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Classifyre · Documentation

Scattered data
in. Closed cases out.

Classifyre reads the systems you already run, raises the findings worth a human's time, and gives you a structured place to work them — with an AI autopilot doing the legwork between scans. These docs explain how the application works, section by section.

Step zero — get it running

Classifyre ships in two distributions, with the same features in both. Install one, then work through the pipeline below.

Option 01 · Docker

Run it on your machine

One image · amd64 + arm64

PostgreSQL, the UI and the scan workers are all inside the image, so there is nothing to provision — and a bind-mounted folder is a source like any other.

docker run -d --name classifyre \  -p 3000:3000 \  --shm-size=1g \  -v classifyre-pgdata:/var/lib/postgresql/data \  -v classifyre-data:/var/lib/classifyre \  -v classifyre-uv-cache:/cache/uv \  classifyre/all-in-one:0.5.12
Docker install guide →

Option 02 · Kubernetes

Or run it on a cluster

Helm, OCI-native · no repo add

The chart deploys the web UI, API, worker, and the ephemeral scan Jobs. PostgreSQL can be embedded for a trial or external for production.

helm install classifyre \
  oci://registry-1.docker.io/classifyre/classifyre-core \
  --namespace classifyre --create-namespace \
  --version 0.5.12
Kubernetes deployment guide →

Then: one pipeline, five steps

  1. 01ConnectPoint Classifyre at systems you already run. Data stays in place.
  2. 02ScanEach run registers every document and file as an asset.
  3. 03DetectDetectors read every asset and raise findings worth a look.
  4. 04Connect the dotsInquiries keep watch; duplicate review finds the same thing twice.
  5. 05ResolveCases collect evidence, weigh hypotheses, and reach a conclusion.

Start where you are

Every section

Looking for a specific connector or detector? The source catalog and detector catalog are generated straight from the product, so they are always current.

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