diff --git a/packages/gateway/src/learning/learning-engine.ts b/packages/gateway/src/learning/learning-engine.ts
index 6780e99..30ff004 100644
--- a/packages/gateway/src/learning/learning-engine.ts
+++ b/packages/gateway/src/learning/learning-engine.ts
@@ -90,7 +90,7 @@ async function detectImprovements(
   const routingResult = await pool.query(
     `SELECT routing_model, task_type, COUNT(*) as total, SUM(CASE WHEN was_fallback THEN 1 ELSE 0 END) as fallback_count
      FROM routing_decisions
-     WHERE created_at > NOW() - INTERVAL '1 day' * $1
+     WHERE created_at > NOW() - MAKE_INTERVAL(days => $1)
      GROUP BY routing_model, task_type
      HAVING SUM(CASE WHEN was_fallback THEN 1 ELSE 0 END)::float / COUNT(*) > 0.3
      ORDER BY fallback_count DESC
@@ -152,11 +152,15 @@ async function analyzeAndImprove(pool: any, duration: string): Promise<number> {
 
   // 2. Identify best-performing models per task type and insert recommendations
   const bestModels = await pool.query(
-    `SELECT DISTINCT ON (task_type) task_type, routing_model, success_rate, avg_latency_ms
+    `SELECT DISTINCT ON (task_type)
+       task_type,
+       routing_model,
+       ROUND((SUM(CASE WHEN success THEN 1 ELSE 0 END)::float / COUNT(*) * 100)::numeric, 2) as success_rate,
+       AVG(latency_ms)::int as avg_latency_ms
      FROM routing_decisions rd
      WHERE created_at > NOW() - INTERVAL '1 day'
-     ORDER BY task_type, (CASE WHEN success THEN 1 ELSE 0 END) DESC, latency_ms ASC
-     LIMIT 50`,
+     GROUP BY task_type, routing_model
+     ORDER BY task_type, (SUM(CASE WHEN success THEN 1 ELSE 0 END)::float / COUNT(*)) DESC, AVG(latency_ms) ASC`,
   );
 
   for (const row of bestModels.rows) {