Neurarch Neurarch / Security and data handling

What we do with your keys, designs, and data

Written for the engineer doing the security review, not for a compliance checkbox. It says exactly what leaves your browser, what we store, and what we never touch. Everything here is a property of how the product is built, verifiable in the client bundle and the open repos.

Last reviewed 2026-07. For a specific question, email security@neurarch.com.

Your API keys true today

Neurarch runs LLM calls two ways, and which one fires is automatic.

What we store, and what we never store true today

The design decision behind the whole product: we learn from the shape of architectures, never their contents.

We storeWe never store
Structural fingerprints: a hash of the layer-type histogram and edge count, plus coarse stats (layer count, depth, parameter count). Model weights of any kind. Trained checkpoints round-trip browser to your own Colab, Kaggle, or Hugging Face, never our storage.
Usage events: feature used, timestamp, quota counts, and lint verdicts (which findings you accept or dismiss). Your training data or datasets. Dataset access happens in your own runtime (your Colab, your HF token), not on our servers.
Saved designs: the architecture graph as JSON (topology and hyperparameters), under row-level security so only you can read your own. Prompt or code contents beyond what a design graph itself contains. We do not retain a transcript store of your agent conversations server-side.

Account isolation true today

Every user-scoped artifact that lands in local storage (keys, agent memory, saved variants, edit history) is registered in one cleanup path that runs on sign-out and on any account switch. Server-side, saved designs, training records, and lint verdicts live behind Postgres row-level security policies, so a query can only ever return the caller's own rows.

The data we aggregate, done privately true today

Neurarch improves its architecture advisor from a stream of anonymous, structural signals: which lint findings users across the base trust versus treat as noise. Two safeguards make this safe to run:

We do not train foundation models on your designs, and we do not sell data.

Infrastructure

LayerProviderNotes
App and functionsVercelStatic frontend plus serverless functions for the proxy and account APIs.
Database and authSupabase (Postgres)Row-level security on every user-scoped table. Auth via Supabase sessions.
Model providersAnthropic, Google, Groq, xAI, OpenRouterReached over TLS. BYOK calls never pass through us.
Optional trainingYour Colab / Kaggle / Modal / HFRuns in your accounts, billed to you, with your tokens.

Enterprise and roadmap

Stated honestly: what is available now on the Team and Enterprise tiers, and what is on the roadmap and not yet certified.

Available on Team now

Shared team workspace with row-level security, per-seat access with a database-enforced seat limit, an append-only change audit log on shared designs, and design review sign-off before training.

On the roadmap planned

SOC 2 Type II attestation, SSO (Google Workspace / Okta / SAML), configurable data-retention windows, and a signed data-processing agreement (DPA). These are planned, not shipped. If your review needs one on a timeline, email us and we will tell you where it actually stands rather than claim a badge we do not hold.

Verify it yourself

Try it, no account needed for the editor
This page describes the product as built at the date above. It is not a contract or a warranty. For a security questionnaire, DPA, or a specific control, email security@neurarch.com. Neurarch: neurarch.com.