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Models come to your data — your data never leaves your infrastructure. You run a tracebloc workspace on your own hardware. Contributors submit models that are scanned and run in isolation against your data, on your machines. Only results leave — and trained model weights only if you choose to share them.

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The trust boundary

How tracebloc keeps your data on your infrastructure — models come to the data, only results leave
Raw data stays inside your infrastructure. Your workspace opens an outbound-only connection — nothing reaches in to pull your data out.

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How it works

1

Deploy your workspace

One command sets up your private workspace on your own infrastructure — a laptop, an on-prem server, or a cloud cluster.
2

Ingest your datasets

Stage your training and test data locally. Metadata syncs to the platform so contributors can see what’s available — the raw data never moves.
3

Create a use case

Define a use case from your datasets and set how submitted models are evaluated.
4

Invite contributors

Whitelist contributors by email. Only the people you invite can take part.
5

Contributors submit and train models

Each model is scanned for vulnerabilities, then trains against your data in an isolated container, on your hardware.
6

Only results are shared

Training and evaluation results flow back to you over TLS. Trained model weights are shared only if you choose to — you control that in the admin panel.

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What stays, what leaves

DataShared with the platform?
Raw training & test dataNever — it stays on your infrastructure
Dataset metadata (schema, row counts)Yes — so contributors know what’s available
Training & evaluation resultsYes — the metrics models are judged on
Trained model weightsOnly if you allow it — your choice per collaboration, set in the admin panel
Enforced by per-job container isolation, a NetworkPolicy that blocks data egress from training pods, a vulnerability scan before any model runs, and TLS on all traffic.
The mental model: 1 machine = 1 workspace = n datasets. One deployment per machine; as many datasets inside it as you like.

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Vocabulary

TermWhat it is
WorkspaceYour private, dedicated AI environment — tracebloc’s software running on your own infrastructure, where you invite contributors to train models on your data
Client IDThe credential that connects your workspace to the platform (created on the clients page)
DatasetData you’ve staged locally for use cases
Use caseA task contributors build models for, against your datasets
ContributorAn external data scientist who submits models — and never sees your raw data

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What it touches

The installer changes only Docker and ~/.tracebloc on your host — no system-wide changes. Uninstalling is a single command, and your data is yours throughout.

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Get started

Quick Start

A running workspace in about 10 minutes, with one command.

Deployment environments

Deploy on local / k3d, bare-metal, EKS, AKS, or OpenShift.
Want the guarantees in detail for your security team? See Security & data handling.