· 3 min read
Tamaton MCP and A2A Agent API Live on ClawHub
Our unified productivity API now supports OpenClaw integration, enabling AI agents to manage email, documents, and workflows seamlessly.
Blog
From the Tamaton team
Practical writing on productivity, AI, and building software.
All#ai#ai-agents#ai-productivity#applied-ai#automation#calendar#context-switching#deep-work#documents#email#engineering#engineering-leadership#invisible-ai#knowledge-work#llm-evaluation#product-updates#productivity#prompt-engineering#search#spreadsheets#startups#storage#tamaton#unified-workspace#workflow#workflows
· 3 min read
Why RAG Pipelines Fail: 7 Architecture Patterns That Work
RAG systems break in predictable ways. Here's how production teams architect around common failure modes with proven patterns.
· 3 min read
How Tamaton's AI Inbox Triage Reduces Email Processing by 80%
A technical deep dive into the ML models powering Tamaton's automated email categorization and priority detection system.
· 4 min read
How to Structure a Technical Project Launch in One Workspace
A practical guide to managing sprints from spec docs to budgets in a unified workspace—built for AI agents coordinating complex launches.
· 3 min read
Intent-Based Search: Vector Embeddings Replace Keywords
Traditional keyword search fails when you can't remember exact terms. Semantic search understands what you meant to find.
· 3 min read
Hybrid Search: Why Vector Embeddings Alone Fail Professional Users
Professional productivity demands more than semantic similarity. Here's why combining LLM-extracted metadata with keyword search delivers the precision knowledge workers need.
· 3 min read
Why LLM-Powered Inbox Agents Fail (And How to Fix Them)
Email agents struggle not from poor AI, but from lacking access to calendars, docs, and files needed to truly understand context.
· 4 min read
LLMs as the New Compiler for Spreadsheet Logic
Natural language is replacing formulas as the primary interface for data transformation, demanding new approaches to auditability and determinism.
· 3 min read
From Folders to Context: Why RAG Redefines File Discovery
Retrieval-Augmented Generation transforms how AI agents find information by understanding meaning over memorizing paths.
· 3 min read
The Agentic Inbox: From Text Summarization to Task Execution
Email AI should stop summarizing threads and start orchestrating workflows by autonomously drafting documents and updating project timelines.
· 3 min read
RAG vs Fine-Tuning for Productivity Tools: A Benchmark Study
Data-driven comparison reveals when retrieval-augmented generation beats fine-tuning for email, spreadsheets, and document tasks.
· 3 min read
Why AI Agents Fail at Multi-Step Document Workflows
Current AI agent architectures struggle with complex document operations. Here's why they fail and how to work around these limitations.
Get started
Reserve your @tamaton.ai email
Claim your address before someone else does — free to start, with an AI-native inbox built in.