<h2>Overview</h2>
<p>Every organization has valuable data trapped in PDFs, Google Docs, and internal wikis. Retrieval-Augmented Generation (RAG) can unlock this—but only if implemented with precision.</p>
<h2>Our Method (RAG Done Right)</h2>
<p>Esoteria&#39;s approach combines clarity, context, and control.</p>
<ul>
<li><strong>Step 1:</strong> Convert internal docs into structured Markdown via Obsidian or GitHub.</li>
<li><strong>Step 2:</strong> Store embeddings in Supabase for query access.</li>
<li><strong>Step 3:</strong> Interface with Ollama or hosted LLMs for on-demand reasoning.</li>
</ul>
<h2>Key Components</h2>
<ul>
<li>Obsidian vault → Supabase sync.</li>
<li>Vector embeddings for semantic retrieval.</li>
<li>LLM (Ollama, OpenAI, Gemini) with Esoteria guardrails.</li>
</ul>
<h2>Implementation Notes</h2>
<p>We emphasize calibration and traceability. Each answer cites the original document location, ensuring verifiable AI responses.</p>
<h2>Extensions / Add-Ons</h2>
<ul>
<li>Role-based content access.</li>
<li>Multilingual document embedding.</li>
<li>Versioned retraining triggers.</li>
</ul>
<h2>Work with Us</h2>
<p>Transform your documentation into a living knowledge base. Esoteria ensures RAG implementations are reliable, maintainable, and explainable.</p>


Esoteria's RAG methodology transforms internal wikis and SOPs into searchable AI knowledge layers using Obsidian, Supabase, and Ollama for verifiable insights.

RAG Done Right: Turning Documentation into an AI Knowledge Base

Overview

Our Method (RAG Done Right)

Key Components

Implementation Notes

Extensions / Add-Ons

Work with Us

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