diff --git a/docs/plans/2026-03-03-lthn-lan-handover.md b/docs/plans/2026-03-03-lthn-lan-handover.md
index d3a411a..33151aa 100644
--- a/docs/plans/2026-03-03-lthn-lan-handover.md
+++ b/docs/plans/2026-03-03-lthn-lan-handover.md
@@ -6,7 +6,7 @@
 
 ## Goal
 
-Stand up the Host UK Laravel app on the Linux homelab as `lthn.lan` — a private dev/ops hub away from production. This joins the existing `.lan` service mesh (ollama.lan, qdrant.lan, eaas.lan).
+Stand up the Host UK Laravel app on the Linux homelab as `lthn.lan` — a private dev/ops hub away from production. This joins the existing `.lan` service mesh (ollama.lthn.lan, qdrant.lthn.lan, eaas.lthn.lan).
 
 ## What lthn.lan Is
 
@@ -22,9 +22,9 @@ Mac (snider) ──hosts file──▶ lthn.lan (10.69.69.165)
                              └── Redis/Dragonfly (port 6379)
 
 Already running on 10.69.69.165:
-  ollama.lan  → Ollama (embeddings, LEM inference)
-  qdrant.lan  → Qdrant (vector search)
-  eaas.lan    → EaaS scoring API v0.2.0
+  ollama.lthn.lan  → Ollama (embeddings, LEM inference)
+  qdrant.lthn.lan  → Qdrant (vector search)
+  eaas.lthn.lan    → EaaS scoring API v0.2.0
 ```
 
 ## Prerequisites
@@ -147,13 +147,13 @@ BROADCAST_CONNECTION=log
 OCTANE_SERVER=frankenphp
 
 # OpenBrain — connects to existing .lan services
-BRAIN_OLLAMA_URL=https://ollama.lan
-BRAIN_QDRANT_URL=https://qdrant.lan
+BRAIN_OLLAMA_URL=https://ollama.lthn.lan
+BRAIN_QDRANT_URL=https://qdrant.lthn.lan
 BRAIN_COLLECTION=openbrain
 BRAIN_EMBEDDING_MODEL=embeddinggemma
 
 # EaaS scorer
-EAAS_URL=https://eaas.lan
+EAAS_URL=https://eaas.lthn.lan
 ```
 
 Then generate the app key:
@@ -175,7 +175,7 @@ labels:
   traefik.docker.network: proxy
 ```
 
-Note: For `.lan` domains, Traefik uses self-signed certs (no Let's Encrypt — not a real TLD). The same pattern as ollama.lan/qdrant.lan/eaas.lan.
+Note: For `.lan` domains, Traefik uses self-signed certs (no Let's Encrypt — not a real TLD). The same pattern as ollama.lthn.lan/qdrant.lthn.lan/eaas.lthn.lan.
 
 ## Step 5: Build and Start
 
@@ -225,11 +225,11 @@ Already done by snider:
 
 ## Embedding Model on GPU
 
-The `embeddinggemma` model on ollama.lan appears to be running on CPU. It's only ~256MB — should fit easily alongside whatever else is on the RX 7800 XT. Check with:
+The `embeddinggemma` model on ollama.lthn.lan appears to be running on CPU. It's only ~256MB — should fit easily alongside whatever else is on the RX 7800 XT. Check with:
 
 ```bash
 # On the Linux machine
-curl -sk https://ollama.lan/api/ps
+curl -sk https://ollama.lthn.lan/api/ps
 ```
 
 If it shows CPU, try pulling it fresh or restarting Ollama — it should auto-detect the GPU.
diff --git a/docs/plans/2026-03-03-openbrain-usage.md b/docs/plans/2026-03-03-openbrain-usage.md
index 679298f..7782ed5 100644
--- a/docs/plans/2026-03-03-openbrain-usage.md
+++ b/docs/plans/2026-03-03-openbrain-usage.md
@@ -25,8 +25,8 @@ Agent ──recall()────▶ BrainService
 
 | Service | URL | What |
 |---------|-----|------|
-| Ollama | `https://ollama.lan` | Embedding model (`embeddinggemma`, 768 dimensions) |
-| Qdrant | `https://qdrant.lan` | Vector storage + cosine similarity search |
+| Ollama | `https://ollama.lthn.lan` | Embedding model (`embeddinggemma`, 768 dimensions) |
+| Qdrant | `https://qdrant.lthn.lan` | Vector storage + cosine similarity search |
 | MariaDB | `lthn-lan-db:3306` | `brain_memories` table (workspace-scoped) |
 | Laravel | `https://lthn.lan` | BrainService, artisan commands, MCP tools |
 
@@ -80,8 +80,8 @@ If the Laravel app isn't available, use the Go brain-seed tool:
 ```bash
 cd ~/Code/go-ai
 go run cmd/brain-seed/main.go \
-  --ollama=https://ollama.lan \
-  --qdrant=https://qdrant.lan \
+  --ollama=https://ollama.lthn.lan \
+  --qdrant=https://qdrant.lthn.lan \
   --collection=openbrain \
   --model=embeddinggemma
 ```
@@ -134,14 +134,14 @@ For debugging or bulk operations:
 
 ```bash
 # Collection stats
-curl -sk https://qdrant.lan/collections/openbrain | python3 -m json.tool
+curl -sk https://qdrant.lthn.lan/collections/openbrain | python3 -m json.tool
 
 # Raw vector search (embed query first via Ollama)
-VECTOR=$(curl -sk https://ollama.lan/api/embeddings \
+VECTOR=$(curl -sk https://ollama.lthn.lan/api/embeddings \
   -d '{"model":"embeddinggemma","prompt":"Traefik setup"}' \
   | python3 -c "import sys,json; print(json.dumps(json.load(sys.stdin)['embedding']))")
 
-curl -sk https://qdrant.lan/collections/openbrain/points/search \
+curl -sk https://qdrant.lthn.lan/collections/openbrain/points/search \
   -H 'Content-Type: application/json' \
   -d "{\"vector\":$VECTOR,\"limit\":5,\"with_payload\":true}" \
   | python3 -m json.tool
@@ -215,7 +215,7 @@ php artisan brain:ingest --workspace=1 --fresh --source=memory
 ### Check Collection Health
 
 ```bash
-curl -sk https://qdrant.lan/collections/openbrain | \
+curl -sk https://qdrant.lthn.lan/collections/openbrain | \
   python3 -c "import sys,json; r=json.load(sys.stdin)['result']; print(f'Points: {r[\"points_count\"]}, Status: {r[\"status\"]}')"
 ```