In this video, we’ll demonstrate the power of Agentic Knowledge Optimization with AI Pack. We’re tackling a key challenge enterprises face today: transforming large volumes of noisy, website knowledge, like API documentation, into highly optimized, LLM-ready content.
Our use case is both simple and impactful: converting approximately 500MB of raw HTML documentation into just 0.5MB of refined, AI-optimized content using the AIPack lab@ako.
Runs in a Terminal User Interface, compatible with Windows, Mac, and Linux, on desktop or cloud.
Your API Key, Your Way, Open source, High power, Zero fluff.
The Demo Walkthrough
We’ll demonstrate how to easily install and set up the lab@ako AI Pack. For this demo, we’re using public documentation from alchemy.com.
Here’s the demo plan:
Install
lab@ako, the Agentic Knowledge Optimization AI Pack deployed on aipack.ai.Run
lab@akoon a small set of files to understand how the agent processes data and validates outputs.Scale the operation by downloading 500MB of raw HTML and increasing concurrency to 32 parallel tasks.
Watch as 30 minutes of sequential processing is condensed into just 5 minutes of high-concurrency performance.
The Agentic Workflow
The lab@ako agent follows a structured, multi-sub-agent pipeline designed for deep optimization:
Fetch & Slim: The process begins by fetching raw documentation files. The agent then “slims” them by stripping away noisy HTML and converting the content into clean Markdown. Each resulting Markdown file includes a concise, AI-generated summary.
Clean & Augment: Each Markdown file is refined further—structural issues are fixed, and the content is enhanced to ensure clarity, coherence, and contextual richness for LLM reasoning.
Final Indexing: Using the summaries from step one, the AI compiles a comprehensive index file,
llm.md. This file serves as a single source of truth for LLMs, enabling fast and precise knowledge retrieval.
In summary, lab@ako transforms 500MB of noisy HTML into 0.5MB of clean, structured, and AI-optimized Markdown, complete with a specialized llm.md index file—ready to power AI systems for accurate and efficient knowledge reasoning.
What’s Next?
Building on the success of lab@ako, we’re preparing to launch lab@kchat, our upcoming Knowledge Chat solution. This next AI Pack will allow users to deploy the kchat agent and begin interacting directly with their optimized content—turning static documentation into an intelligent, conversational knowledge experience.
