AI workspace
Every frontier model, your company's knowledge, agents that act in your systems, and the governance IT needs. One place your teams work together instead of every department buying its own tool.
Free 25 users · $500 in credits · No credit card required
An AI workspace is a single environment where a whole company works with AI instead of every team buying its own tool. It combines access to multiple models, answers grounded in the company's own documents and systems, agents that act in business software, and administrative control over data, access and cost.
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. And because work is done by teams rather than individuals, the ones that get adopted are shared. A workspace where each person holds a private conversation with AI is a chat tool with an admin panel attached.
Why teams, not individuals ->Types
There are four: productivity workspaces with AI built in, data annotation environments, business AI workspaces that govern AI use across a company, and GPU and cloud compute workspaces for building models.
Type 1
AI built into documents, task boards and team chat. It drafts, summarizes and automates inside the tools your team already uses to plan work.
Best when the problem is document and project overhead.
Type 2
Environments where teams label and prepare training data for machine learning.
Best when you are building models, not using them.
Type 3
One governed place for all AI use across a company: multiple models, retrieval over internal knowledge, agents that act in business systems, shared team surfaces, and administrative control over data, access and spend.
Best when AI has spread across the company with no oversight. This is the category OrgLogic is in.
Type 4
Hosted environments with the compute and tooling to develop and run AI models.
Best when a data science team needs infrastructure.
Choosing
Three questions settle it: whether it is for you alone, for one small team working mostly on documents, or for a business where more than one team is already using AI.
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. The work has to be visible to the team rather than trapped in individual chat histories. IT and security need to see what AI is doing with company data. And the cost has to survive a company-wide rollout.
Requirements
Five things: put AI where the team works, offer every frontier model rather than one vendor's, govern by default rather than as an upgrade, ground answers in the company's own knowledge, and cost something that survives a company-wide rollout.
People and agents in the same threads, on knowledge the team owns, so one person's result is reusable by everybody. If value only accrues to whoever writes the best prompts, adoption stops at those people and the rollout stalls. This is the requirement most tools in the category skip, and it is the one that decides whether a pilot becomes a deployment.
Engineers, lawyers and marketers do not want the same model. A business AI workspace gives access to all of them, lets people switch mid-conversation, and routes automatically per task. It should also work with the AI tools your teams already refuse to give up, rather than asking you to rip them out.
Not a feature you upgrade to. Nothing trained on your data, contractually. PII redacted before anything reaches a model. A full audit trail of every message, agent action and retrieval, searchable and exportable. Permissions set per agent and per connection, revocable in seconds. And a boundary that matches how the company is organized, so a team's knowledge is scoped to that team.
Generic AI guesses about your business. A business AI workspace retrieves from your own documents and systems, cites the source and how fresh it is, and tells you it does not know when the answer is not there. An answer you cannot trace is an answer you cannot use.
Tools priced at $25 to $60 a seat are affordable for a pilot team and painful at a thousand people. You want a seat price that does not force you to ration access, and a usage layer you can actually see.
How OrgLogic works
On every plan including Free. API and SDK from Standard.
A Space holds the team's threads, its knowledge bases and its agents. People and agents post in the same threads, so an answer is visible to everyone and the next person picks up where the last one stopped. Pin an output or add an agent and it is there for the whole Space. Spaces are open so anyone in the company can find and join, or invite only. Link one to a Slack channel and the conversation happens in either place. See Spaces
Add an agent for PR review, deal prep, ticket triage, contract review or policy lookup, or describe what you need and confirm what gets assembled. Run them on demand, on a schedule, or on an event like a new Jira ticket. Agents act in Salesforce, Jira, Confluence, ServiceNow, GitHub, SharePoint, Notion, Google Workspace, SAP and any REST API, and each one carries its own permissions, a named accountable manager and its own audit trail. See AI Agents
Switch models mid-conversation, or let smart routing pick per task. Upload files, search the web with citations, generate images, export any thread. On web, Slack, Microsoft Teams and Chrome. See AI Chat
Nothing trained on your data, contractually. PII redaction before every model call. A full audit trail across threads, agents and retrievals. Model availability per organization and per team. Budget caps that stop spend, and cost analytics by team, user, model and agent. TLS 1.3, AES-256, complete isolation between organizations, configurable retention. See governance
Knowledge sits at three levels: yours in your Private Space, the team's in each Space, the company's across the organization, with OrgLogic controlling access at each one. Every answer cites its source and when it was last synced. Connect it to Claude or ChatGPT over MCP and those tools answer from your knowledge too, scoped to each person. See the Company Brain
Multi-step automations on a node canvas, triggered on a schedule or an event. A step can run an agent, and that agent's own permissions and manager still apply.
Comparison
Models
One vendor's
Usually one embedded model
Every frontier model, switchable mid-thread, smart routing
Seat price
$25 to $60
Bundled into the suite
$8 annual, $10 monthly. Free to 25 users
Usage cost
Bundled, not itemized
Bundled, not itemized
Separate and itemized. Our keys at cost plus 6%, or yours at zero surcharge
Grounding
Limited to connected apps
Limited to the suite's own content
Company Brain over your systems, with source and freshness on every answer
Shape
Built around one person's conversation
AI assists a document, not a team
Shared Spaces where people and agents work together
Agents
Limited or none
Limited or none
Named members with per-connector scope and an accountable manager
Governance
On higher tiers
Inherited from the suite
Full, on every plan including Free
Your existing AI
Replaces it
Separate from it
Grounds it over MCP, and governs its model traffic through the gateway
Security
It depends on three things: whether your data trains anybody's model, whether sensitive data reaches the model at all, and whether you can prove afterwards what happened. A business AI workspace should answer all three without you asking.
Nothing is used to train any model, contractually. Data sent to model providers goes by API only, is not retained beyond the time needed to answer, and every provider is under a DPA prohibiting use for training. Traffic is encrypted with TLS 1.3, storage with AES-256, and organizations are completely isolated from one another. If you would rather route through your own provider accounts, bring your own keys at zero surcharge.
PII is detected and redacted before any prompt reaches a model, on every call from every Space and every agent, with the rules under your control. Guardrails and content policies are set organization-wide.
Every thread message, agent action and Brain retrieval is logged with the Space, thread, actor, agent, its accountable manager, model, sources and cost. That includes retrievals made from a third-party AI client. Searchable, exportable, with administrator-configurable retention.
SOC 2 Type II
ISO 27001
GDPR
HIPAA BAA
VPC and on-premise on Enterprise
In practice
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. Smart routing let engineers pick the right model per task, and Slack and Chrome drove the adoption.
Pricing
OrgLogic is $0 for up to 25 users, $8 per seat per month on annual billing or $10 monthly, and custom on Enterprise. Model usage is billed separately: our keys at cost plus 6%, or your own at zero surcharge.
Free
Up to 25 users, no seat minimum, no card.
Unlimited Spaces, threads and agents
Full governance
$500 in pooled model credits
Standard
seat / mo
Up to 25 users, no seat minimum, no card.
Everything in Free
API and SDK
Enterprise
100 seat minimum.
SSO, SCIM, domain verification
Single-tenant VPC and on-premise
FAQ
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, shared surfaces where a team and its agents work together, and administrative control over data, access and cost.
Four: productivity and project management workspaces with AI built in; data annotation and labeling environments; business and enterprise AI workspaces that govern all AI use across a company; and GPU and cloud compute workspaces for building and running models. OrgLogic is in the third category.
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 customers use both, with Google Workspace connected as a source of company knowledge.
Those are single-vendor assistants built around one person's conversation: one provider's models, usage bundled into the seat price, typically $25 to $60 a seat. An AI workspace gives you every frontier model at $8 a seat with usage priced separately, and puts the work in shared team surfaces. You also do not have to choose. Connect the Company Brain to them over MCP and they answer from your company's knowledge, and route their model traffic through OrgLogic and it inherits your governance.
One where a team's threads, knowledge and AI agents live in the same place, rather than each person holding a private conversation with a model. The practical difference is reuse: one person's result, prompt or agent is available to everyone on the team, so value does not depend on who happens to be good at prompting.
Yes. A thread inside a Space is shared. People and agents post in the same conversation, agents answer when mentioned, and scheduled agent runs post into the thread where the team is already reading.
In OrgLogic, knowledge sits at three levels. Yours, in your Private Space, where your own connections and what they bring in stay until you share them. The team's, in each Space, reachable by every member of that Space. And the company's, attached by an admin to the Spaces that need it. OrgLogic controls access at each level.
An agent belongs to the Spaces it is added to. It draws on what those Spaces know, acts in the systems it has been scoped to, and can be mentioned by anyone in the Space. Its permissions, its accountable manager and its audit trail belong to the agent, not to whoever happens to be talking to it.
Nothing is used to train any model, contractually, and data sent to providers is not retained beyond the time needed to answer. PII is redacted before any prompt reaches a model. Everything is logged and exportable. OrgLogic is SOC 2 Type II and ISO 27001 certified, with HIPAA BAA and GDPR support, and 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: our keys at cost plus 6%, or your own at zero surcharge. Single-vendor assistants typically run $25 to $60 a seat with usage bundled and not itemized.
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.
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.