diff --git a/.gitignore b/.gitignore
index c45ff83..3ea898e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,6 +1,7 @@
 __pycache__
 *.pyc
 .venv
+.venv*
 .DS_Store
 venv/
 /.vscode
diff --git a/poetry.lock b/poetry.lock
index 6f4f74c..ae89569 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,114 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+
+[[package]]
+name = "aiohttp"
+version = "3.9.5"
+description = "Async http client/server framework (asyncio)"
+optional = false
+python-versions = ">=3.8"
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+]
+
+[package.dependencies]
+aiosignal = ">=1.1.2"
+async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
+attrs = ">=17.3.0"
+frozenlist = ">=1.1.1"
+multidict = ">=4.5,<7.0"
+yarl = ">=1.0,<2.0"
+
+[package.extras]
+speedups = ["Brotli", "aiodns", "brotlicffi"]
+
+[[package]]
+name = "aiosignal"
+version = "1.3.1"
+description = "aiosignal: a list of registered asynchronous callbacks"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "aiosignal-1.3.1-py3-none-any.whl", hash = "sha256:f8376fb07dd1e86a584e4fcdec80b36b7f81aac666ebc724e2c090300dd83b17"},
+    {file = "aiosignal-1.3.1.tar.gz", hash = "sha256:54cd96e15e1649b75d6c87526a6ff0b6c1b0dd3459f43d9ca11d48c339b68cfc"},
+]
+
+[package.dependencies]
+frozenlist = ">=1.1.0"
 
 [[package]]
 name = "annotated-types"
@@ -13,13 +123,13 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "4.3.0"
+version = "4.4.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "anyio-4.3.0-py3-none-any.whl", hash = "sha256:048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8"},
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+    {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"},
+    {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"},
 ]
 
 [package.dependencies]
@@ -62,6 +172,36 @@ six = ">=1.12.0"
 astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
 test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
 
+[[package]]
+name = "async-timeout"
+version = "4.0.3"
+description = "Timeout context manager for asyncio programs"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "async-timeout-4.0.3.tar.gz", hash = "sha256:4640d96be84d82d02ed59ea2b7105a0f7b33abe8703703cd0ab0bf87c427522f"},
+    {file = "async_timeout-4.0.3-py3-none-any.whl", hash = "sha256:7405140ff1230c310e51dc27b3145b9092d659ce68ff733fb0cefe3ee42be028"},
+]
+
+[[package]]
+name = "attrs"
+version = "23.2.0"
+description = "Classes Without Boilerplate"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
+[package.extras]
+cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
+dev = ["attrs[tests]", "pre-commit"]
+docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
+tests = ["attrs[tests-no-zope]", "zope-interface"]
+tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
+tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
+
 [[package]]
 name = "black"
 version = "24.4.2"
@@ -112,17 +252,17 @@ uvloop = ["uvloop (>=0.15.2)"]
 
 [[package]]
 name = "boto3"
-version = "1.34.110"
+version = "1.34.138"
 description = "The AWS SDK for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "boto3-1.34.138-py3-none-any.whl", hash = "sha256:81518aa95fad71279411fb5c94da4b4a554a5d53fc876faca62b7b5c8737f1cb"},
+    {file = "boto3-1.34.138.tar.gz", hash = "sha256:f79c15e33eb7706f197d98d828b193cf0891966682ad3ec5e900f6f9e7362e35"},
 ]
 
 [package.dependencies]
-botocore = ">=1.34.110,<1.35.0"
+botocore = ">=1.34.138,<1.35.0"
 jmespath = ">=0.7.1,<2.0.0"
 s3transfer = ">=0.10.0,<0.11.0"
 
@@ -131,13 +271,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
 name = "botocore"
-version = "1.34.110"
+version = "1.34.138"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "botocore-1.34.138.tar.gz", hash = "sha256:f558bbea96c4a4abbaeeedc477dabb00902311ba1ca6327974a6819b9f384920"},
 ]
 
 [package.dependencies]
@@ -149,17 +289,17 @@ urllib3 = [
 ]
 
 [package.extras]
-crt = ["awscrt (==0.20.9)"]
+crt = ["awscrt (==0.20.11)"]
 
 [[package]]
 name = "certifi"
-version = "2024.2.2"
+version = "2024.6.2"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
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 ]
 
 [[package]]
@@ -341,13 +481,13 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [[package]]
 name = "cohere"
-version = "5.5.0"
+version = "5.5.8"
 description = ""
 optional = false
 python-versions = "<4.0,>=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -355,9 +495,10 @@ boto3 = ">=1.34.0,<2.0.0"
 fastavro = ">=1.9.4,<2.0.0"
 httpx = ">=0.21.2"
 httpx-sse = ">=0.4.0,<0.5.0"
+parameterized = ">=0.9.0,<0.10.0"
 pydantic = ">=1.9.2"
 requests = ">=2.0.0,<3.0.0"
-tokenizers = ">=0.19,<0.20"
+tokenizers = ">=0.15,<1"
 types-requests = ">=2.0.0,<3.0.0"
 typing_extensions = ">=4.0.0"
 
@@ -471,63 +612,63 @@ test-no-images = ["pytest", "pytest-cov", "pytest-xdist", "wurlitzer"]
 
 [[package]]
 name = "coverage"
-version = "7.5.1"
+version = "7.5.4"
 description = "Code coverage measurement for Python"
 optional = false
 python-versions = ">=3.8"
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 [package.dependencies]
@@ -553,33 +694,33 @@ tests = ["pytest", "pytest-cov", "pytest-xdist"]
 
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 ]
 
 [[package]]
@@ -694,69 +835,69 @@ zstandard = ["zstandard"]
 
 [[package]]
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 typing = ["typing-extensions (>=4.8)"]
 
 [[package]]
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 optional = true
 python-versions = ">=3.8"
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 unicode = ["unicodedata2 (>=15.1.0)"]
 woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
 
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+version = "2024.6.1"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.extras]
@@ -790,6 +1017,7 @@ adl = ["adlfs"]
 arrow = ["pyarrow (>=1)"]
 dask = ["dask", "distributed"]
 dev = ["pre-commit", "ruff"]
+doc = ["numpydoc", "sphinx", "sphinx-design", "sphinx-rtd-theme", "yarl"]
 dropbox = ["dropbox", "dropboxdrivefs", "requests"]
 full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
 fuse = ["fusepy"]
@@ -880,13 +1108,13 @@ files = [
 
 [[package]]
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+version = "0.23.4"
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 python-versions = ">=3.8.0"
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 ]
 
 [package.dependencies]
@@ -925,22 +1153,22 @@ files = [
 
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 perf = ["ipython"]
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 [[package]]
 name = "importlib-resources"
@@ -973,13 +1201,13 @@ files = [
 
 [[package]]
 name = "ipykernel"
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+version = "6.29.5"
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 python-versions = ">=3.8"
 files = [
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@@ -1073,13 +1301,13 @@ files = [
 
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+version = "8.6.2"
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 [[package]]
 name = "jupyter-core"
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 fpx = ["olefile"]
 mic = ["olefile"]
 tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
@@ -1601,13 +1953,13 @@ testing = ["pytest", "pytest-benchmark"]
 
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 [package.dependencies]
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 [package.extras]
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 [package.extras]
 testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
 
+[[package]]
+name = "pytest-asyncio"
+version = "0.23.7"
+description = "Pytest support for asyncio"
+optional = false
+python-versions = ">=3.8"
+files = [
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+docs = ["sphinx (>=5.3)", "sphinx-rtd-theme (>=1.0)"]
+testing = ["coverage (>=6.2)", "hypothesis (>=5.7.1)"]
+
 [[package]]
 name = "pytest-cov"
 version = "4.1.0"
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 [[package]]
 name = "requests"
-version = "2.32.2"
+version = "2.32.3"
 description = "Python HTTP for Humans."
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 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
@@ -2257,13 +2637,13 @@ files = [
 
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+version = "0.10.2"
 description = "An Amazon S3 Transfer Manager"
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-python-versions = ">= 3.8"
+python-versions = ">=3.8"
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 [[package]]
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-version = "0.0.44"
+version = "0.0.48"
 description = "Super fast semantic router for AI decision making"
 optional = false
 python-versions = "<3.13,>=3.9"
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 cohere = ">=5.00,<6.00"
 colorama = ">=0.4.6,<0.5.0"
 colorlog = ">=6.8.0,<7.0.0"
@@ -2296,16 +2677,16 @@ requests-mock = ">=1.12.1,<2.0.0"
 tiktoken = ">=0.6.0,<1.0.0"
 
 [package.extras]
-bedrock = ["boto3 (>=1.34.98,<2.0.0)"]
-fastembed = ["fastembed (>=0.2.4,<0.3.0)"]
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 [[package]]
 name = "six"
@@ -2541,22 +2922,22 @@ files = [
 
 [[package]]
 name = "tornado"
-version = "6.4"
+version = "6.4.1"
 description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
 optional = false
-python-versions = ">= 3.8"
+python-versions = ">=3.8"
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 ]
 
 [[package]]
@@ -2621,13 +3002,13 @@ types-urllib3 = "*"
 
 [[package]]
 name = "types-requests"
-version = "2.32.0.20240521"
+version = "2.32.0.20240622"
 description = "Typing stubs for requests"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2646,24 +3027,24 @@ files = [
 
 [[package]]
 name = "typing-extensions"
-version = "4.11.0"
+version = "4.12.2"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
 name = "urllib3"
-version = "1.26.18"
+version = "1.26.19"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
 files = [
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 ]
 
 [package.extras]
@@ -2673,13 +3054,13 @@ socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
 
 [[package]]
 name = "urllib3"
-version = "2.2.1"
+version = "2.2.2"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -2699,20 +3080,123 @@ files = [
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 ]
 
+[[package]]
+name = "yarl"
+version = "1.9.4"
+description = "Yet another URL library"
+optional = false
+python-versions = ">=3.7"
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+    {file = "yarl-1.9.4.tar.gz", hash = "sha256:566db86717cf8080b99b58b083b773a908ae40f06681e87e589a976faf8246bf"},
+]
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+
 [[package]]
 name = "zipp"
-version = "3.18.2"
+version = "3.19.2"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.18.2-py3-none-any.whl", hash = "sha256:dce197b859eb796242b0622af1b8beb0a722d52aa2f57133ead08edd5bf5374e"},
-    {file = "zipp-3.18.2.tar.gz", hash = "sha256:6278d9ddbcfb1f1089a88fde84481528b07b0e10474e09dcfe53dad4069fa059"},
+    {file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"},
+    {file = "zipp-3.19.2.tar.gz", hash = "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19"},
 ]
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
 
 [extras]
 stats = ["matplotlib"]
@@ -2720,4 +3204,4 @@ stats = ["matplotlib"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.13"
-content-hash = "7c3cc56a4859e97f65aa77ab55d233f29feef225b2f14870dd70198dcb3339ac"
+content-hash = "5d6aa477eca05abe2412924ca9b9121bc5aab3746cbc4ba4d73e9a3bb44d2f15"
diff --git a/pyproject.toml b/pyproject.toml
index a44dc5d..c4ffd14 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "semantic-chunkers"
-version = "0.0.6"
+version = "0.0.7"
 description = "Super advanced chunking methods for AI"
 authors = ["Aurelio AI <hello@aurelio.ai>"]
 readme = "README.md"
@@ -16,7 +16,7 @@ regex = "^2023.12.25"
 tiktoken = ">=0.7.0,<1.0.0"
 matplotlib = { version = "^3.8.3", optional = true}
 requests-mock = "^1.12.1"
-semantic-router = ">=0.0.20,<0.1.0"
+semantic-router = ">=0.0.48,<0.1.0"
 
 [tool.poetry.extras]
 stats = ["matplotlib"]
@@ -28,6 +28,7 @@ pytest = "^7.4.3"
 pytest-mock = "^3.12.0"
 pytest-cov = "^4.1.0"
 pytest-xdist = "^3.5.0"
+pytest-asyncio = "^0.23.7"
 mypy = "^1.7.1"
 types-pyyaml = "^6.0.12.12"
 types-requests = "^2.31.0"
diff --git a/semantic_chunkers/chunkers/base.py b/semantic_chunkers/chunkers/base.py
index 4351e57..2e55e5c 100644
--- a/semantic_chunkers/chunkers/base.py
+++ b/semantic_chunkers/chunkers/base.py
@@ -2,8 +2,8 @@
 
 from colorama import Fore, Style
 from pydantic.v1 import BaseModel, Extra
-
 from semantic_router.encoders.base import BaseEncoder
+
 from semantic_chunkers.schema import Chunk
 from semantic_chunkers.splitters.base import BaseSplitter
 
diff --git a/semantic_chunkers/chunkers/consecutive.py b/semantic_chunkers/chunkers/consecutive.py
index 1b5664f..c8c8541 100644
--- a/semantic_chunkers/chunkers/consecutive.py
+++ b/semantic_chunkers/chunkers/consecutive.py
@@ -1,11 +1,11 @@
 from typing import Any, List
-from tqdm.auto import tqdm
 
 import numpy as np
-
 from semantic_router.encoders.base import BaseEncoder
-from semantic_chunkers.schema import Chunk
+from tqdm.auto import tqdm
+
 from semantic_chunkers.chunkers.base import BaseChunker
+from semantic_chunkers.schema import Chunk
 from semantic_chunkers.splitters.base import BaseSplitter
 from semantic_chunkers.splitters.sentence import RegexSplitter
 
@@ -58,6 +58,40 @@ def _chunk(self, splits: List[Any], batch_size: int = 64) -> List[Chunk]:
         self.chunks = chunks
         return chunks
 
+    async def _async_chunk(
+        self, splits: List[Any], batch_size: int = 64
+    ) -> List[Chunk]:
+        """Merge splits into chunks using semantic similarity.
+
+        :param splits: splits to be merged into chunks.
+
+        :return: list of chunks.
+        """
+        split_embeds = []
+        num_splits = len(splits)
+        for i in tqdm(range(0, num_splits, batch_size)):
+            split_embeds.extend(await self.encoder.acall(splits[i : i + batch_size]))
+        norm_embeds = split_embeds / np.linalg.norm(split_embeds, axis=1, keepdims=True)
+        sim_matrix = np.matmul(norm_embeds, norm_embeds.T)
+        chunks = []
+        curr_split_start_idx = 0
+
+        for idx in tqdm(range(1, norm_embeds.shape[0])):
+            curr_sim_score = sim_matrix[idx - 1][idx]
+            if idx < len(sim_matrix) and curr_sim_score < self.score_threshold:
+                chunks.append(
+                    Chunk(
+                        splits=splits[curr_split_start_idx:idx],
+                        is_triggered=True,
+                        triggered_score=curr_sim_score,
+                    )
+                )
+                curr_split_start_idx = idx
+        # append final chunk
+        chunks.append(Chunk(splits=splits[curr_split_start_idx:]))
+        self.chunks = chunks
+        return chunks
+
     def __call__(self, docs: List[Any]) -> List[List[Chunk]]:
         """Split documents into smaller chunks based on semantic similarity.
 
@@ -76,3 +110,15 @@ def __call__(self, docs: List[Any]) -> List[List[Chunk]]:
             doc_chunks = self._chunk(splits)
             all_chunks.append(doc_chunks)
         return all_chunks
+
+    async def acall(self, docs: List[Any]) -> List[List[Chunk]]:
+        all_chunks = []
+        for doc in docs:
+            # split the document into sentences (if needed)
+            if isinstance(doc, str):
+                splits = self._split(doc)
+            else:
+                splits = doc
+            doc_chunks = await self._async_chunk(splits)
+            all_chunks.append(doc_chunks)
+        return all_chunks
diff --git a/semantic_chunkers/chunkers/cumulative.py b/semantic_chunkers/chunkers/cumulative.py
index 973952e..b538b14 100644
--- a/semantic_chunkers/chunkers/cumulative.py
+++ b/semantic_chunkers/chunkers/cumulative.py
@@ -1,11 +1,11 @@
 from typing import Any, List
-from tqdm.auto import tqdm
 
 import numpy as np
-
 from semantic_router.encoders import BaseEncoder
-from semantic_chunkers.schema import Chunk
+from tqdm.auto import tqdm
+
 from semantic_chunkers.chunkers.base import BaseChunker
+from semantic_chunkers.schema import Chunk
 from semantic_chunkers.splitters.base import BaseSplitter
 from semantic_chunkers.splitters.sentence import RegexSplitter
 
@@ -76,6 +76,62 @@ def _chunk(self, splits: List[Any], batch_size: int = 64) -> List[Chunk]:
 
         return chunks
 
+    async def _async_chunk(
+        self, splits: List[Any], batch_size: int = 64
+    ) -> List[Chunk]:
+        """Merge splits into chunks using semantic similarity.
+
+        :param splits: splits to be merged into chunks.
+
+        :return: list of chunks.
+        """
+        chunks = []
+        curr_chunk_start_idx = 0
+        num_splits = len(splits)
+
+        for idx in tqdm(range(num_splits)):
+            if idx + 1 < num_splits:  # Ensure there is a next document to compare with.
+                if idx == 0:
+                    # On the first iteration, compare the
+                    # first document directly to the second.
+                    curr_chunk_docs = splits[idx]
+                else:
+                    # For subsequent iterations, compare cumulative
+                    # documents up to the current one with the next.
+                    curr_chunk_docs = "\n".join(splits[curr_chunk_start_idx : idx + 1])
+                next_doc = splits[idx + 1]
+
+                # Embedding and similarity calculation remains the same.
+                curr_chunk_docs_embed_result = await self.encoder.acall(
+                    [curr_chunk_docs]
+                )
+                next_doc_embed_result = await self.encoder.acall([next_doc])
+                curr_chunk_docs_embed = curr_chunk_docs_embed_result[0]
+                next_doc_embed = next_doc_embed_result[0]
+
+                curr_sim_score = np.dot(curr_chunk_docs_embed, next_doc_embed) / (
+                    np.linalg.norm(curr_chunk_docs_embed)
+                    * np.linalg.norm(next_doc_embed)
+                )
+                # Decision to chunk based on similarity score.
+                if curr_sim_score < self.score_threshold:
+                    chunks.append(
+                        Chunk(
+                            splits=list(splits[curr_chunk_start_idx : idx + 1]),
+                            is_triggered=True,
+                            triggered_score=curr_sim_score,
+                        )
+                    )
+                    curr_chunk_start_idx = (
+                        idx + 1
+                    )  # Update the start index for the next segment.
+
+        # Add the last segment after the loop.
+        if curr_chunk_start_idx < num_splits:
+            chunks.append(Chunk(splits=list(splits[curr_chunk_start_idx:])))
+
+        return chunks
+
     def __call__(self, docs: List[str]) -> List[List[Chunk]]:
         """Split documents into smaller chunks based on semantic similarity.
 
@@ -94,3 +150,22 @@ def __call__(self, docs: List[str]) -> List[List[Chunk]]:
             doc_chunks = self._chunk(splits)
             all_chunks.append(doc_chunks)
         return all_chunks
+
+    async def acall(self, docs: List[str]) -> List[List[Chunk]]:
+        """Split documents into smaller chunks based on semantic similarity.
+
+        :param docs: list of text documents to be chunk, if only wanted to
+            chunk a single document, pass it as a list with a single element.
+
+        :return: list of list objects containing the chunks.
+        """
+        all_chunks = []
+        for doc in docs:
+            # split the document into sentences (if needed)
+            if isinstance(doc, str):
+                splits = self._split(doc)
+            else:
+                splits = doc
+            doc_chunks = await self._async_chunk(splits)
+            all_chunks.append(doc_chunks)
+        return all_chunks
diff --git a/semantic_chunkers/chunkers/statistical.py b/semantic_chunkers/chunkers/statistical.py
index a234a24..97e5629 100644
--- a/semantic_chunkers/chunkers/statistical.py
+++ b/semantic_chunkers/chunkers/statistical.py
@@ -2,16 +2,15 @@
 from typing import Any, List
 
 import numpy as np
-
 from semantic_router.encoders.base import BaseEncoder
-from semantic_chunkers.schema import Chunk
+from tqdm.auto import tqdm
+
 from semantic_chunkers.chunkers.base import BaseChunker
+from semantic_chunkers.schema import Chunk
 from semantic_chunkers.splitters.base import BaseSplitter
 from semantic_chunkers.splitters.sentence import RegexSplitter
-from semantic_chunkers.utils.text import tiktoken_length
 from semantic_chunkers.utils.logger import logger
-
-from tqdm.auto import tqdm
+from semantic_chunkers.utils.text import tiktoken_length
 
 
 @dataclass
@@ -66,15 +65,18 @@ def __init__(
         self.split_tokens_tolerance = split_tokens_tolerance
         self.enable_statistics = enable_statistics
         self.statistics: ChunkStatistics
+        self.DEFAULT_THRESHOLD = 0.5
 
     def _chunk(
         self, splits: List[Any], batch_size: int = 64, enforce_max_tokens: bool = False
     ) -> List[Chunk]:
-        """Merge splits into chunks using semantic similarity, with optional enforcement of maximum token limits per chunk.
+        """Merge splits into chunks using semantic similarity, with optional enforcement
+        of maximum token limits per chunk.
 
         :param splits: Splits to be merged into chunks.
         :param batch_size: Number of splits to process in one batch.
-        :param enforce_max_tokens: If True, further split chunks that exceed the maximum token limit.
+        :param enforce_max_tokens: If True, further split chunks that exceed the maximum
+        token limit.
 
         :return: List of chunks.
         """
@@ -108,7 +110,81 @@ def _chunk(
             if self.dynamic_threshold:
                 self._find_optimal_threshold(batch_splits, similarities)
             else:
-                self.calculated_threshold = self.encoder.score_threshold
+                self.calculated_threshold = (
+                    self.encoder.score_threshold
+                    if self.encoder.score_threshold
+                    else self.DEFAULT_THRESHOLD
+                )
+            split_indices = self._find_split_indices(similarities=similarities)
+            doc_chunks = self._split_documents(
+                batch_splits, split_indices, similarities
+            )
+
+            if len(doc_chunks) > 1:
+                chunks.extend(doc_chunks[:-1])
+                last_split = doc_chunks[-1]
+            else:
+                last_split = doc_chunks[0]
+
+            if self.plot_chunks:
+                self.plot_similarity_scores(similarities, split_indices, doc_chunks)
+
+            if self.enable_statistics:
+                print(self.statistics)
+
+        if last_split:
+            chunks.append(last_split)
+
+        return chunks
+
+    async def _async_chunk(
+        self, splits: List[Any], batch_size: int = 64, enforce_max_tokens: bool = False
+    ) -> List[Chunk]:
+        """Merge splits into chunks using semantic similarity, with optional enforcement
+        of maximum token limits per chunk.
+
+        :param splits: Splits to be merged into chunks.
+        :param batch_size: Number of splits to process in one batch.
+        :param enforce_max_tokens: If True, further split chunks that exceed the maximum
+        token limit.
+
+        :return: List of chunks.
+        """
+        # Split the docs that already exceed max_split_tokens to smaller chunks
+        if enforce_max_tokens:
+            new_splits = []
+            for split in splits:
+                token_count = tiktoken_length(split)
+                if token_count > self.max_split_tokens:
+                    logger.info(
+                        f"Single document exceeds the maximum token limit "
+                        f"of {self.max_split_tokens}. "
+                        "Splitting to sentences before semantically merging."
+                    )
+                    _splits = self._split(split)
+                    new_splits.extend(_splits)
+                else:
+                    new_splits.append(split)
+
+            splits = [split for split in new_splits if split and split.strip()]
+
+        chunks = []
+        last_split = None
+        for i in tqdm(range(0, len(splits), batch_size)):
+            batch_splits = splits[i : i + batch_size]
+            if last_split is not None:
+                batch_splits = last_split.splits + batch_splits
+
+            encoded_splits = await self._async_encode_documents(batch_splits)
+            similarities = self._calculate_similarity_scores(encoded_splits)
+            if self.dynamic_threshold:
+                self._find_optimal_threshold(batch_splits, similarities)
+            else:
+                self.calculated_threshold = (
+                    self.encoder.score_threshold
+                    if self.encoder.score_threshold
+                    else self.DEFAULT_THRESHOLD
+                )
             split_indices = self._find_split_indices(similarities=similarities)
             doc_chunks = self._split_documents(
                 batch_splits, split_indices, similarities
@@ -159,11 +235,39 @@ def __call__(self, docs: List[str], batch_size: int = 64) -> List[List[Chunk]]:
                 raise ValueError("The document must be a string.")
         return all_chunks
 
+    async def acall(self, docs: List[str], batch_size: int = 64) -> List[List[Chunk]]:
+        """Split documents into smaller chunks based on semantic similarity.
+
+        :param docs: list of text documents to be split, if only wanted to
+            split a single document, pass it as a list with a single element.
+
+        :return: list of Chunk objects containing the split documents.
+        """
+        if not docs:
+            raise ValueError("At least one document is required for splitting.")
+
+        all_chunks = []
+        for doc in docs:
+            token_count = tiktoken_length(doc)
+            if token_count > self.max_split_tokens:
+                logger.info(
+                    f"Single document exceeds the maximum token limit "
+                    f"of {self.max_split_tokens}. "
+                    "Splitting to sentences before semantically merging."
+                )
+            if isinstance(doc, str):
+                splits = self._split(doc)
+                doc_chunks = await self._async_chunk(splits, batch_size=batch_size)
+                all_chunks.append(doc_chunks)
+            else:
+                raise ValueError("The document must be a string.")
+        return all_chunks
+
     def _encode_documents(self, docs: List[str]) -> np.ndarray:
         """
-        Encodes a list of documents into embeddings. If the number of documents exceeds 2000,
-        the documents are split into batches to avoid overloading the encoder. OpenAI has a
-        limit of len(array) < 2048.
+        Encodes a list of documents into embeddings. If the number of documents
+        exceeds 2000, the documents are split into batches to avoid overloading
+        the encoder. OpenAI has a limit of len(array) < 2048.
 
         :param docs: List of text documents to be encoded.
         :return: A numpy array of embeddings for the given documents.
@@ -182,6 +286,29 @@ def _encode_documents(self, docs: List[str]) -> np.ndarray:
 
         return np.array(embeddings)
 
+    async def _async_encode_documents(self, docs: List[str]) -> np.ndarray:
+        """
+        Encodes a list of documents into embeddings. If the number of documents
+        exceeds 2000, the documents are split into batches to avoid overloading
+        the encoder. OpenAI has a limit of len(array) < 2048.
+
+        :param docs: List of text documents to be encoded.
+        :return: A numpy array of embeddings for the given documents.
+        """
+        max_docs_per_batch = 2000
+        embeddings = []
+
+        for i in range(0, len(docs), max_docs_per_batch):
+            batch_docs = docs[i : i + max_docs_per_batch]
+            try:
+                batch_embeddings = await self.encoder.acall(batch_docs)
+                embeddings.extend(batch_embeddings)
+            except Exception as e:
+                logger.error(f"Error encoding documents {batch_docs}: {e}")
+                raise
+
+        return np.array(embeddings)
+
     def _calculate_similarity_scores(self, encoded_docs: np.ndarray) -> List[float]:
         raw_similarities = []
         for idx in range(1, len(encoded_docs)):
diff --git a/semantic_chunkers/splitters/__init__.py b/semantic_chunkers/splitters/__init__.py
index c6d858a..5b3d258 100644
--- a/semantic_chunkers/splitters/__init__.py
+++ b/semantic_chunkers/splitters/__init__.py
@@ -1,7 +1,6 @@
 from semantic_chunkers.splitters.base import BaseSplitter
 from semantic_chunkers.splitters.sentence import RegexSplitter
 
-
 __all__ = [
     "BaseSplitter",
     "RegexSplitter",
diff --git a/semantic_chunkers/splitters/sentence.py b/semantic_chunkers/splitters/sentence.py
index cd8b2b3..667cc91 100644
--- a/semantic_chunkers/splitters/sentence.py
+++ b/semantic_chunkers/splitters/sentence.py
@@ -1,6 +1,7 @@
-import regex
 from typing import List
 
+import regex
+
 from semantic_chunkers.splitters.base import BaseSplitter
 
 
diff --git a/tests/unit/test_splitters.py b/tests/unit/test_splitters.py
index b37e891..8ef8139 100644
--- a/tests/unit/test_splitters.py
+++ b/tests/unit/test_splitters.py
@@ -1,14 +1,17 @@
-from unittest.mock import Mock, create_autospec
+from unittest.mock import AsyncMock, Mock, create_autospec
 
 import numpy as np
 import pytest
-
 from semantic_router.encoders.base import BaseEncoder
 from semantic_router.encoders.cohere import CohereEncoder
-from semantic_chunkers import BaseChunker
-from semantic_chunkers import BaseSplitter
-from semantic_chunkers import ConsecutiveChunker
-from semantic_chunkers import CumulativeChunker
+
+from semantic_chunkers import (
+    BaseChunker,
+    BaseSplitter,
+    ConsecutiveChunker,
+    CumulativeChunker,
+    StatisticalChunker,
+)
 
 
 def test_consecutive_sim_splitter():
@@ -41,6 +44,41 @@ def test_consecutive_sim_splitter():
     ], "Second split does not match expected documents"
 
 
+@pytest.mark.asyncio
+async def test_async_consecutive_sim_splitter():
+    # Create a Mock object for the encoder
+    mock_encoder = AsyncMock()
+
+    async def async_return(*args, **kwargs):
+        return np.array([[1, 0], [1, 0.1], [0, 1]])
+
+    mock_encoder.acall.side_effect = async_return
+
+    cohere_encoder = CohereEncoder(
+        name="",
+        cohere_api_key="a",
+    )
+    # Instantiate the ConsecutiveSimSplitter with the mock encoder
+    splitter = ConsecutiveChunker(encoder=cohere_encoder, score_threshold=0.9)
+    splitter.encoder = mock_encoder
+
+    # Define some documents
+    docs = ["doc1 about something", "doc2 about something", "doc3 about something"]
+
+    # Use the splitter to split the documents
+    splits = await splitter.acall(docs)
+
+    # Verify the splits
+    print(splits)
+    assert len(splits) == 3, "Expected three sets of chunks"
+    assert splits[0][0].splits == [
+        "doc1 about something"
+    ], "First split does not match expected documents"
+    assert splits[2][0].splits == [
+        "doc3 about something"
+    ], "Second split does not match expected documents"
+
+
 def test_cumulative_sim_splitter():
     # Mock the BaseEncoder
     mock_encoder = Mock()
@@ -76,6 +114,42 @@ def test_cumulative_sim_splitter():
     assert len(splits) == 5, f"{len(splits)}"
 
 
+@pytest.mark.asyncio
+async def test_async_cumulative_sim_splitter():
+    # Mock the BaseEncoder
+    mock_encoder = AsyncMock()
+    # Adjust the side_effect to simulate the encoder's behavior for cumulative document comparisons
+    # This simplistic simulation assumes binary embeddings for demonstration purposes
+    # Define a side_effect function for the mock encoder
+    mock_encoder.side_effect = lambda x: (
+        [[0.5, 0]] if "doc1" in x or "doc1\ndoc2" in x or "doc2" in x else [[0, 0.5]]
+    )
+
+    # Instantiate the CumulativeSimSplitter with the mock encoder
+    cohere_encoder = CohereEncoder(
+        name="",
+        cohere_api_key="a",
+    )
+    splitter = CumulativeChunker(encoder=cohere_encoder, score_threshold=0.9)
+    splitter.encoder = mock_encoder
+
+    # Define some documents
+    docs = [
+        "doc1 about something",
+        "doc2 about something",
+        "doc3 about something",
+        "doc4 about something",
+        "doc5 about something",
+    ]
+
+    # Use the splitter to split the documents
+    splits = await splitter.acall(docs)
+
+    # Verify the splits
+    # The expected outcome needs to match the logic defined in your mock_encoder's side_effect
+    assert len(splits) == 5, f"{len(splits)}"
+
+
 def test_consecutive_similarity_splitter_single_doc():
     mock_encoder = create_autospec(BaseEncoder)
     # Assuming any return value since it should not reach the point of using the encoder
@@ -101,6 +175,67 @@ def test_cumulative_similarity_splitter_single_doc():
     assert len(chunks) == 1
 
 
+def test_statistical_chunker():
+    # Create a Mock object for the encoder
+    mock_encoder = Mock()
+    mock_encoder.side_effect = lambda docs: np.array([[1, 0] for _ in docs])
+
+    cohere_encoder = CohereEncoder(
+        name="",
+        cohere_api_key="a",
+    )
+    # Instantiate the ConsecutiveSimSplitter with the mock encoder
+    splitter = StatisticalChunker(encoder=cohere_encoder)
+    splitter.encoder = mock_encoder
+
+    # Define some documents
+    docs = ["doc1 about something", "doc2 about something", "doc3 about something"]
+
+    # Use the splitter to split the documents
+    splits = splitter(docs=docs)
+
+    # Verify the splits
+    print(splits)
+    assert len(splits) == 3, "Expected three sets of chunks"
+    assert splits[0][0].splits == [
+        "doc1 about something"
+    ], "First split does not match expected documents"
+    assert splits[2][0].splits == [
+        "doc3 about something"
+    ], "Second split does not match expected documents"
+
+
+@pytest.mark.asyncio
+async def test_async_statistical_chunker():
+    # Create a Mock object for the encoder
+    mock_encoder = AsyncMock()
+    mock_encoder.side_effect = lambda docs: np.array([[1, 0] for _ in docs])
+
+    cohere_encoder = CohereEncoder(
+        name="",
+        cohere_api_key="a",
+    )
+    # Instantiate the ConsecutiveSimSplitter with the mock encoder
+    splitter = StatisticalChunker(encoder=cohere_encoder)
+    splitter.encoder = mock_encoder
+
+    # Define some documents
+    docs = ["doc1 about something", "doc2 about something", "doc3 about something"]
+
+    # Use the splitter to split the documents
+    splits = await splitter.acall(docs=docs)
+
+    # Verify the splits
+    print(splits)
+    assert len(splits) == 3, "Expected three sets of chunks"
+    assert splits[0][0].splits == [
+        "doc1 about something"
+    ], "First split does not match expected documents"
+    assert splits[2][0].splits == [
+        "doc3 about something"
+    ], "Second split does not match expected documents"
+
+
 @pytest.fixture
 def base_splitter_instance():
     # Now MockEncoder includes default values for required fields
@@ -112,7 +247,6 @@ def base_splitter_instance():
         name="test_splitter",
         encoder=mock_encoder,
         splitter=mock_splitter,
-        score_threshold=0.5,
     )