diff --git a/surfsense_backend/app/indexing_pipeline/cache/serialization.py b/surfsense_backend/app/indexing_pipeline/cache/serialization.py
new file mode 100644
index 000000000..f9d53b471
--- /dev/null
+++ b/surfsense_backend/app/indexing_pipeline/cache/serialization.py
@@ -0,0 +1,71 @@
+"""Serialize an EmbeddingSet to a compact, self-describing blob (no pickle).
+
+Layout: ``MAGIC | uint32 header_len | json header | float32 matrix``. The header
+carries the dim, chunk count, and ordered chunk texts; the matrix holds the
+summary vector followed by one row per chunk, all float32 for compactness.
+"""
+
+from __future__ import annotations
+
+import json
+import struct
+
+import numpy as np
+
+from app.indexing_pipeline.cache.schemas import CachedChunk, EmbeddingSet
+
+# Marker at the start of every blob: "SurfSense EMBeddings, version 1"-> SSEMB1. Lets us
+# reject foreign blobs and bump the trailing digit if the layout ever changes.
+_MAGIC = b"SSEMB1"
+# 4-byte big-endian unsigned int written before the variable-length JSON header,
+# so the reader knows where the header ends and the float matrix begins.
+_HEADER_LEN = struct.Struct(">I")
+
+
+def serialize(embedding_set: EmbeddingSet) -> bytes:
+    summary = np.asarray(embedding_set.summary_embedding, dtype=np.float32).reshape(-1)
+    dim = int(summary.shape[0])
+
+    rows = [summary]
+    texts: list[str] = []
+    for chunk in embedding_set.chunks:
+        vector = np.asarray(chunk.embedding, dtype=np.float32).reshape(-1)
+        if vector.shape[0] != dim:
+            raise ValueError("All vectors in an embedding set must share one dimension.")
+        rows.append(vector)
+        texts.append(chunk.text)
+
+    matrix = np.stack(rows, axis=0)
+    header = json.dumps(
+        {"dim": dim, "count": len(texts), "texts": texts}, ensure_ascii=False
+    ).encode("utf-8")
+    return b"".join(
+        [_MAGIC, _HEADER_LEN.pack(len(header)), header, matrix.tobytes(order="C")]
+    )
+
+
+def deserialize(blob: bytes) -> EmbeddingSet:
+    view = memoryview(blob)
+    if bytes(view[: len(_MAGIC)]) != _MAGIC:
+        raise ValueError("Unrecognized embedding cache blob.")
+
+    offset = len(_MAGIC)
+    (header_len,) = _HEADER_LEN.unpack(view[offset : offset + _HEADER_LEN.size])
+    offset += _HEADER_LEN.size
+
+    header = json.loads(bytes(view[offset : offset + header_len]).decode("utf-8"))
+    offset += header_len
+
+    dim = int(header["dim"])
+    count = int(header["count"])
+    texts: list[str] = header["texts"]
+
+    matrix = np.frombuffer(view[offset:], dtype=np.float32)
+    if matrix.shape[0] != (count + 1) * dim:
+        raise ValueError("Embedding cache blob is truncated or corrupt.")
+    matrix = matrix.reshape(count + 1, dim)
+
+    return EmbeddingSet(
+        summary_embedding=matrix[0],
+        chunks=[CachedChunk(text=texts[i], embedding=matrix[i + 1]) for i in range(count)],
+    )