
Your Inbox Is the Best RAG Corpus You Already Own
Email, calendar, and file storage are already chunked, timestamped, and permission-aware. Treating them as your retrieval corpus beats dumping documents into a vector database.

Email, calendar, and file storage are already chunked, timestamped, and permission-aware. Treating them as your retrieval corpus beats dumping documents into a vector database.

A concrete walkthrough of building a Tamaton agent that triages inbox, drafts replies with real context, and books follow-ups — with a human holding the approval button.

Long, harmless context isn't neutral. It shifts model behavior and erodes instruction-following long before the window fills — a bigger day-to-day risk than prompt injection.

There is no single best AI tool. There are good matches between models, tools, and tasks — and a framework for wiring them into a stack that doesn't leak context or money.

Most productivity APIs were built for humans clicking buttons. Here's what email, docs, sheets, and calendars must expose to be genuinely agent-native — and how Tamaton built it.

Stop judging AI assistants on vibes. Build a 50-item golden set from your own work and score them on retrieval, refusals, and cost per resolved task.

Folders and filters were a workaround for bad search. In an AI-native stack, your inbox is a messy index — and treating it that way changes triage, search, and follow-up.

An agent with access to every email, doc, and file is a liability until you scope it. Here's how to define, test, and audit custom agent permissions in Tamaton.

Demo RAG retrieves from one clean corpus. Real work means retrieving across email, docs, spreadsheets, and calendar — where context is stale, duplicated, and contradictory.

Assistants don't fail at memory because models forget. They fail because memory gets stored as a text blob instead of a governed store with recency, decay, provenance and conflict rules.

Tamaton search spans mail, docs, and files — but every hit is checked against the caller's grants at query time, so an over-eager agent can't retrieve what it was never allowed to see.

New adoption data shows the biggest productivity gains go to people using purpose-built AI tools, not general chatbots. The difference isn't model quality — it's context and integration.
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