Exemem — Discover everything. Expose nothing.

🔎

Fingerprints, not files

Every file becomes a vector embedding — a numerical fingerprint that captures meaning without revealing content. Fingerprints are public. Raw data stays private.

👻

One disguise per entry

Each entry gets a unique derived pseudonym. No two entries can be linked to the same owner. Even 1,000 entries from one person look like 1,000 unrelated identities.

📈

More users = stronger privacy

Network growth increases the anonymity set. Bigger crowd, harder to deanonymize. The opposite of traditional social networks.

Two layers

Public discovery. Private content. Separated by design.

Store your data

Upload files. AI generates vector embeddings — numerical fingerprints published to the public layer. Raw data stays behind Fold DB's access controls.

Discover across the network

Search all public embeddings with a similarity query. Results show per-entry pseudonyms and similarity scores — never real identities or linked entries.

Request access

Found a match? Request raw data through the entry's pseudonym. Fold DB enforces the owner's policies: trust distance, cryptographic keys, payment gates.

Applications

Privacy-preserving similarity discovery across any data type.

📷

Photo Discovery

"Do strangers have photos of me?"

Search face embeddings across the network. Find 14 matching entries, each under a unique pseudonym. You can't tell if they belong to 14 people or 1. Request access or removal through the pseudonym.

🎵

IP Discovery

"Did someone copy my work?"

Embed your creative work — audio, text, code. Find entries with 96% similarity uploaded before your release. File a timestamped claim in the append-only store without knowing who owns the match.

🩹

Medical Cohorts

"Who else has my condition?"

Patients publish clinical profile embeddings. Researchers discover 47 matching entries for a rare disease signature. Contact pseudonyms to propose trials — nobody's identity revealed unless they choose.

💻

Threat Intelligence

"Has anyone seen this attack?"

Security teams publish embeddings of indicators of compromise. Search finds 7 matching entries. Share defenses through pseudonyms without revealing which organizations were hit.

Built for developers

Store data, generate embeddings, and run similarity discovery. REST API, TypeScript & Python SDKs.

// SOURCE CODE

import { Exemem } from 'exemem';

// Connect to the network
const em = new Exemem({ apiKey: 'em_your_api_key' });

// Ingest a file — embedding published, raw data behind your fold
await em.ingest('photo.jpg', { transform: 'face_embed_512' });

// Similarity discovery — search all public embeddings
const matches = await em.discover({
  query: myFaceEmbedding,
  threshold: 0.9
});
// Returns: [{ pseudonym: '0xA7f3...', similarity: 0.98, type: 'photo' }, ...]

// Request access to raw data through a pseudonym
const data = await em.requestAccess(matches[0].pseudonym);

                

from exemem import Exemem

# Connect to the network
em = Exemem(api_key='em_your_api_key')

# Ingest a file — embedding published, raw data behind your fold
em.ingest('photo.jpg', transform='face_embed_512')

# Similarity discovery — search all public embeddings
matches = em.discover(
    query=my_face_embedding,
    threshold=0.9
)
# Returns: [{ pseudonym: '0xA7f3...', similarity: 0.98, type: 'photo' }, ...]

# Request access to raw data through a pseudonym
data = em.request_access(matches[0].pseudonym)

                

# Ingest a file — embedding published, raw data behind your fold
curl -X POST https://api.exemem.com/api/ingestion/process \
  -H "Content-Type: application/json" \
  -d '{"file": "photo.jpg", "transform": "face_embed_512"}'

# Similarity discovery — search all public embeddings
curl -X POST https://api.exemem.com/api/discover \
  -H "Content-Type: application/json" \
  -d '{"query": [0.12, -0.34, ...], "threshold": 0.9}'

# Request access to raw data through a pseudonym
curl -X POST https://api.exemem.com/api/access/request \
  -H "Content-Type: application/json" \
  -d '{"pseudonym": "0xA7f3..."}'

                

npm install exemem # coming soon pip install exemem # coming soon

Public discovery. Private content.

Two layers with different trust models. Discovery is open. Access is controlled.

	Public Layer	Private Layer
What	Embeddings + Pseudonyms	Raw Data + Fold DB
Visibility	Open to everyone, no access gates	Policy-gated per entry
Content	Vector fingerprint (irreversible)	Original file (photos, docs, audio)
Identity	Unlinkable per-entry pseudonym	Owner verified via master key
Access control	None — embeddings are public	Trust distance, cryptographic keys, payment
Encryption	N/A (public data)	AES-256-GCM at rest
Index	Global ANN (HNSW/IVF), sublinear	Per-owner Fold DB instance
Store	Append-only, immutable record	Owner-controlled, deletable
Purpose	Discovery — find what exists	Access — control who sees it

File → T_embed → Vector → Public Index

Access Request → Pseudonym Route → Fold DB Policy → Raw Data

Discover everything.Expose nothing.