Add Nucleus to ChatGPT

You already use ChatGPT, and your work already lives in Drive. ChatGPT can read it, one source at a time, from inside its own walls.

ChatGPT reaches your files now. Built-in connectors let it read Google Drive, SharePoint, Dropbox and more, and custom MCP connectors let it reach almost anything. That solves reach. What it does not solve: connectors tend to read one source at a time and lean on keyword matching more than meaning, and ChatGPT only looks when you prompt it, so a question that spans your CRM and your docs and your database comes back partial. And every bit of that context is ChatGPT's alone. Your Claude and your Copilot cannot reuse it. The same holds if you put Gemini on the same Drive: native to Workspace, but locked to Google's ecosystem and to Drive alone. Whichever single assistant you pick, you are still reading one silo from inside one vendor's walls.

Every source is a separate connector ChatGPT reads on request. Nothing joins them, nothing checks the answer, and none of it carries over to your other tools.

By Nucleus Team

The architecture

Without the layer

ChatGPT

A connector per source

Drive, SharePoint, CRM (each separate, read on prompt)

Connectors tend to read one source at a time and lean on keyword matching, so cross-source questions come back partial.
ChatGPT reads only when you prompt it. It does not hold a live, grounded memory of your work.
The context is ChatGPT's alone. Your Claude and Copilot cannot reuse it.

With Nucleus

ChatGPT

One custom MCP connector to Nucleus

Nucleus joins Drive, CRM, Postgres, and docs into one grounded, weighted context

One connector. Nucleus joins every source into a single grounded, weighted context instead of a connector per source.
Semantic, not keyword. Nucleus reasons over meaning and returns the answer with its sources.
The same layer feeds ChatGPT and Claude and Copilot, so the context is not locked to one tool.

What stops a wrong answer becoming permanent

COVE, chain of verification

01Decomposesplit into atomic claims

02Retrieve per claimone claim at a time

03Verify alonejudged on own evidence

04Scoreper claim, not per answer

05Attributesources on, uncertainty shown

ChatGPT reads Nucleus through a custom MCP connector, and each grounded answer cites the Nucleus source it drew from.
supportedNucleus COVE run over this playbook's own workflow, August 2026confidence 94%

The command

Per prompt · type it in any chat

Answer using my joined context, not one connector at a time, and cite where each part came from.

Set once · settings → custom instructions

Ground every answer in my Nucleus sources and show the source for each claim.

Once it's in your settings, every task starts grounded. You can still say use Nucleus at Attio to point it at a specific source.

Give your AI the same context you have

Connect your sources once. Every workflow, agent and assistant reads the same grounded layer, and everything written back is checked before it lands.

Create your Nucleus

Terms

Context Layer: A context layer is the persistent, source-grounded organizational memory that sits between an organization's data and whichever model it uses, so every assistant and agent reads the same weighted, reconciled picture instead of re-deriving it per tool. It exists independently of any single agent or session. It is not a vector store, which retrieves passages without reconciling them, and not agent memory, which belongs to one agent and resets with it.
Understanding Layer: The understanding layer is Nucleus's name for the context layer it builds: the layer between organizational data and foundation models where scattered sources become one coherent, verifiable account of what an organization knows. Storage holds the data and the model does the reasoning; the understanding layer makes the two compatible, adding memory, relationships across sources, grounding, and verification. It is an implementation of the context layer category, not a competing concept.
AI Memory: AI memory is context that survives across sessions and across tools, so an assistant does not start from zero each time or relearn what another tool already knows. It is not a longer context window, which extends a single session rather than persisting anything, and not chat history, which replays what was said rather than retaining what was established. Memory for one agent is also not memory for an organization; the two are different layers of the stack.
Organizational AI Memory: Organizational AI memory is the persistent, permission-aware record of what an organization knows, decides, and corrects, held so any AI system can retrieve it at inference time. It is not a knowledge base, which stores content; memory also preserves who established what, when, and on what evidence. And it is not agent memory, which belongs to one agent and resets with it. Memory for one agent is not memory for an organization.
Grounding: Grounding is supplying a model with the sources it should rely on before it answers, so the output is constrained by evidence rather than by the model's guesswork. It improves what goes into generation; it does not test what came out, which is verification's job. The two are parallel disciplines, not interchangeable ones: a well-grounded system can still produce a fluent claim no source supports.
AI Provenance: AI provenance is knowing which source a specific claim came from, at the level of the claim rather than the answer. A cited answer tells you which documents were consulted; provenance tells you which sentence rests on which source. The difference matters the moment someone asks why a system said something, because an answer-level citation cannot be checked and a claim-level one can.