diff --git a/README.md b/README.md
index beb5c2f..508c6d3 100644
--- a/README.md
+++ b/README.md
@@ -85,3 +85,32 @@ Each `indicoio` function has a corresponding batch function for analyzing many e
 >>> batch_sentiment(['Best day ever', 'Worst day ever'])
 [0.9899001220871786, 0.005709885173415242]
 ```
+
+
+Calling multiple APIs with a single function
+---------
+There are two multiple API functions `predict_text` and `predict_image`. These functions are similar to the existing api functions, but take in an additional `apis` argument as a list of strings of API names (defaults to all existing apis). `predict_text` accepts a list of existing text APIs and vice versa for `predict_image`. These functions also support batch as the other functions do.
+
+Accepted text API names: `text_tags, political, sentiment, language`
+
+Accepted image API names: `fer, facial_features, image_features`
+
+```python
+>>> from indicoio import predict_text, predict_image, batch_predict_text, batch_predict_image
+
+>>> predict_text('Best day ever', apis=["sentiment", "language"])
+{'sentiment': 0.9899001220871786, 'language': {u'Swedish': 0.0022464881013042294, u'Vietnamese': 9.887170914498351e-05, ...}}
+
+>>> batch_predict_text(['Best day ever', 'Worst day ever'], apis=["sentiment", "language"])
+{'sentiment': [0.9899001220871786, 0.005709885173415242], 'language': [{u'Swedish': 0.0022464881013042294, u'Vietnamese': 9.887170914498351e-05, u'Romanian': 0.00010661175919993216, ...}, {u'Swedish': 0.4924352805804646, u'Vietnamese': 0.028574824174911372, u'Romanian': 0.004185623723173551, u'Dutch': 0.000717033819689362, u'Korean': 0.0030093489153785826, ...}]}
+
+>>> import numpy as np
+
+>>> test_face = np.linspace(0,50,48*48).reshape(48,48).tolist()
+
+>>> predict_image(test_face, apis=["fer", "facial_features"])
+{'facial_features': [0.0, -0.026176479280200796, 0.20707644777495776, ...], 'fer': {u'Angry': 0.08877494466353497, u'Sad': 0.3933999409104264, u'Neutral': 0.1910612654566151, u'Surprise': 0.0346146405941845, u'Fear': 0.17682159820518667, u'Happy': 0.11532761017005204}}
+
+>>> batch_predict_image([test_face, test_face], apis=["fer", "facial_features"])
+{'facial_features': [[0.0, -0.026176479280200796, 0.20707644777495776, ...], [0.0, -0.026176479280200796, 0.20707644777495776, ...]], 'fer': [{u'Angry': 0.08877494466353497, u'Sad': 0.3933999409104264, u'Neutral': 0.1910612654566151, u'Surprise': 0.0346146405941845, u'Fear': 0.17682159820518667, u'Happy': 0.11532761017005204}, { u'Angry': 0.08877494466353497, u'Sad': 0.3933999409104264, u'Neutral': 0.1910612654566151, u'Surprise': 0.0346146405941845, u'Fear': 0.17682159820518667, u'Happy': 0.11532761017005204}]}
+```
diff --git a/README.rst b/README.rst
index a31c07d..7b692de 100644
--- a/README.rst
+++ b/README.rst
@@ -18,7 +18,7 @@ From source:
 
 .. code:: bash
 
-    git clone https://github.com/IndicoDataSolutions/IndicoIo-python.git 
+    git clone https://github.com/IndicoDataSolutions/IndicoIo-python.git
     python setup.py install
 
 API Keys + Setup
@@ -54,8 +54,7 @@ Examples
     >>> indicoio.config.api_key = "YOUR_API_KEY"
 
     >>> political("Guns don't kill people. People kill people.")
-    {u'Libertarian': 0.47740164630834825, u'Green': 0.08454409540443657, 
-        u'Liberal': 0.16617097211030055, u'Conservative': 0.2718832861769146}
+    {u'Libertarian': 0.47740164630834825, u'Green': 0.08454409540443657, u'Liberal': 0.16617097211030055, u'Conservative': 0.2718832861769146}
 
     >>> sentiment('Worst movie ever.')
     0.07062467665597527
@@ -71,23 +70,21 @@ Examples
     >>> text_tags(test_text, top_n=1) # return only keys with top_n values
     {u'startups_and_entrepreneurship': 0.21888586688354486}
 
-    >>> import numpy as np 
+    >>> import numpy as np
 
     >>> test_face = np.linspace(0,50,48*48).reshape(48,48).tolist()
 
     >>> fer(test_face)
-    {u'Angry': 0.08843749137458341, u'Sad': 0.39091163159204684, u'Neutral': 0.1947947999669361,
-         u'Surprise': 0.03443785859010413, u'Fear': 0.17574534848440568, u'Happy': 0.11567286999192382}
+    {u'Angry': 0.08843749137458341, u'Sad': 0.39091163159204684, u'Neutral': 0.1947947999669361, u'Surprise': 0.03443785859010413, u'Fear': 0.17574534848440568, u'Happy': 0.11567286999192382}
 
     >>> facial_features(test_face)
     [0.0, -0.02568680526917187, 0.21645604230056517, ..., 3.0342637531932777]
 
     >>> language('Quis custodiet ipsos custodes')
-    {u'Swedish': 0.00033330636691921914, u'Lithuanian': 0.007328693814717631, 
-        u'Vietnamese': 0.0002686116137658802, u'Romanian': 8.133913804076592e-06, ...}
+    {u'Swedish': 0.00033330636691921914, u'Lithuanian': 0.007328693814717631, u'Vietnamese': 0.0002686116137658802, u'Romanian': 8.133913804076592e-06, ...}
 
-Batch API Access
-----------------
+Batch API
+---------
 
 Each ``indicoio`` function has a corresponding batch function for
 analyzing many examples with a single request. Simply pass in a list of
@@ -100,3 +97,37 @@ inputs and receive a list of results in return.
     >>> batch_sentiment(['Best day ever', 'Worst day ever'])
     [0.9899001220871786, 0.005709885173415242]
 
+Calling multiple APIs with a single function
+--------------------------------------------
+
+There are two multiple API functions ``predict_text`` and
+``predict_image``. These functions are similar to the existing api
+functions, but take in an additional ``apis`` argument as a list of
+strings of API names (defaults to all existing apis). ``predict_text``
+accepts a list of existing text APIs and vice versa for
+``predict_image``. These functions also support batch as the other
+functions do.
+
+Accepted text API names: ``text_tags, political, sentiment, language``
+
+Accepted image API names: ``fer, facial_features, image_features``
+
+.. code:: python
+
+    >>> from indicoio import predict_text, predict_image, batch_predict_text, batch_predict_image
+
+    >>> predict_text('Best day ever', apis=["sentiment", "language"])
+    {'sentiment': 0.9899001220871786, 'language': {u'Swedish': 0.0022464881013042294, u'Vietnamese': 9.887170914498351e-05, ...}}
+
+    >>> batch_predict_text(['Best day ever', 'Worst day ever'], apis=["sentiment", "language"])
+    {'sentiment': [0.9899001220871786, 0.005709885173415242], 'language': [{u'Swedish': 0.0022464881013042294, u'Vietnamese': 9.887170914498351e-05, u'Romanian': 0.00010661175919993216, ...}, {u'Swedish': 0.4924352805804646, u'Vietnamese': 0.028574824174911372, u'Romanian': 0.004185623723173551, u'Dutch': 0.000717033819689362, u'Korean': 0.0030093489153785826, ...}]}
+
+    >>> import numpy as np
+
+    >>> test_face = np.linspace(0,50,48*48).reshape(48,48).tolist()
+
+    >>> predict_image(test_face, apis=["fer", "facial_features"])
+    {'facial_features': [0.0, -0.026176479280200796, 0.20707644777495776, ...], 'fer': {u'Angry': 0.08877494466353497, u'Sad': 0.3933999409104264, u'Neutral': 0.1910612654566151, u'Surprise': 0.0346146405941845, u'Fear': 0.17682159820518667, u'Happy': 0.11532761017005204}}
+
+    >>> batch_predict_image([test_face, test_face], apis=["fer", "facial_features"])
+    {'facial_features': [[0.0, -0.026176479280200796, 0.20707644777495776, ...], [0.0, -0.026176479280200796, 0.20707644777495776, ...]], 'fer': [{u'Angry': 0.08877494466353497, u'Sad': 0.3933999409104264, u'Neutral': 0.1910612654566151, u'Surprise': 0.0346146405941845, u'Fear': 0.17682159820518667, u'Happy': 0.11532761017005204}, { u'Angry': 0.08877494466353497, u'Sad': 0.3933999409104264, u'Neutral': 0.1910612654566151, u'Surprise': 0.0346146405941845, u'Fear': 0.17682159820518667, u'Happy': 0.11532761017005204}]}