Nicheloom visits: 58,069 all-time · 7,637 in the last 7 days

The opportunity tracker for new startups.

Today: 1,953 launches · Niche of the day: specialized calculation tools · See what launched today →

XTrace

Encrypted vector DB (search embeddings without exposing them)

Get picks like this daily. Today's launch numbers, the top launches, AI and tech news, and a daily opportunity pick, straight to your inbox.

Want to reach these readers? Sponsor an issue.

Hey everyone! This is XTrace. Wanted to share what we’ve been working on for the past year.We built a private vector database from the ground up that performs similarity search on encrypted vectors. The server never sees your plaintext embeddings or documents.The problem we’re trying to solve: every vector DB today requires plaintext on the server. If you're doing RAG over sensitive data (medical, legal, financial), your embeddings — which researchers have shown can be inverted to recover original text — sit exposed on someone else's infrastructure.XTrace encrypts everything on your machine first. Vectors get Paillier homomorphic encryption, text gets AES-256. The server stores and searches only ciphertexts. Your keys never leave your environment.We just open-sourced the SDK (Apache 2.0). You can run the encryption verification tests offline without even creating an account.Trade-offs we're upfront about: there's latency overhead from the encryption operations. We're actively optimizing this. The free tier is rate-limited but fully functional.Happy to answer questions about the crypto approach, architecture decisions, or anything else.

HN · launched Apr 22, 2026 · 13 upvotes (top 33%) · 0 views

Niche: database management systems and engines · 162 launches, 5 in the last 7 days ▼ -17% · Explore this niche →

Category and tags

  • Security
  • Data infrastructure
  • Developer
  • AI-native
  • Commercial product

Visit website ↗ Source listing ↗

Could you build this?

No Building an encrypted vector database that queries high-dimensional vectors without decrypting them requires cutting-edge cryptographic knowledge in homomorphic encryption (FHE) or secure multi-party computation.

What it would actually take: The system requires an implementation using advanced cryptographic schemes such as CKKS or BFV fully homomorphic encryption, or specialized hardware secure enclaves (AWS Nitro Enclaves / Intel SGX). The hard part is designing approximate nearest neighbor (ANN) search algorithms that can evaluate dot products and Euclidean distances over ciphertexts without prohibitive computational latency and ciphertext explosion, requiring a team of applied cryptographers and systems engineers.

Discussion

3 comments analyzed.

Concerns raised: Data still goes through AI tools even if encrypted locally, Unclear if solution prevents AI companies from retaining conversations

Competitors

Other products that read as similar to this one : 131 launches clear the similarity bar, closest 8 shown.

Attention rank: #46 of 132 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 174 days after the earliest competitor.

The niche, labels, vibe code rating and competitor comparison on this page are analysis by Nicheloom. If you use them, please credit nicheloom.com. How you may use our data.