
Why Your RAG Pipeline Fails on the Inbox
Email and calendar data breaks most RAG setups because relevance is temporal and social, not just semantic. A bigger embedding model won't save you.

Email and calendar data breaks most RAG setups because relevance is temporal and social, not just semantic. A bigger embedding model won't save you.

Authentication tells us an agent is real. Delegation tells us who it's acting for — and that's the primitive we're missing. Here's how to build it.

Move past one-off prompts. Build a persistent Tamaton agent that watches your inbox, files, and calendar, then acts on a schedule with human checkpoints.

A recent 200M-parameter hobbyist MoE is a great reminder: email triage, classification, and routing rarely need frontier models. Here's where small wins.

Retrieval-augmented generation quietly breaks on email and files because it ranks documents, not conversations. Here's why, and what to do about it.

A practical framework for picking the best AI model for productivity tasks — weighing latency, cost, and reliability over leaderboard scores.

A product deep-dive on the permission, preview, and rollback design that makes an AI email agent safe to actually act — not just suggest.

A concrete look at how shared context across inbox, docs, sheets, and calendar gives AI agents verifiable grounding — and fewer confidently wrong actions.

A field-tested breakdown of which inbox tasks AI agents handle reliably today, and where human review still isn't optional.

A field guide to separating agents that actually finish work from the reasoning-superhero hype — and the boring, bounded jobs they quietly win at.

A concrete walkthrough of building a scoped Tamaton agent that triages your inbox, drafts replies, and files documents — with its own identity and permissions.

Email is the hardest RAG target there is — threads, quotes, and recency all conspire against you. Here's how to design inbox retrieval that's actually accurate.
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