diff --git a/test/lib/test_transformers.py b/test/lib/test_transformers.py
index 3e4a647..22b892c 100644
--- a/test/lib/test_transformers.py
+++ b/test/lib/test_transformers.py
@@ -5,6 +5,7 @@
 from transactron import *
 from transactron.lib.adapters import Adapter, AdapterTrans
 from transactron.lib.transformers import *
+from transactron.testing.testbenchio import CallTrigger
 from transactron.utils._typing import ModuleLike, MethodStruct, RecordDict
 from transactron.utils import ModuleConnector
 from transactron.testing import (
@@ -294,3 +295,72 @@ def elaborate(self, platform):
         m.submodules.method = self.method = TestbenchIO(AdapterTrans(product.use(m)))
 
         return m
+
+
+class NonexclusiveWrapperTestCircuit(Elaboratable):
+    def __init__(self, iosize: int, wrappers: int, callers: int):
+        self.iosize = iosize
+        self.wrappers = wrappers
+        self.callers = callers
+        self.sources: list[list[TestbenchIO]] = []
+
+    def elaborate(self, platform):
+        m = TModule()
+
+        layout = data_layout(self.iosize)
+
+        m.submodules.target = self.target = TestbenchIO(Adapter.create(i=layout, o=layout))
+
+        for i in range(self.wrappers):
+            nonex = NonexclusiveWrapper(self.target.adapter.iface).use(m)
+            sources = []
+            self.sources.append(sources)
+
+            for j in range(self.callers):
+                m.submodules[f"source_{i}_{j}"] = source = TestbenchIO(AdapterTrans(nonex))
+                sources.append(source)
+
+        return m
+
+
+class TestNonexclusiveWrapper(TestCaseWithSimulator):
+    def test_nonexclusive_wrapper(self):
+        iosize = 4
+        wrappers = 2
+        callers = 2
+        iterations = 100
+        m = NonexclusiveWrapperTestCircuit(iosize, wrappers, callers)
+
+        def caller_process(i: int):
+            async def process(sim: TestbenchContext):
+                for _ in range(iterations):
+                    j = random.randrange(callers)
+                    data = random.randrange(2**iosize)
+                    ret = await m.sources[i][j].call(sim, data=data)
+                    assert ret.data == (data + 1) % (2**iosize)
+                    await self.random_wait_geom(sim, 0.5)
+
+            return process
+
+        @def_method_mock(lambda: m.target)
+        def target(data):
+            return {"data": data + 1}
+
+        with self.run_simulation(m) as sim:
+            self.add_mock(sim, target())
+            for i in range(wrappers):
+                sim.add_testbench(caller_process(i))
+
+    def test_no_conflict(self):
+        m = NonexclusiveWrapperTestCircuit(1, 1, 2)
+
+        async def process(sim: TestbenchContext):
+            res1, res2 = await CallTrigger(sim).call(m.sources[0][0], data=1).call(m.sources[0][1], data=2).until_done()
+            assert res1 is not None and res2 is not None  # there was no conflict, however the result is undefined
+
+        @def_method_mock(lambda: m.target)
+        def target(data):
+            return {"data": data + 1}
+
+        with self.run_simulation(m) as sim:
+            sim.add_testbench(process)
diff --git a/transactron/lib/transformers.py b/transactron/lib/transformers.py
index 0a75cef..cf374f2 100644
--- a/transactron/lib/transformers.py
+++ b/transactron/lib/transformers.py
@@ -28,6 +28,7 @@
     "Collector",
     "CatTrans",
     "ConnectAndMapTrans",
+    "NonexclusiveWrapper",
 ]
 
 
@@ -453,3 +454,27 @@ def elaborate(self, platform):
         m.submodules.connect = ConnectTrans(self.method1, transformer.method)
 
         return m
+
+
+class NonexclusiveWrapper(Elaboratable, Transformer):
+    """Nonexclusive wrapper around a method.
+
+    Useful when you can assume, for external reasons, that a given method will
+    never be called more than once in a given clock cycle - even when the
+    call graph indicates it could.
+
+    Possible use case is unifying parallel pipelines with the same latency.
+    """
+
+    def __init__(self, target: Method):
+        self.target = target
+        self.method = Method.like(target)
+
+    def elaborate(self, platform):
+        m = TModule()
+
+        @def_method(m, self.method, nonexclusive=True)
+        def _(arg):
+            return self.target(m, arg)
+
+        return m