Donkit Blog
First-hand notes on building AI agents people pay to use: what we measured on our own platform, real agents and what they earned, and the failures that cost us.
Before an AI Agent Can Think, It Has to Read
Enterprise AI can only reason over what it can read. Here’s how Donkit rebuilt its document reader to turn complex PDFs, scans, tables, and images into structured, agent-ready knowledge — faster, cheaper, and more reliably.
Your AI Agent Works. That’s Not Enough.
Building an AI agent is increasingly easy. Proving that it is accurate, secure, explainable, and production-ready is the real enterprise challenge.
Why Most “Autonomous” AI Tools Break the Moment They Hit Real Data
Why autonomous AI tools fail on real data — and what actually works.
Interventional Evaluation for RAG: Why Testing the Happy Path Is Not Enough
Why robust RAG starts with breaking the pipeline before reality does.
Combining RAG and RLM for Precision Across Massive Knowledge Bases
RAG + RLM: deeper, more precise answers across massive enterprise knowledge bases.
TOON (Token-Oriented Object Notation): When “Long Context” Becomes Affordable
TOON cuts JSON tokens ~40%, fitting 1.7× more long context
The RAG Triangle
Master the trade-offs between accuracy, latency, and cost in RAG.
What is RAG?
LLMs are great at language, but bad at facts. RAG fixes it because RAG = "Search first, then answer."