diff --git a/README.md b/README.md
index 115abf5..a9dc0ee 100644
--- a/README.md
+++ b/README.md
@@ -91,24 +91,6 @@ doc2 = Nx.tensor([[0.5, 0.5]], type: :f32)                          # 1 token
 scores = ExMaxsimCpu.maxsim_scores_variable(query, [doc1, doc2])
 ```
 
-### Raw Binary API (Advanced)
-
-For maximum performance when you already have raw binary data:
-
-```elixir
-# Query and docs as native-endian f32 binaries
-query_bin = <<1.0::float-32-native, 0.0::float-32-native, ...>>
-docs_bin = <<...>>
-
-scores_bin = ExMaxsimCpu.maxsim_scores_raw(
-  query_bin, q_len, dim,
-  docs_bin, n_docs, d_len
-)
-
-# Parse scores
-scores = for <<score::float-32-native <- scores_bin>>, do: score
-```
-
 ## Performance Tuning
 
 ### Environment Variables
diff --git a/lib/ex_maxsim_cpu.ex b/lib/ex_maxsim_cpu.ex
index 1f6ebe0..176ed26 100644
--- a/lib/ex_maxsim_cpu.ex
+++ b/lib/ex_maxsim_cpu.ex
@@ -8,6 +8,8 @@ defmodule ExMaxsimCpu do
 
   ## Usage with Nx Tensors
 
+  ExMaxsimCpu exposes an Nx-only public API.
+
       # Query: [q_len, dim] tensor
       query = Nx.tensor([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], type: :f32)
 
@@ -20,15 +22,6 @@ defmodule ExMaxsimCpu do
       scores = ExMaxsimCpu.maxsim_scores(query, docs)
       # => #Nx.Tensor<f32[2]>
 
-  ## Usage with Raw Binaries
-
-  For advanced use cases, you can use raw binaries directly:
-
-      query_bin = <<0.1::float-32-native, 0.2::float-32-native, ...>>
-      docs_bin = <<...>>
-
-      scores = ExMaxsimCpu.maxsim_scores_raw(query_bin, q_len, dim, docs_bin, n_docs, d_len)
-
   ## Performance Notes
 
   - Uses Dirty CPU schedulers for compute-intensive operations
@@ -108,7 +101,7 @@ defmodule ExMaxsimCpu do
   @spec maxsim_scores_variable(Nx.Tensor.t(), [Nx.Tensor.t()]) :: Nx.Tensor.t()
   def maxsim_scores_variable(_query, []) do
     raise ArgumentError,
-          "Empty document list not supported (Nx cannot create empty tensors). Use maxsim_scores_variable_raw/5 for empty lists."
+          "Empty document list not supported (Nx cannot create empty tensors)."
   end
 
   def maxsim_scores_variable(query, docs) when is_list(docs) do
@@ -145,103 +138,6 @@ defmodule ExMaxsimCpu do
     Nx.from_binary(scores_bin, :f32)
   end
 
-  @doc """
-  Compute MaxSim scores using raw binaries (advanced API).
-
-  This is a lower-level API for users who want to avoid Nx tensor overhead.
-
-  ## Parameters
-
-  - `query_bin`: Binary containing query vectors as f32 values (native endian)
-  - `q_len`: Number of query tokens
-  - `dim`: Embedding dimension
-  - `docs_bin`: Binary containing document vectors as f32 values
-  - `n_docs`: Number of documents
-  - `d_len`: Number of tokens per document (must be uniform)
-
-  ## Returns
-
-  Binary containing n_docs f32 scores.
-  """
-  @spec maxsim_scores_raw(
-          binary(),
-          pos_integer(),
-          pos_integer(),
-          binary(),
-          pos_integer(),
-          pos_integer()
-        ) ::
-          binary()
-  def maxsim_scores_raw(query_bin, q_len, dim, docs_bin, n_docs, d_len)
-      when is_binary(query_bin) and is_binary(docs_bin) and
-             is_integer(q_len) and q_len > 0 and
-             is_integer(dim) and dim > 0 and
-             is_integer(n_docs) and n_docs > 0 and
-             is_integer(d_len) and d_len > 0 do
-    expected_query_size = q_len * dim * 4
-    expected_docs_size = n_docs * d_len * dim * 4
-
-    if byte_size(query_bin) != expected_query_size do
-      raise ArgumentError,
-            "Query binary size mismatch: expected #{expected_query_size}, got #{byte_size(query_bin)}"
-    end
-
-    if byte_size(docs_bin) != expected_docs_size do
-      raise ArgumentError,
-            "Docs binary size mismatch: expected #{expected_docs_size}, got #{byte_size(docs_bin)}"
-    end
-
-    Nif.maxsim_scores_nif(query_bin, q_len, dim, docs_bin, n_docs, d_len)
-  end
-
-  @doc """
-  Compute MaxSim scores for variable-length documents using raw binaries (advanced API).
-
-  ## Parameters
-
-  - `query_bin`: Binary containing query vectors as f32 values
-  - `q_len`: Number of query tokens
-  - `dim`: Embedding dimension
-  - `doc_bins`: List of binaries, each containing a document's vectors
-  - `doc_lens`: List of token counts for each document
-
-  ## Returns
-
-  Binary containing n_docs f32 scores.
-  """
-  @spec maxsim_scores_variable_raw(binary(), pos_integer(), pos_integer(), [binary()], [
-          pos_integer()
-        ]) ::
-          binary()
-  def maxsim_scores_variable_raw(_query_bin, _q_len, _dim, [], []), do: <<>>
-
-  def maxsim_scores_variable_raw(query_bin, q_len, dim, doc_bins, doc_lens)
-      when is_binary(query_bin) and is_list(doc_bins) and is_list(doc_lens) do
-    expected_query_size = q_len * dim * 4
-
-    if byte_size(query_bin) != expected_query_size do
-      raise ArgumentError,
-            "Query binary size mismatch: expected #{expected_query_size}, got #{byte_size(query_bin)}"
-    end
-
-    if length(doc_bins) != length(doc_lens) do
-      raise ArgumentError, "doc_bins and doc_lens must have the same length"
-    end
-
-    # Validate each document binary size
-    Enum.zip(doc_bins, doc_lens)
-    |> Enum.with_index()
-    |> Enum.each(fn {{bin, len}, idx} ->
-      expected = len * dim * 4
-
-      if byte_size(bin) != expected do
-        raise ArgumentError,
-              "Doc #{idx} binary size mismatch: expected #{expected}, got #{byte_size(bin)}"
-      end
-    end)
-
-    Nif.maxsim_scores_variable_nif(query_bin, q_len, dim, doc_bins, doc_lens)
-  end
 
   # Private helpers
 
diff --git a/mix.exs b/mix.exs
index 364d1d5..92c048e 100644
--- a/mix.exs
+++ b/mix.exs
@@ -1,7 +1,7 @@
 defmodule ExMaxsimCpu.MixProject do
   use Mix.Project
 
-  @version "0.2.0"
+  @version "0.3.0"
   @source_url "https://github.com/mixedbread-ai/maxsim-cpu"
 
   def project do
diff --git a/test/ex_maxsim_cpu_test.exs b/test/ex_maxsim_cpu_test.exs
index 8cde251..d1656ff 100644
--- a/test/ex_maxsim_cpu_test.exs
+++ b/test/ex_maxsim_cpu_test.exs
@@ -92,41 +92,8 @@ defmodule ExMaxsimCpuTest do
     test "handles empty list" do
       query = Nx.tensor([[1.0, 0.0]], type: :f32)
 
-      # Nx doesn't support empty tensors, so we return an empty binary
-      scores =
-        ExMaxsimCpu.maxsim_scores_variable_raw(
-          Nx.to_binary(query),
-          1,
-          2,
-          [],
-          []
-        )
-
-      assert scores == <<>>
-    end
-  end
-
-  describe "maxsim_scores_raw/6" do
-    test "works with raw binaries" do
-      # Query: 1 token, dim 2
-      query_bin = <<1.0::float-32-native, 0.0::float-32-native>>
-
-      # 1 doc: 1 token, dim 2
-      docs_bin = <<1.0::float-32-native, 0.0::float-32-native>>
-
-      scores_bin = ExMaxsimCpu.maxsim_scores_raw(query_bin, 1, 2, docs_bin, 1, 1)
-
-      assert byte_size(scores_bin) == 4
-      <<score::float-32-native>> = scores_bin
-      assert_in_delta(score, 1.0, 1.0e-5)
-    end
-
-    test "raises on size mismatch" do
-      query_bin = <<1.0::float-32-native>>
-      docs_bin = <<1.0::float-32-native>>
-
-      assert_raise ArgumentError, ~r/Query binary size mismatch/, fn ->
-        ExMaxsimCpu.maxsim_scores_raw(query_bin, 2, 2, docs_bin, 1, 1)
+      assert_raise ArgumentError, ~r/Empty document list not supported/, fn ->
+        ExMaxsimCpu.maxsim_scores_variable(query, [])
       end
     end
   end