
Your Inbox Is a Retrieval Problem, Not a Sorting Problem
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.

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.

Model quality isn't your bottleneck anymore — context portability is. We map where the time actually goes in AI-assisted work, and what to fix first.

Mailboxes, files, and calendars are structured, timestamped, and permissioned — which is why naive vector search fails on them. Here's a retrieval design that works.

A concrete walkthrough of Tamaton's inbox triage agent: how 84 unread messages become a ranked queue with searches run, drafts written, and calendar holds already placed.

Confident guessing is the most expensive failure mode in AI workflows. Here's how surfaced confidence and cited sources turn inbox triage and search into something you can actually trust.

Clever prompts don't make agents accurate — verified, fresh sources do. Here are the grounding architecture patterns that keep AI agents honest.

Pretraining freezes your model's worldview on a date that has already passed. We compare knowledge editing research with retrieval as strategies for keeping an AI assistant accurate.
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