diff --git a/image_prediction/pipeline.py b/image_prediction/pipeline.py
index 7bf84b5..d4d5024 100644
--- a/image_prediction/pipeline.py
+++ b/image_prediction/pipeline.py
@@ -1,8 +1,8 @@
 import os
 from functools import partial
-from itertools import chain, starmap
+from itertools import chain, tee
 
-from funcy import rcompose, juxt, first, compose, second, chunks, curry
+from funcy import rcompose, juxt, first, compose, second, chunks, identity
 
 from image_prediction.config import CONFIG
 from image_prediction.default_objects import get_formatter, get_mlflow_model_loader, get_image_classifier, get_extractor
@@ -21,43 +21,54 @@ def load_pipeline(**kwargs):
     return pipeline
 
 
+def parallel(*fs):
+    return lambda *args: (f(a) for f, a in zip(fs, args))
+
+
+def splat(f):
+    return lambda x: f(*x)
+
+
+def inspect(x):
+    x = list(x)
+    import IPython
+
+    IPython.embed()
+    return x
+
+
 class Pipeline:
     def __init__(self, model_loader, model_identifier, batch_size=16, **kwargs):
         extractor = get_extractor(**kwargs)
-        batcher = compose(lift(list), partial(chunks, batch_size))
         classifier = get_image_classifier(model_loader, model_identifier)
-
-        left = compose(classifier, lift(first))
-        right = lift(second)
-
         formatter = get_formatter()
 
+        batcher = partial(chunks, batch_size)
+
+        classify = compose(chain.from_iterable, lift(classifier), batcher)
+
+
         def join_prediction_and_metadata(prd, mdt):
             return {"classification": prd, **mdt}
 
-
-        # --------
-        # -- -- -- --
-        # == == == ==
-        # -- -- -- --
-        # --------
-        # --------
-
-        def inspect(x):
-            x = list(x)
-            import IPython
-            IPython.embed()
-            return x
+        #                                           +--classify--+
+        # --extract image metadata paris-->--split--|            |--zip-->-join-pairs-->format-->return
+        #                                           +--identity--+
 
         self.pipe = rcompose(
             extractor,
-            batcher,
-            lift(juxt(left, right)),
-            starlift(zip),
-            lift(starlift(join_prediction_and_metadata)),
-            chain.from_iterable,
+            tee,
+            splat(parallel(*map(lift, (first, second)))),
+            splat(parallel(classify, identity)),
+            splat(zip),
+            starlift(join_prediction_and_metadata),
             formatter,
         )
 
     def __call__(self, pdf: bytes, page_range: range = None):
-        yield from self.pipe(pdf, page_range=page_range)
+         r = self.pipe(pdf, page_range=page_range)
+
+         r = list(r)
+
+
+         return r