Why Server-Blind AI Storage?

Most “AI databases” store embeddings in the cloud and send prompts to remote LLMs. The server — or the vendor — can see your documents, queries, and agent memory.

FLASH inverts this model.

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The Problem

Your documents  →  Cloud vector DB  →  Plaintext embeddings + metadata visible
Your questions  →  Remote LLM API    →  Query + context logged
Agent memory    →  SaaS memory layer →  Third-party retention policies

Even with “encryption at rest,” the service operator typically holds keys or sees decrypted payloads at query time.

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The FLASH Model

Your documents  →  Client encrypts  →  Server stores ciphertext + trapdoors only
Your RAG query  →  Client embeds      →  Similarity over encrypted vectors
Agent memory    →  Local FLASH vault  →  Encrypted episodic recall

The engine answers: “Which trapdoor / vector ID matches?” — never “What does the user’s document say?”

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Three Workloads, One Layer

1. Private RAG

Ingest PDFs, notes, or medical records. Ask questions semantically. The storage layer never holds plaintext.

const rag = client.privateRAG("clinical");
await rag.ingest({ title: "Protocol", text: "..." });
const ctx = await rag.ask("contraindications?");

2. Agent Memory

Agents need persistent context. FLASH stores memories encrypted with importance scores and TTL — not in a vendor’s conversation log.

const memory = client.agentMemory("assistant");
await memory.remember("User prefers Arabic UI", { importance: 2 });

3. Sealed Vault

API keys and secrets in an isolated domain with passphrase lock — separate from general collections.

const vault = client.sealedVault("secrets");
vault.unlock("passphrase");
await vault.put("openai_key", { value: "sk-..." });

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When Server-Blind Matters

Industry	Risk without server-blind storage
Healthcare	PHI exposure in vector indexes
Legal	Privileged documents in cloud RAG
Finance	Client data in LLM context logs
Personal AI	Notes and journals on third-party servers

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What FLASH Is Not

• Not a chatbot — flashsh ask retrieves encrypted chunks, it does not call OpenAI
• Not a MongoDB replacement — different category entirely
• Not “encryption optional” — zero-knowledge is architectural

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Get Started

1. Positioning — when to choose FLASH
2. Private RAG — ingest + ask workflow
3. LangChain Integration — connect AI frameworks
4. Intelligence Console — local web UI