DRAAN — pronounced Dee-Ran — is the only AI architecture where your data stays yours, and the model just provides the language. Connect your knowledge. Ask anything. Sell what you build. Built by CmdShift, LLC · Founder: Darin Manley
100%Data Sovereignty $0Token Cost 0GPUs Required 10msRetrieval Latency
Data sovereignty. Your knowledge never enters a cloud LLM, never trains a model you don't own, never leaks to a competitor through a foundation-model vendor. Air-gap capable. On-prem capable. Browser-only capable.
Compute arbitrage. Cloud LLM tokens cost $2–$75 per million. DRAAN runs on a $50/mo VPS, a Raspberry Pi, or directly in the user's browser. Same architecture, two substrates.
Your data is currency. Corpus owners get paid when their indexed knowledge answers someone else's question through the Brain Marketplace. Your data isn't a cost center — it's a revenue line.
Verifiable answers. Every sentence in every response traces to the source passage it came from. No hallucinations. No "trust me." No compliance review hell.
Every word pulled verbatim from your source documents. Zero generation. Fully sourced. Fully verifiable. Use this for legal, medical, regulatory, or any context where invention is unacceptable.
Any LLM — local or hosted — writes fluent prose from DRAAN's retrieved facts. Per-sentence verification against sources. Every claim marked as sourced or unsourced. The LLM is the voice. DRAAN is the brain.
Side-by-side amber/blue verification overlay shows the LLM's draft next to the sourced ground truth, sentence by sentence. The reviewer sees, at a glance, what the model actually has evidence for — and what it invented.
Indexed corpora — Brains — are served from brain.draan.ai. Each Brain covers a domain: medicine, law, finance, military doctrine, a codebase, a company's internal wiki. Buyers download once and run locally forever. Sellers get paid every time their corpus answers a query. Your data is worth currency. We built the rails.
ICLR 2026 Google Research · arXiv:2504.19874
DRAAN uses TurboQuant — 6× KV cache compression with zero accuracy loss. Developed by Google Research and presented at ICLR 2026, TurboQuant compresses the KV cache by up to 6× using a two-stage process: a random orthogonal rotation (PolarQuant) that redistributes vector energy uniformly across coordinates, followed by a 1-bit Quantized Johnson-Lindenstrauss correction. The result is near-full-precision output at 3.5 bits per channel — mathematically identical to FP16 on every benchmark tested.
6×KV cache compression 0%accuracy loss 8×attention speedup
L4 — Response composition (extractive): Multi-signal sentence scoring. Deduplication. Coherent assembly from top-ranked passages.
L3 — Cross-attention (math): scores = K @ Q / (sqrt(d) × temperature) → weights = softmax(scores). No parameters. Pure matrix multiplication.
L2 — TF-IDF + BM25 index (statistics): Per-node vector stores. L2-normalized document vectors. Cosine similarity retrieval.
L1 — Text processing: Overlapping word-count chunking. Tokenization. N-gram extraction. No model — just string operations.
L0 — Browser fleet (ingest): 30 headless browser instances, each specializing in a topic cluster. 600-page distributed knowledge base.
Cloud LLMs: $2–$75 per 1M tokens. 1M queries/month = $1,000–$37,500/month.
DRAAN: $0 in token costs. One server at $50–$100/month. That's it.
* Figures shown are for illustrative purposes only and do not represent DRAAN's pricing model.
Server mode: Deploy on any Linux box — Raspberry Pi, VPS, or on-prem server. No GPU required. Air-gap capable.
Browser mode: Runs entirely client-side via WebGPU. The knowledge index downloads once and runs locally forever. Same retrieval architecture. Same math. Two substrates.
python · web tech · databases · ml basics · deep learning · networking · infrastructure · security · physics · quantum · biology · neuroscience · astronomy · mathematics · economics · ancient history · modern history · military · philosophy · earth science · medicine · energy · distributed sys · info retrieval · robotics · psychology · anomalous · survival · education · business
RAG retrieves context and feeds it to a large language model for generation. DRAAN retrieves context and composes the answer directly through cross-attention math — no large language model in the loop. RAG needs a large GPU-hosted model. DRAAN needs only standard linear algebra.
Wrong metric for the retrieval layer. DRAAN's statistical retrieval runs in 10ms — that's the number that stops the conversation. No token generation, no GPU, no API cost, no streaming required.
No — and that's the product. DRAAN runs underneath your LLM, not instead of it. Your LLM generates. DRAAN verifies. Together, the stack is trustworthy. The LLM alone is not.
This site is a React SPA. For AI-readable source code and full content: draan.ai/llms.txt · draan.ai/llms-full.txt
Contact: darin.j.manley@gmail.com · 206-227-9124 · CmdShift, LLC © 2026