Best practices
Your AI is only as good as the data it can see
Ask an ungrounded model about your refund policy and it will describe a plausible refund policy, someone’s, just not yours. The single biggest upgrade most teams can make to answer quality is not a better model or a cleverer prompt. It is giving the AI access to the data the question is actually about.
The three layers of grounding
Grounding is not one feature; it arrives in layers. The first is manual: attach the document, paste the numbers. It works, but it makes every user responsible for finding the right context every time. The second is connected storage (Google Drive, OneDrive), where the file comes into the conversation without leaving governance behind. The third is live data feeds: real-time and batch pipelines from internal systems, so answers reflect what your organisation knows right now, not what someone remembered to attach. Each layer removes a step where context gets lost.
What changes when answers are grounded
The obvious change is accuracy: the model quotes your actual policy, your actual pipeline, your actual metrics. The subtler change is organisational. Grounded AI becomes the fastest way to look something up, so people stop interrupting the one colleague who knows where the document lives. Internal knowledge that used to be tribal, buried in drives and inboxes, becomes queryable by everyone who is entitled to see it. That last clause is where security enters.
Grounding without the security hole
Connecting company data to an AI tool is exactly the step that makes security teams nervous, and they are right to be. Three controls make it safe. First, access should be scoped and revocable: a connected drive is an OAuth grant you can withdraw, not a bulk copy of the folder. Second, permissions must carry through: a user asking through AI should see only what they could see directly. Third, guardrails and audit logs need to cover grounded conversations exactly as they cover plain chat, because sensitive data now flows through the answers. This is why grounding belongs in a governed workspace rather than in whichever browser extension asked for drive access first.
Where to start
Do not begin with a data-lake project. Pick the one corpus your team queries most (usually policies, product docs, or a shared drive of past work), connect it, and measure how often answers cite it. The pattern we see repeatedly: within weeks, grounded questions become the majority of usage, because answers about your own world are the ones worth asking. Generic questions have generic value; grounded ones compound.