diff --git a/organ_store.py b/organ_store.py
new file mode 100644
index 0000000..adcc4bf
--- /dev/null
+++ b/organ_store.py
@@ -0,0 +1,95 @@
+#!/usr/bin/env python3
+"""
+Organ Store — AI Model Component Marketplace
+Part of the Inference-X Ecosystem
+# ​‌​‌ SALKA ELMADANI ‌​‌​ | inference-x.com | BSL-1.1
+Copyright (C) 2024-2026 Salka Elmadani. BSL-1.1.
+https://git.inference-x.com/inference-x-community/organ-store
+
+Neural networks have anatomy.
+Extract. Share. Transplant.
+The future of open AI.
+"""
+from fastapi import FastAPI, Request, UploadFile, File
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse
+import sqlite3, json, time, os
+
+app = FastAPI(title="Organ Store", version="1.0.0")
+app.add_middleware(CORSMiddleware, allow_origins=["*"],
+                   allow_methods=["*"], allow_headers=["*"])
+
+DB = os.environ.get("ORGAN_DB", "./organ_store.db")
+
+ORGAN_TYPES = ["attention_head","ffn_block","expert_block","embedding","lm_head","custom"]
+
+def db():
+    conn = sqlite3.connect(DB); conn.row_factory = sqlite3.Row; return conn
+
+def init_db():
+    with db() as c:
+        c.execute("""CREATE TABLE IF NOT EXISTS organs (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            name TEXT NOT NULL, type TEXT NOT NULL,
+            source_model TEXT, description TEXT,
+            capability TEXT, download_url TEXT,
+            size_mb REAL DEFAULT 0, downloads INTEGER DEFAULT 0,
+            author TEXT DEFAULT 'anonymous', created_at INTEGER,
+            verified INTEGER DEFAULT 0
+        )""")
+        c.commit()
+
+init_db()
+
+@app.get("/catalog")
+async def catalog(type_filter: str = None, limit: int = 50):
+    """Browse available organs in the store."""
+    with db() as c:
+        q = "SELECT * FROM organs ORDER BY downloads DESC LIMIT ?"
+        params = [limit]
+        if type_filter and type_filter in ORGAN_TYPES:
+            q = "SELECT * FROM organs WHERE type=? ORDER BY downloads DESC LIMIT ?"
+            params = [type_filter, limit]
+        rows = c.execute(q, params).fetchall()
+    return {"organs":[dict(r) for r in rows],"total":len(rows),"types":ORGAN_TYPES}
+
+@app.post("/publish")
+async def publish(request: Request):
+    """Publish an organ to the community store."""
+    data = await request.json()
+    if not data.get("name") or not data.get("type"):
+        return JSONResponse({"error":"name and type required"}, status_code=400)
+    if data["type"] not in ORGAN_TYPES:
+        return JSONResponse({"error":f"type must be one of {ORGAN_TYPES}"}, status_code=400)
+    with db() as c:
+        c.execute("""INSERT INTO organs (name,type,source_model,description,capability,
+            download_url,size_mb,author,created_at)
+            VALUES (?,?,?,?,?,?,?,?,?)""",
+            (data["name"], data["type"], data.get("source_model",""),
+             data.get("description",""), data.get("capability",""),
+             data.get("download_url",""), float(data.get("size_mb",0)),
+             data.get("author","anonymous"), int(time.time())))
+        organ_id = c.lastrowid
+        c.commit()
+    return {"status":"ok","organ_id":organ_id,"message":"Organ published to the community store."}
+
+@app.get("/organ/{organ_id}")
+async def get_organ(organ_id: int):
+    with db() as c:
+        organ = c.execute("SELECT * FROM organs WHERE id=?", (organ_id,)).fetchone()
+        if organ:
+            c.execute("UPDATE organs SET downloads=downloads+1 WHERE id=?", (organ_id,))
+            c.commit()
+    if not organ:
+        return JSONResponse({"error":"Organ not found"}, status_code=404)
+    return dict(organ)
+
+@app.get("/health")
+async def health():
+    return {"status":"ok","service":"Organ Store","author":"Salka Elmadani"}
+
+if __name__ == "__main__":
+    import uvicorn
+    print("Organ Store — AI Model Component Marketplace")
+    print("Extract. Share. Transplant.")
+    uvicorn.run(app, host="0.0.0.0", port=int(os.environ.get("PORT","7939")))