diff --git a/README.md b/README.md
index bd5d2e3..9af1b1f 100644
--- a/README.md
+++ b/README.md
@@ -21,9 +21,9 @@ clinical tool and makes no diagnostic claims.
 | Database   | PostgreSQL 16                                          | `docker-compose.yml` |
 | Frontend   | SvelteKit, TypeScript                                  | `web/`               |
 | ML         | LightGBM pathogenicity scorer, MLflow registry         | `ml/`                |
-| Orchestration | Argo Workflows + Argo Events (pipeline), Pub/Sub (events) | `infra/argo-workflows/` |
+| Orchestration | Cloud Run job (default), Argo Workflows + Argo Events, Pub/Sub | `infra/terraform/`, `infra/argo-workflows/` |
 | Platform   | Kubernetes (Kustomize), ArgoCD (GitOps)                | `infra/k8s/`, `infra/argocd/` |
-| Cloud      | GCP: GKE Autopilot, Cloud SQL, GCS, Batch, Secret Manager, Artifact Registry | `infra/terraform/` |
+| Cloud      | GCP: Cloud Run, Google Batch, GCS, Secret Manager, Artifact Registry; GKE Autopilot and Cloud SQL behind flags | `infra/terraform/` |
 | CI/CD      | GitHub Actions, Workload Identity Federation           | `.github/workflows/` |
 
 ## Quick start (local)
@@ -51,7 +51,7 @@ the rest of that patient's genome is not public. It writes the phenotype list al
 so the case can be created exactly as reported. See [docs/data.md](docs/data.md) for the
 provenance and for what this case does and does not demonstrate.
 
-The docker-compose API has no Nextflow, so "Run VEP annotation" marks the job failed with the
+The docker-compose API has no Nextflow, so "Analyse case" marks the job failed with the
 command to run instead. With Nextflow and Docker on the host, a VEP cache in `pipeline/cache/vep`
 and a VCF under `data/` (see [data/README.md](data/README.md)):
 
@@ -68,7 +68,7 @@ pipeline on a handful of variants:
 VEP_DATABASE=true make pipeline VCF=pipeline/tests/data/tiny.vcf
 ```
 
-To make the UI's "Run VEP annotation" button work, run the API on the host (where Nextflow and
+To make the UI's "Analyse case" button work, run the API on the host (where Nextflow and
 Docker are) rather than in docker-compose:
 
 ```bash
@@ -80,7 +80,7 @@ cd api && DATABASE_URL=postgresql+asyncpg://rarelens:rarelens@localhost:5432/rar
 ```
 
 `PIPELINE_DATABASE_URL` is what the loader container gets: inside it, the API's own `localhost`
-would be the container itself. `LOCAL_DATA_ROOT` is the directory a sample's `vcf_uri` must sit under.
+would be the container itself. `LOCAL_DATA_ROOT` is the directory a case's `vcf_uri` must sit under.
 
 To train and register a model (the API scores with `models:/rarelens-pathogenicity@production`):
 
@@ -133,9 +133,10 @@ Every green CI run on `main` bumps image tags in the gcp overlay and ArgoCD depl
 ## Data
 
 The demo runs on published, openly licensed human data: the NIST Genome in a Bottle HG002
-benchmark genome as the sample, ClinVar for labels, gnomAD for allele frequencies. Sources,
-licences, citations and how the model should be evaluated honestly are in
-[docs/data.md](docs/data.md).
+benchmark genome as the background sample, ClinVar for labels, gnomAD for allele frequencies, the
+Human Phenotype Ontology's gene-to-phenotype annotations as what the ranking matches against, and
+GA4GH phenopackets curated from case reports for the published case. Sources, licences, citations
+and how the model should be evaluated honestly are in [docs/data.md](docs/data.md).
 
 ## Architecture
 
@@ -145,13 +146,18 @@ than AWS.
 
 ## Status
 
-Work in progress. Milestones, in order:
+A self-training lab, built in the open. Working end to end:
 
-1. Skeleton, Postgres, FastAPI, Nextflow VEP annotation on a public VCF, CI green
-2. SvelteKit UI: sample list, variant table with filters, job status
+1. Postgres, FastAPI, Nextflow VEP annotation on a public VCF, CI green
+2. SvelteKit UI: cases, a phenotype-ranked candidate list showing the evidence behind each rank,
+   shortlist/dismiss decisions, and a case report
 3. Kubernetes manifests, kind, Argo Workflows trigger
-4. Terraform for GCP, ArgoCD GitOps deploy
-5. Pathogenicity model, MLflow registry, prediction endpoint
+4. Terraform for GCP: serverless Cloud Run + Batch by default, GKE and ArgoCD behind a flag
+5. Pathogenicity model trained on ClinVar, MLflow registry, scoring endpoint
+6. A demo case built from a published patient, with the citations behind it
+
+Known gaps: allele frequencies need the 25 GB VEP cache, because VEP's database mode returns
+none, so the rarity term does no work without it; and the deployed demo has no authentication.
 
 ## Licence