who works with whom, on what, across every system. More relevant context for each query means better, faster, more token-efficient answers on the first try.
Trusted by teams across leading enterprises
The Collaboration Graph is how Atolio's context layer knows what's relevant to each person
It maps who works with whom, and on what, across every connected system, and ranks context for the person or agent asking.
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Built from the work your teams already do
It builds itself from everyday work in every connected system, in four steps, and gets sharper the longer it runs.
Accounts in every system tie back to one person.
Accounts
@jlee in Slack, jordan.lee in Jira, J. Lee in Salesforce, jlee@acme in Gmail
Resolved by
Your identity provider
Everyday communication shows who works together.
Signals
Emails, direct messages, @-mentions, and comments
Weighted by
How often people work together, and how recently
What people do with each item ties them to their work.
Signals
Owns, edits, shares, is assigned, reviews, and posts in
Weighted by
Editing a ticket counts for more than viewing a document
Related work links up, and the graph keeps itself current.
Links
A deal to its channel, a ticket to its fix, a renewal to its legal thread, so related work surfaces before anyone asks
Over time
Ties strengthen with contact, fade when it stops, and shift when teams change
Same question, but two people means two rankings
Personalized search leans toward each person's own work, team, and collaborators, so the most useful item comes first for each of them.
What's the latest on the Henderson renewal?
Jordan Lee
Account exec · asking in
Claude
- 1
Henderson renewalOpportunityYou own it - 2
Revised terms v3Shared fileYou edited it, and Sam in Legal commented - 3
Legal hold on termsEmail threadRelated, not asked for: linked to your renewal - 4
#deal-hendersonChannelYou post here
Ana Reyes
Support · asking in
Copilot
- 1
SUP-412 SSO defectTicketAssigned to you - 2
auth-service fixPull requestReviewed by Raj, who you work with - 3Henderson renewalOpportunityLinked to your ticket
- 4
Henderson escalationTeams chatJordan @-mentioned you here
Find your subject-matter experts
People search ranked by real expertise: the graph shows who sits at the center of the work on any topic, so people and agents know who to go to.
- Ranked by real workWho writes, edits, reviews, and gets assigned the work, not profiles nobody updates.
- Ranked for youThe experts closest to you and your team come first.
- Get to the bottom of anythingOpen a document, ticket, or deal and see the people at its center, with their related work.
Who knows the most about SSO for Henderson?
Ranked for Jordan Lee
- SUP-412 SSO defectTicket
- Henderson renewalOpportunity
- auth-service fixPull request
SSO runbookConfluence
- Ana ReyesTop expertSupportAssigned the SSO defect, and works the Henderson account with youSUP-412Henderson renewal
- Raj KapoorProductReviewed the fix, and you DM oftenauth-service fix
- Lena OrtizSecurityEdits the SSO runbookSSO runbook
Better context for every agent, inside your perimeter
Every agent, in any AI tool, reaches the graph through MCP or API. On every request:
- Copilot
- Claude
ChatGPT
Gemini- Agents you build yourself
- Ranked for the person it works forAn agent working for Jordan gets results ranked for Jordan's work, so its answers fit what Jordan needs.
- Fewer tokensThe most relevant content comes first. Less noise means agents spend less time and burn less tokens answering a query.
- Never widens accessRanking changes what comes first, never what anyone can see. Permissions are checked on every query.
- Stays in your environmentThe graph is built and stored in your cloud or on-prem, under your keys.
Collaboration graph vs. knowledge graph
Both connect information, but an enterprise knowledge graph and a collaboration graph answer different questions. Atolio uses both: a document graph links related content, such as a ticket and its fix, and the Collaboration Graph ranks it for each person.
What it maps
Entities and facts, such as products and how they relate
How it's built
Designed and curated by a team
How it stays current
Updated when someone edits it
Access
Varies by product
Atolio's Collaboration Graph
What it maps
How people and their work connect
How it's built
Builds itself from everyday work
How it stays current
Strengthens, fades, and shifts as work changes
Access
Ranks only inside each person's permissions
Not all enterprise graphs are built the same
The differences show up in what feeds the graph, where it runs, and who can use it.
Compare Atolio with:
Graph inside one suite
Graph in a vendor's cloud
Atolio's Collaboration Graph
What feeds it
Mostly that suite's own apps
Connected apps, copied to the vendor
Every connected system, from Microsoft 365 to Slack, Jira, and Salesforce
Where it runs
The suite vendor's cloud
The vendor's cloud
Your cloud or on-prem, under your keys
Who can use it
That suite's assistant and agents
The vendor's assistant and agents
Any AI tool or agent, through MCP or API
Finding experts
Profiles and org charts
Varies
Who sits at the center of the work on a topic
FAQs
Collaboration Graph questions
AI can find subject-matter experts by looking at who actually does the work on a topic.
Most expert directories depend on profiles and skills lists that people fill in once and rarely update. Atolio takes a different approach. It looks at who writes, edits, reviews, comments on, and gets assigned the work in every connected system, and shows who sits at the center of that work on a given topic.
When you search for people on a topic, colleagues are ranked by how central they are to the work and how close they are to you and your team, with the documents, tickets, and deals that put them there. You can also start from the work itself: open a document, ticket, or deal, and see the people at its center and what else they're working on.
Because it's built from real activity, it stays current as people change projects and teams, with nothing for anyone to maintain. AI agents can use the same signals, for example to route a support question to the engineer who reviewed the fix. Everything respects permissions: you only see work you can already open, checked on every query.
A collaboration graph isn't the same as a knowledge graph; it maps how people and work connect.
A knowledge graph models entities and facts, such as products, accounts, and how they relate, and a team usually designs its structure and keeps it curated. A collaboration graph builds itself from everyday activity. Atolio's knows who emailed whom about a deal, who edited the revised terms, who was assigned the support ticket, and who reviewed the fix. Those ties strengthen, fade, and shift on their own as work changes, so nobody has to maintain them.
Atolio also keeps a document graph that links related content, such as an opportunity and its Slack channel, or a ticket and the pull request that fixes it. Together, the two let Atolio rank results by relevance to the person asking and surface related work they didn't know to look for.
Both sit on top of your permissions, which sync continuously from your source systems and are checked on every query, so neither can expose something a person couldn't already open. For most teams, the label matters less than the outcome: answers that fit the person asking, from every system they use.
No, personalized ranking can't expose content someone shouldn't see; it only reorders what they can open.
Permissions are applied before ranking. They sync continuously from your source systems and are checked on every query, against each person's direct, group, and nested-group memberships, before anything is retrieved. Ranking then happens only inside that permitted set. If a close colleague edited a document you can't open, that document never shows up in your results, however strong your tie to that colleague.
The same rule applies to AI agents. An agent acting for a person gets that person's permissions and that person's ranking, and a knowledge scope can narrow what it reaches even further. Restricted data stays off-limits to every agent until its owner approves access.
When access changes in a source system, the change syncs to Atolio in near real time, and the next query reflects it. The activity data behind the ranking, the index, and the permission data all stay inside your environment, under your identity and access management (IAM) policies and your encryption keys, and Atolio can't see any of it.
Yes, AI agents get personalized results, ranked for the person they're working for.
Copilot, Claude, ChatGPT, Gemini, and agents you build yourself connect to Atolio through the Model Context Protocol (MCP) or an application programming interface (API). When an agent acts for a person, Atolio ranks results toward that person's work, team, and collaborators, exactly as it would if they searched themselves. A sales assistant working for an account executive sees that rep's deals and threads first. A support agent working for an engineer sees the tickets and fixes closest to that engineer's work.
Ranking matters even more for agents than for people. An agent that gets the most relevant passages first needs fewer searches, fewer retries, and fewer tokens to reach a good answer, often on the first try. Without that ranking, an agent has to sort through the top result from every system itself, which costs time and tokens on every step.
Agents follow the same permission rules as people: an agent sees only what the person it works for can see, a knowledge scope can narrow it further, and restricted data stays off-limits until its owner approves access.
When someone changes teams, their rankings shift toward the new team's work over time.
A team move shows up in two ways. The org chart changes in your identity provider, and the person starts working with new people on new things. Atolio treats a team change as a strong signal, so results start leaning toward the new team's documents, tickets, channels, and people. Old ties fade rather than disappearing overnight, which keeps past context useful: if someone moved from support to product, their history with an account issue can still help when that issue comes back.
Nobody has to retag a profile, update a skills directory, or rebuild anything. The same is true for new hires: as they start working, their ties build from that activity.
Permissions are separate from ranking. When someone's access changes in a source system, the change syncs to Atolio in near real time and is checked on every query. Ranking never grants access; it only changes what comes first among the things a person can already open. AI agents working for that person follow the same updated ranking and permissions.