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OpenAI's latest upgrade to ChatGPT equips it with "company knowledge," enabling the AI to draw insights from internal tools like Slack and SharePoint, offering businesses more precise and contextually relevant advice.
October 23, 2025
OpenAI has introduced a significant update to ChatGPT, specifically for its Business, Enterprise, and Edu tiers. The new feature, called "company knowledge," integrates context from various internal tools to provide more accurate and relevant answers tailored to your business needs.
ChatGPT now has the capability to aggregate data from multiple sources within your organization, such as Slack, SharePoint, Google Drive, and GitHub. This integration is powered by a version of GPT-5 that's optimized for cross-referencing information across these platforms. Here’s what this means in practice:
For practitioners, this update means more efficient and accurate decision-making. Instead of manually sifting through various tools and documents, you can get a consolidated view directly from ChatGPT. This is particularly useful for tasks like preparing for client calls, where context often spans multiple platforms.
To use company knowledge, follow these steps:

Imagine you’re preparing for an upcoming client call. With company knowledge enabled, ChatGPT can:
This comprehensive briefing ensures you have all the necessary context at your fingertips, making your preparation more efficient and thorough.
One of the standout features of company knowledge is its ability to handle ambiguous situations. By running multiple searches and cross-referencing details, it can:
This capability makes company knowledge particularly valuable in dynamic work environments where information is constantly evolving.
If you’re on ChatGPT Business, Enterprise, or Edu, you can start using company knowledge today. Simply head to the message composer, tap “Company knowledge,” and follow the prompts to connect your apps.
For more detailed instructions and support, check out the OpenAI Help Center.
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Kai built ML infrastructure at a Bay Area startup before developing an obsession with transformer architectures and inference optimisation that eventually pulled him out of product work entirely. A stint at a compute research lab sharpened his instinct for what actually matters in a model release versus what is marketing. He writes from the inside — from the perspective of someone who has debugged the systems he is describing at three in the morning. He is allergic to hype and instinctively drawn to the unglamorous plumbing questions that everyone else skips over.
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