diff --git a/Dockerfile b/Dockerfile
index 13b7dda..33c7362 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -14,4 +14,10 @@ COPY . .
 ENV PYTHONPATH=/app
 ENV PYTHONUNBUFFERED=1
 
-EXPOSE 8000
\ No newline at end of file
+# Make entrypoint executable
+RUN chmod +x scripts/entrypoint.sh
+
+EXPOSE 8000
+
+# Default command: conditional data gen + train + serve
+CMD ["/app/scripts/entrypoint.sh"]
diff --git a/docker-compose.yml b/docker-compose.yml
index 050d230..a0a2947 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -49,44 +49,45 @@ services:
 
   api:
     build: .
-    depends_on:
-      kafka-setup:
-        condition: service_completed_successfully
     ports:
       - "8000:8000"
     volumes:
       - ./data:/app/data
       - ./ml/artifacts:/app/ml/artifacts
-    command: >
-      bash -c "
-      python -m data.generate_data &&
-      python -m ml.train &&
-      uvicorn api.main:app --host 0.0.0.0 --port 8000
-      "
+    # API is completely independent of Kafka — it only serves the scoring model.
+    # Producer/consumer gate on api health to ensure data & artifacts exist.
     healthcheck:
-      test: ["CMD", "curl", "-s", "http://localhost:8000/health", "||", "exit", "1"]
+      # CMD-SHELL gives us a real shell so || works; -f makes curl fail on 4xx/5xx
+      test: ["CMD-SHELL", "curl -sf http://localhost:8000/health || exit 1"]
       interval: 10s
       timeout: 5s
       retries: 5
+    restart: unless-stopped
 
   producer:
     build: .
     depends_on:
       api:
         condition: service_healthy
+      kafka-setup:
+        condition: service_completed_successfully
     volumes:
       - ./data:/app/data
     command: python -m kafka.producer --servers kafka:29092 --rate 50
+    restart: unless-stopped
 
   consumer:
     build: .
     depends_on:
       api:
         condition: service_healthy
+      kafka-setup:
+        condition: service_completed_successfully
     volumes:
       - ./data:/app/data
       - ./ml/artifacts:/app/ml/artifacts
     command: python -m kafka.consumer --servers kafka:29092 --mode direct --print-flagged
+    restart: unless-stopped
 
   control-center:
     image: confluentinc/cp-enterprise-control-center:7.5.0
@@ -103,4 +104,4 @@ services:
       CONTROL_CENTER_INTERNAL_TOPICS_PARTITIONS: 1
       CONTROL_CENTER_MONITORING_INTERCEPTOR_TOPIC_PARTITIONS: 1
       CONFLUENT_METRICS_TOPIC_REPLICATION: 1
-      PORT: 9021
\ No newline at end of file
+      PORT: 9021
diff --git a/scripts/entrypoint.sh b/scripts/entrypoint.sh
new file mode 100644
index 0000000..cfbf1bf
--- /dev/null
+++ b/scripts/entrypoint.sh
@@ -0,0 +1,28 @@
+#!/bin/bash
+set -euo pipefail
+
+ARTIFACTS_DIR="ml/artifacts"
+DATA_FILE="data/transactions.csv"
+
+echo "[entrypoint] Fraud Radar API container starting..."
+
+# Generate synthetic data if missing (idempotent — same seed every time)
+if [ ! -f "$DATA_FILE" ]; then
+    echo "[entrypoint] Synthetic data not found at $DATA_FILE — generating..."
+    python -m data.generate_data
+else
+    echo "[entrypoint] Using existing data: $DATA_FILE ($(wc -l < "$DATA_FILE" | tr -d ' ') rows)"
+fi
+
+# Train model if any artifact is missing — avoids retraining on every restart
+if [ ! -f "$ARTIFACTS_DIR/isolation_forest.pkl" ] || \
+   [ ! -f "$ARTIFACTS_DIR/scaler.pkl" ] || \
+   [ ! -f "$ARTIFACTS_DIR/calibration.json" ]; then
+    echo "[entrypoint] Model artifacts missing — training Isolation Forest..."
+    python -m ml.train
+else
+    echo "[entrypoint] Using existing model artifacts."
+fi
+
+echo "[entrypoint] Starting API server on 0.0.0.0:8000..."
+exec uvicorn api.main:app --host 0.0.0.0 --port 8000