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.Your organization's knowledge is spread across dozens of systems that were never built to talk to each other. The company brain connects them into one place your AI can draw on.
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.