❌ Traditional RAG
- Re-derives knowledge from scratch every query
- No accumulation — same question, same expensive re-computation
- Contradictions buried until you happen to ask about them
- Chat history is not a knowledge base
Wiki-first knowledge
Your knowledge, Compilednot searched.
Drop in your documents. LLM Wiki reads them, extracts key information, and builds a persistent, interlinked llm knowledge base that compounds over time. Not RAG. Not search. Compilation.
→ Flow: Docs → Agent → Wiki
No spam, ever · Launching soon · Early access for waitlist members
The Problem with How We Use AI Today
✅ LLM Wiki
- Compiles knowledge once, keeps it current
- Every source makes the wiki richer — knowledge compounds
- Contradictions flagged at ingest, not query time
- A persistent, evolving artifact you own
That's why we built LLM Wiki — to turn your AI from a search engine into a knowledge engine.
How LLM Wiki Works
Your llm wiki agent pipeline: ingest, compile, then query — structured like a real wiki, not ad-hoc retrieval.
Drop In Your Sources
Upload research papers, articles, transcripts, notes — anything. The LLM reads each source, extracts key information, and files it into the wiki. Every source is immutable once ingested.
Watch Your Wiki Grow
The wiki is a structured collection of interlinked markdown pages. Entity pages, concept pages, summaries, cross-references. Every new source touches 10–15 pages, strengthening your knowledge graph ai view.
Ask, Explore, Maintain
Ask questions against the compiled wiki — answers come with [[wikilink]] citations. Run lint checks to find orphans, contradictions, and gaps. Good answers get filed back. Your knowledge compounds.
What You Get
An automated wiki builder experience — polished pages, links, and maintenance without running local tooling.
🏗️ Persistent Wiki
Structured markdown pages that accumulate across sessions — an ai wiki generator that keeps growing. Your knowledge doesn't reset — it grows.
🔗 Cross-References
Pre-built links between entities, concepts, and sources. Navigate your knowledge like a real encyclopedia.
⚡ Contradiction Detection
New sources that contradict old claims are flagged at ingest time. No more hidden conflicts.
📊 Knowledge Graph
Interactive graph visualization showing every connection. See the big picture and find hidden links.
🔄 Living Overview
overview.md is revised on every ingest to reflect the current synthesis. Always up to date.
🤖 AI-Maintained
You curate sources and ask questions. The AI does the rest — creating pages, linking, linting, updating.
📋 Lint Reports
Find orphans, broken links, missing pages, and data gaps. Keep your wiki healthy and complete.
💾 Version Tracked
Every edit is a commit. Full version history means you can always see what changed and why.
Not RAG. Not Search. Compilation.
Compare rag vs wiki approaches — same LLM, different artifact.
Knowledge model
RAG: Re-derive per query
LLM Wiki: Compile once, keep current
Retrieval unit
RAG: Raw text chunks
LLM Wiki: Structured wiki pages
Cross-references
RAG: None
LLM Wiki: Pre-built [[wikilinks]]
Contradictions
RAG: Surface at query time
LLM Wiki: Flagged at ingest
Accumulation
RAG: None
LLM Wiki: Every source enriches it
Maintenance
RAG: Manual
LLM Wiki: AI does it automatically
Output format
RAG: Chat response
LLM Wiki: Persistent wiki + graph
Cost over time
RAG: Linear (same cost/query)
LLM Wiki: Decreasing (amortized compiles)
Use Cases
From research to personal knowledge base ai — one workflow, many domains.
Go deep on a topic over weeks. Upload papers, articles, reports. Build a comprehensive wiki with an evolving thesis — your llm research tool for long-horizon synthesis. Your research compounds instead of evaporating.
File each chapter as you go. Build pages for characters, themes, plot threads. Think fan wikis — built automatically as you read.
Feed meeting transcripts, project docs, customer calls. Get an internal wiki that stays current because the AI maintains it. No more outdated wiki pages.
Track goals, health, habits. File journal entries, articles, podcast notes. Build a structured picture of yourself over time.
Tip: pairing notes with wikis? See also obsidian ai wiki workflows — LLM Wiki aims for zero local setup in the browser.
Under the Hood
🔒 raw/ (Immutable)
Papers · Articles · Transcripts · Notes · Images
⬇ READ ONLY ⬇
✨ wiki/ (LLM-Owned)
index.md · overview.md · entities/ · concepts/ · sources/ · syntheses/ · log.md
⬇ WRITES ⬇
📜 Schema (System Instructions)
Conventions · Workflows · Naming Rules · Lint Rules
Your sources stay immutable. The wiki is a living artifact, maintained by the AI.
Frequently Asked Questions
Answers for ai knowledge management buyers and builders.
LLM Wiki is an AI-powered knowledge management platform that compiles your documents into a persistent, interlinked wiki. Unlike traditional RAG (Retrieval Augmented Generation) that re-derives knowledge for each query, LLM Wiki builds a structured knowledge base that compounds over time — every new source makes your wiki richer, more interconnected, and more useful.
RAG retrieves raw text chunks at query time. LLM Wiki compiles knowledge once and maintains it. Think of it this way: RAG is like searching through a pile of notes every time you have a question. LLM Wiki is like having a research assistant who organizes, cross-references, and maintains a real encyclopedia for you. No more re-deriving the same insights. No more missing connections.
LLM Wiki will offer a free tier at launch. Join the waitlist to be among the first to try it and lock in early adopter benefits.
You can add research papers, articles, transcripts, notes, books, reports — virtually any text-based content. Each source is immutable once ingested, and the wiki derives all its content from these sources with full traceability.
Yes. LLM Wiki takes privacy seriously. Your sources and wiki are encrypted and stored securely. We never share your data with third parties. You own your knowledge.
Absolutely. LLM Wiki's version-tracked architecture makes it ideal for teams. Every edit is tracked, every change is versioned. Multiple team members can contribute sources, and the AI maintains the wiki across all contributions.
The LLM Wiki approach means using an LLM to maintain a wiki, not just answer ad-hoc questions. The model reads your sources, extracts structure, and keeps interlinked markdown pages up to date — so knowledge compounds in one place instead of being re-derived from scratch on every query.
When a new source is ingested, the LLM Wiki Agent checks the extracted information against existing wiki pages. If a new claim contradicts an established one, it's flagged in the wiki and in the log — so you can review and resolve it, rather than discovering contradictions only at query time.
🚀 Launching Soon
Your Knowledge Deserves More Than Search
Join the waitlist and be among the first to experience AI-powered knowledge compilation. No spam, ever.
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Trusted approach
Why a compiled wiki
Serious knowledge work needs a persistent artifact — not another ephemeral thread. LLM Wiki keeps structure, cross-links, and synthesis current as you add sources, so answers stay grounded in what you actually filed.
Compilation amortizes effort: each ingest improves the whole graph instead of re-deriving context on every question.
🧩
Structured by design
Entities, concept pages, and wikilinks — not loose chunks floating in chat.
📦
Transparent workflow
Immutable sources, versioned edits, and traceability from claim to file.
🔬
Built for depth
For research, reading notes, and teams who outgrow plain Q&A.
🚀
Coming Soon
Join the waitlist to be among the first to try it