You can give ChatGPT or Claude access to company knowledge in four ways: upload files to a project, connect the apps where your knowledge lives, connect a custom tool through MCP (Model Context Protocol), or build your own retrieval system. For most small and mid-sized teams, projects are the quickest start and MCP or built-in connectors are the durable answer, because the AI then reads current knowledge instead of a copy that goes stale.

Why the AI doesn't know your company

ChatGPT and Claude are trained on public data. They don't know your clients, prices, processes or what your team decided last week. So every conversation starts with copy and paste, and answers drift toward the generic.

Many people work around this on their own. Microsoft found that 78% of AI users bring their own AI tools to work, and 80% at small and medium-sized companies (Microsoft Work Trend Index, 2024). That usually means company knowledge is pasted into personal chats with no structure, no permissions and no way to keep it current. The goal of this guide is to replace that with something deliberate.

Option 1: Projects (fastest start)

Both assistants let you group chats into a project with shared files and instructions.

ChatGPT Projects. A project has a Sources section for files and connected context that every chat in the project can use, plus project instructions that apply to all of its chats (OpenAI, ChatGPT docs). File limits depend on your plan.

Claude Projects. Each project has its own knowledge base and custom instructions. On paid plans, when project knowledge gets close to the context limit, Claude switches to a retrieval mode that expands capacity by up to 10 times. On Team and Enterprise plans, projects can be shared with specific people or the whole organization, with viewer and editor roles (Anthropic, Claude Help Center).

Good for: a defined body of knowledge such as a pricing guide, brand rules or a sales playbook. Limits: files are copies. When the original changes, someone has to re-upload it. Projects also don't know your current tasks or who is working on what.

Option 2: Built-in connectors to your work apps

Both assistants can connect to common work apps so they read the source directly.

ChatGPT offers a "company knowledge" mode that searches connected work apps and tools and answers with citations to the source (OpenAI, Developer docs). Check OpenAI's help center for which plans include which connectors.
Claude offers connectors to popular apps and lets you add custom connectors; on Team and Enterprise plans, owners add a connector for the organization and each member then connects with their own account (Anthropic, Claude Help Center).

Good for: knowledge already well organized in Google Drive, SharePoint, Slack, Notion or similar tools. Limits: the AI can only be as good as the source. If the answer is buried in a 40-page document or split across five chat threads, search won't fix that.

Option 3: MCP (Model Context Protocol)

MCP is an open standard for connecting AI applications to external systems: data sources, tools and workflows. The official documentation compares it to a USB-C port for AI applications (Model Context Protocol docs). Anthropic introduced it in November 2024 because even strong models were cut off from data locked in separate systems, with each source needing its own integration (Anthropic, 2024). Today both Claude and ChatGPT can connect to MCP servers.

How it works in practice:

The tool that holds your knowledge offers an MCP server (a URL).
In Claude, you add it as a custom connector; in ChatGPT, you add it as a custom MCP plugin. Both support OAuth sign-in, so the assistant doesn't need your password.
The assistant can now search, read and, if allowed, write to that tool from any chat.

For ChatGPT's search features, OpenAI recommends that an MCP server offers a read-only search tool and a fetch tool, and ChatGPT shows citations for results that include a URL. Write actions need manual confirmation in the conversation (OpenAI, Developer docs).

Good for: keeping the AI connected to live knowledge and current work, and using the same knowledge from several assistants. Limits: you rely on the quality of the MCP server. Both vendors warn that they don't verify third-party servers, and that prompt injection is a real risk. Connect only servers you trust, and switch off tools that write data unless you need them (Anthropic, Claude Help Center; OpenAI, Developer docs).

More background: What Is MCP (Model Context Protocol)?

Option 4: Build your own (RAG)

Retrieval-augmented generation, or RAG, was described in a 2020 research paper: a language model retrieves relevant passages from an index and grounds its answer in them, which made answers more factual and specific (Lewis et al., 2020). You can build this yourself with a vector database, embeddings and an API.

Good for: companies with developers, special data or strict control requirements. Limits: it's a software project. You maintain the indexing, permissions and quality yourself.

Comparison

	Projects	Built-in connectors	MCP	Own RAG
Setup time	Minutes	Minutes to hours	Minutes, if the tool offers a server	Weeks
Stays current	No (copies)	Yes	Yes	Depends on your pipeline
Respects permissions	Per project	Yes, per user	Depends on the server	You build it
Can take actions	No	Some	Yes, if allowed	You build it
Needs developers	No	No	No (to connect)	Yes

What makes the answers good

The connection is the easy part. Answer quality depends on the knowledge itself:

Short pages beat long documents. One question, one answer, linked to related pages.
Write down decisions with reasons. "We stopped offering discounts above 10% because…" is something an AI can use; a spreadsheet of past prices is not.
Keep knowledge next to the work. If the AI can also see goals, tasks and owners, it can answer "who is working on this?" and not just "what is our policy?"
Remove outdated content. An AI will happily quote last year's price list.

How to Get Knowledge Out of Employees' Heads covers how to build this up.

Security checklist

Use your company's business plan, not personal accounts, so admins control connectors.
Prefer OAuth connectors over shared API keys.
Give the AI read access first; add write access only where you need it.
Review which connectors are active every few months.
Tell the team which assistant is approved, so knowledge stops landing in personal chats.

How Orakis does it

Orakis keeps goals, tasks, team chat, documents and a linked knowledge base in one workspace, and offers an MCP connection so Claude, ChatGPT or any other MCP-capable assistant can work directly with it. That means the assistant reads current knowledge and current work, not uploaded copies. Inside Orakis, Alfred, the built-in AI agent, answers questions from the same knowledge and can create or update work, and leads decide how much it may approve on its own. Orakis is young and in closed beta; its integrations today are Google Calendar, Gmail, Microsoft Outlook and MCP-capable assistants.

FAQ

Is it safe to connect company data to ChatGPT or Claude? It can be, with care. Use a business plan so admins control which connectors exist, prefer connectors where each person signs in with their own account, connect only trusted MCP servers, and check each vendor's data terms.

Which is better for company knowledge, ChatGPT or Claude? Both support projects, connectors and MCP. Pick the one your team already uses and connect it to a single, well-kept source of knowledge.

Do I need a developer? Not for projects, built-in connectors or connecting an existing MCP server. You need one only to build your own RAG system or your own MCP server.

How to Give ChatGPT or Claude Access to Company Knowledge