The failure mode with AI document work is rarely the analysis. It is that the analysis lives in a chat thread nobody can find three weeks later. Here is a workflow that puts the output somewhere it survives.
Step 1 — do the reading in Claude, not in your knowledge base
Claude's Opus tier is where the long-context work belongs: contracts, research papers, transcripts, anything where the middle of the document matters as much as the top.
Ask for structure rather than a summary. "Extract every commitment with a date attached, and flag the ones with no owner" produces something you can act on. "Summarise this" produces something you will re-read the original to verify.
Step 2 — write the structured output into Notion, not back into chat
This is the step people skip, and it is the whole point. A chat thread is not a knowledge base — it has no schema, no permissions model, and no way for a colleague to find it.
Paste the structured extraction into a Notion database with real properties: owner, date, source document, status. You are converting a conversation into a queryable record.
Step 3 — let Notion AI handle retrieval
Notion AI is not the tool to do your hardest reasoning, and it is a mistake to ask it to. Its value is that it already knows what is in your workspace.
Once the analysis is in Notion as structured rows, "what did we commit to in the vendor contracts this quarter" becomes a question with an answer, asked by someone who was not in the original conversation.
Why not do all of it in one tool
You can, and it will be worse. Doing the deep reading inside a workspace tool means accepting a weaker model for the hard part. Doing the retrieval inside a chat tool means the output is not findable.
The split exists because the two jobs have genuinely different requirements: one needs reasoning depth, the other needs to be searchable by someone else in six months.