Multiplayer AI
AI arrived for individuals. Work is done by teams. Multiplayer AI closes that gap: one place where a team, its knowledge and its AI agents work together, under rules the company sets, on whichever models the team chooses.
Free 25 users · $500 in credits · No credit card required
Multiplayer AI is AI that a team uses together rather than one person at a time. The conversation is shared, the knowledge behind it belongs to the team, the AI agents in it are members with names and permissions, and the company can see and govern all of it.
Single-player AI is the opposite, and it is what almost every company runs today. One employee, one chat window, one tool. The work happens, and then it disappears.
The distinction is not about features. It is about where the AI sits. In single-player AI the unit is the prompt, so what you get depends on how well one person writes one. In multiplayer AI the unit is the team, so what you get depends on what the team already knows and has already built.
The problem
This is what single-player AI costs, and it does not show up on any invoice. Four people spend an hour each getting to roughly the same place. None of them sees the others. The best of the four answers stays with whoever wrote the best prompt.
The shift
Where the AI sits
A private window
The room where the team works
What you get depends on
How well you prompt
What the team knows and has built
The best work
Stays with one person
Becomes the team's
Agents
Tools someone runs
Members with a name, a job and a permission set
Company knowledge
Every tool starts from zero
Accumulates in one place and works with any model
A new joiner
Starts from nothing
Reads a month of the team's real reasoning on day one
What IT sees
Nothing, or blocks everything
Everything, and can say yes
Models commoditize. Every frontier lab is months from every other one and the gap is closing. A team's shared context, the agents it has built, and the trust that lets a company say yes to all of it do not commoditize. That is the layer worth owning.
Collaborative AI
Four things, and a shared transcript is only the first. A container the team works in, knowledge the team owns, agents that are members rather than tools, and governance that lets the company allow all of it.
Letting colleagues read each other's chat history is not collaboration, it is surveillance with extra steps. A Space holds the team's threads, its knowledge and its agents together, so the next question starts from what the team already established rather than from nothing.
An agent with a name, a job and a permission set is a teammate. A prompt somebody pastes around is not. Mention it like a colleague, let it run on a schedule, and give it a named manager who is accountable for what it does.
Files posted in a thread and outputs the team pins become part of what the Space knows, automatically. Members bring in the systems the work lives in. Nobody files anything twice, and nothing useful depends on one person remembering to share it.
The reason companies block AI is that they cannot see it. Nothing trained on your data, PII redacted before every model call, every message and agent action and retrieval on the record. Remove the blindness and the answer changes.
Diagnosis
None of these show up as a line item. All of them are expensive.
Two people on the same team have solved the same problem with AI and neither knows the other did.
Your best AI user is a bottleneck, because everyone forwards them things to "run through the AI".
A new joiner has no way to see how the team actually reasons, only the documents that survived.
Nobody can tell you what your company spends on AI, or which team spends it.
A departing employee takes their prompts, their context and their AI workflow with them.
Security cannot answer whether a customer contract has ever been pasted into a chat window.
of employees already use unsanctioned AI tools. IBM, 2025
additional cost of a shadow AI breach. IBM, 2025
By function
The pattern repeats everywhere: the work happens in a thread the team can see, an agent does the part that should be automatic, and the output stays where the next person will find it.
A PR Review agent in GitHub. A Debug agent across logs and Jira. Incident threads where the team and its agents work the same problem in one place instead of five DMs.
A Deal Prep agent pulling account history from Salesforce. A weekly pipeline post that appears in the thread on its own. Competitor reads the whole team can see.
A Content agent running your brand voice as a Skill. Campaign threads, translation, drafts the whole team builds on.
A Policy agent over the handbook. A Contract Review agent over SharePoint, flagging risk, with every action logged.
A Ticket Triage agent across ServiceNow and Confluence. Escalation threads where the answer stays visible to the next person who hits the same issue.
The finance analyst who never writes a good prompt still gets the team's agents, the team's knowledge and the team's working patterns.
The obvious objection
No, and the difference decides whether anyone keeps using it after week two. A shared transcript lets you read what a colleague asked. It does not make their result usable, it does not let an agent act on it, and it does not give IT anything they did not have before.
Multiplayer AI means the output becomes knowledge the next question is grounded in. It means the agent your colleague configured is now a member of the Space you both work in, with its own permissions and its own accountable manager. It means the thread, the knowledge and the agents move together, and the company can see the whole thing.
Read-only visibility into someone else's chat is a feature. A container the team actually works in is a different product.
In practice
A pre-opening cognitive city development in Florida, fourteen districts, built by a confidential enterprise, reached a 2-week median team adoption, department to daily active use, via Slack and Teams. It now runs 14 agents in production across finance, legal, procurement, investor relations and program management.
Two weeks matters more than it looks. Single-player tools roll out for months and still serve the same handful of power users, because every person has to discover their own value from scratch. When the value is already in the Space, the next member inherits it.
Department to daily active use.
Across five functions.
Governance shipped first.
FAQ
AI that a team uses together rather than one person at a time. The thread is shared, the knowledge belongs to the team, AI agents participate as members with names and permissions, and the company can govern all of it. The contrast is single-player AI: one employee, one chat window, one tool, no record.
They describe the same shift. Collaborative AI is the broader term for AI more than one person works with. Multiplayer AI is the sharper version: not just several people able to use the same tool, but a shared container where the team's threads, knowledge and agents live together, so the next person inherits what the last one built.
No, and it makes them better. Connect the Company Brain over MCP and Claude, ChatGPT, Copilot or Claude Code answer from your company's knowledge instead of the public internet, with each person getting back only what they were already entitled to. Separately, route their model traffic through OrgLogic and those calls get an audit trail, PII redaction before the model, a per-agent credential revocable in one click, and a budget cap that stops spend. Two different things, and you can take either.
Every frontier model, switchable mid-thread, with smart routing picking per task. Bring your own keys for anything, including pre-purchased Azure or Anthropic capacity.
Yes. A team lead signs up free, creates a Space and connects their own sources. No admin gate, and governance is already on.
Nothing appears in a Space unless the Space was granted it or a member deliberately put it there. A person's own connections and the knowledge created through them stay at user level in their Private Space, and sharing something out is an explicit act that is attributed and logged.
Knowledge sits at three levels: yours in your Private Space, the team's in each Space, and the company's across the organization, with OrgLogic controlling access at each level. Thread files and pinned outputs become part of what the Space knows automatically.
Knowledge synced through their connection disconnects from the Space and any member reconnecting the same source restores it. Thread files, pinned outputs and everything native to the Space stay. The team's working knowledge does not walk out of the door.
$0 up to 25 users, $8 per seat per month annually or $10 monthly on Standard, custom on Enterprise. Model usage separately: our keys at cost plus 6%, or your own at zero surcharge.
An AI workspace is a single environment where a whole company works with AI, instead of every team buying its own tool. One place to reach multiple models, answers grounded in the company's own documents and systems, and administrative control over what AI can see and do.
The phrase gets used for several different products, so the useful question is not what an AI workspace is in general. It is which kind you need.
Most tools that call themselves AI workspaces consolidate documents and tasks. An AI workspace built for a business consolidates something harder: models, knowledge, and control.
AI built into documents, task boards, and team chat. The AI drafts, summarizes, and automates inside the tools your team already uses to plan work.
Best when the problem is document and project overhead.
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Environments where teams label and prepare training data for machine learning.
Best when you are building models, not using them.
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One governed place for all AI use across a company. Multiple models, retrieval over internal knowledge, agents that act in business systems, and administrative controls over data, access, and spend.
Best when AI has spread across the company with no oversight. This is the category OrgLogic is in.
Hosted environments with the compute and tooling to develop and run AI models.
Best for data science and ML engineering teams who need infrastructure.
Use a consumer AI assistant. You need one model you like and a good interface. Governance, permissions, and per-seat economics are not your problem yet.
Look at a productivity workspace with AI built in. Your problem is coordination overhead, and the AI is there to reduce it.
The requirements change completely. People will want different models for different work. Answers have to come from company knowledge, not the public internet. IT and security need to see what AI is doing with company data. Finance needs the cost to survive a company-wide rollout.
Engineers, lawyers, and marketers do not want the same model. A business AI workspace gives access to every major model, lets people switch mid-conversation, and routes automatically to the right model for the task. One vendor's assistant can only ever offer one vendor's model. It should also govern the AI tools your teams already refuse to give up, rather than asking you to rip them out.
Not a feature you upgrade to. Bring your own API keys so data flows directly to model providers and OrgLogic never sees it. A full audit trail of every chat and agent action, searchable and exportable. PII redaction before anything reaches a model. Permissions set per agent, per connection, revocable in seconds.
Generic AI guesses about your business. A business AI workspace retrieves from your own documents and systems and cites the source document and how fresh it is, and tells you it does not know when retrieval comes back thin. An answer you cannot trace is an answer you cannot use.
AI tools priced at $25 to $60 a seat are affordable for a pilot team and painful at a thousand people. Model usage billed at cost, and a seat price that does not force you to ration access to the people who need it.
Every major model in one place. Switch models mid-conversation from a dropdown, or let smart routing pick the right one. Upload files for analysis, search the web with clickable citations, generate images, save prompts to a personal or shared library, and export any conversation to PDF or Markdown. Available on web, Slack, Microsoft Teams, and Chrome, so people use AI where they already work.
The grounded knowledge layer that Chat and Agents draw on inside OrgLogic. Knowledge bases hold uploaded files, websites, pasted text, and content pulled from connected systems, shared privately, with named people, or across the workspace. Personal knowledge syncs under each person's own credentials, so the index cannot contain anything they could not already access. Indexes refresh continuously, so answers reflect current state rather than the last sync, and every answer cites its source and when it was last synced.
AI workers built for a specific job and deployed to a team. Build one with a no-code builder, or start from a template for PR review, deal prep, ticket triage, contract review, onboarding questions, or policy lookup. Run them on demand, on a schedule, or triggered by an event like a new Jira ticket or a Salesforce stage change. Agents act in Salesforce, Jira, Confluence, ServiceNow, GitHub, SharePoint, Notion, Google Workspace, Slack, SAP, and any REST API. Every agent carries its own permissions and its own audit trail, so you can answer which agent accessed which record, for which user, at which time.
Default on every plan, including Free. BYOK at zero surcharge. Full audit trail across chat and agents. PII redaction applied before the model sees anything. Model availability controls per workspace and per team. Per-team budget controls with alerts and hard caps. Cost analytics by team, user, model, and agent. Configurable retention, complete isolation between workspaces, TLS 1.3 in transit, AES-256 at rest, and a contractual guarantee that customer data is never used for training.
Yes. OrgLogic is free for up to 25 users, with no seat minimum and no card required.
The Free plan is not a governance-free trial. BYOK, full audit trail, PII redaction, guardrails, per-agent permissions, cost analytics, and data retention controls are all included, along with the web app, Slack, Teams, and Chrome.
It also includes the part most teams assume is enterprise-only: the AI tools your people already use, including Claude, ChatGPT, and Copilot, can be routed through OrgLogic so their traffic inherits the same governance. You can see and control AI across your company before you have paid anything.
Every active user adds $20 to a shared credit pool for model usage, up to $500 at 25 users. Credits do not expire, and they carry over if you convert to a paid plan.
* One vendor's models
* $25 to $60 a seat
* Usage bundled, not itemized
* No bring-your-own keys
* Grounding limited to connected apps
* Cannot govern your other AI tools
* Usually one embedded model
* Bundled into the suite price
* Usage bundled, not itemized
* No bring-your-own keys
* Grounding limited to the suite's own content
* Cannot govern your other AI tools
* Every major model, switchable mid-chat, smart routing
* $8 a seat annual, $10 monthly
* BYOK at zero surcharge, or at cost plus 6%
* Bring your own keys on every plan
* Company Brain, with source and freshness on answers
* Governs Claude, ChatGPT, and Copilot through the gateway
* Free for 25 users, full governance included
Where does our data go?
With BYOK, you connect your own provider API keys and your data flows directly to the model providers. OrgLogic never sees it. Keys are configured by an administrator, so end users are never asked to bring their own. Nothing is used to train any model, contractually. Traffic is encrypted with TLS 1.3, storage with AES-256, and workspaces are completely isolated from one another.
What can we prove afterward?
Every chat and every agent action is logged, searchable, and exportable. Agent logs record which agent accessed which system objects, for which user, at which time. PII is detected and redacted before a prompt reaches a model, with the rules under your control. Retention is administrator-configurable.
A publicly traded autonomous vehicle technology company with roughly 1,500 employees consolidated 12 AI tools into OrgLogic, cut AI spend 70%, and reduced shadow AI by 91%, with engineers adopting within 2 weeks.
Their engineering teams had been running AI on personal accounts, with proprietary algorithms and sensor data going into tools nobody was tracking. BYOK kept that data inside their own environment. Smart routing let engineers pick the right model per task. Slack and Chrome drove the adoption.
Across Troopr Labs, the company behind OrgLogic, the platform runs in 600+ enterprise deployments, including Snowflake, Spotify, Rakuten, Snap, Delivery Hero, Wayfair, Aptean, and Cubic.
$0
Up to 25 users, no seat minimum. Full governance, including routing your existing AI tools through the gateway. $20 in model credits per active user, pooled, up to $500.
$8 per seat / month
Billed annually, or $10 monthly. Unlimited users, no seat minimum. Adds API and SDK access.
Custom
100 seat minimum. Adds SSO, SCIM, domain verification, single-tenant VPC, and on-premise deployment
An AI workspace is a single environment where a company works with AI instead of each team buying its own tool. It typically combines access to AI models, retrieval over the company's own documents and systems, and administrative control over data, access, and cost.
Is there a free AI workspace? / OrgLogic is free for up to 25 users with full governance included, no card required, and $20 of model credits per active user up to $500.
OrgLogic is free for up to 25 users with full governance included, no card required, and $20 of model credits per active user up to $500.
No. Google Workspace with Gemini embeds AI into Google's own productivity apps. A business AI workspace sits above your tools rather than inside one suite, gives access to models from multiple providers, and governs AI use across the company. Many OrgLogic customers use both, with Google Workspace connected as a source of company knowledge.
Those are single-vendor assistants: one provider's models, usage bundled into the seat price, typically $25 to $60 a seat. An AI workspace gives you every major model at $8 a seat with usage priced separately. You also do not have to choose. Claude, ChatGPT, Copilot, and Claude Code can be routed through OrgLogic on every plan, including Free, so they keep working while their model traffic inherits your governance.
In OrgLogic, permissions attach to connections and agents rather than to a single global setting. A person's own index is built under their own credentials, so it cannot contain anything they could not already access. Knowledge bases are shared privately, with named people, or workspace-wide. Each agent gets a read or write scope per connected system, and access can be revoked in seconds.
With BYOK, your data flows straight to the model providers and OrgLogic never sees it. PII is redacted before any prompt reaches a model, everything is logged and exportable, and OrgLogic is SOC 2 Type II and ISO 27001 certified, with HIPAA BAA and GDPR support. Enterprise adds single-tenant VPC and on-premise deployment.
OrgLogic is $0 up to 25 users, $8 per seat per month annually or $10 monthly on Standard, and custom on Enterprise. Model usage is billed separately: your own keys at zero surcharge, or OrgLogic-provisioned at cost plus 6%. Single-vendor assistants typically run $25 to $60 a seat with usage bundled and not itemized.
Yes, on every plan including Free, at zero surcharge. An administrator configures the keys once and data flows directly to the model providers. End users are never prompted to connect keys of their own.
A team can sign up and be working the same day. One publicly traded autonomous vehicle technology company with roughly 1,500 employees had engineers adopting within 2 weeks. Enterprise rollouts with SSO, SCIM, and a single-tenant deployment take longer and come with an implementation team.