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diff --git a/snips_inference_agl/intent_parser/intent_parser.py b/snips_inference_agl/intent_parser/intent_parser.py
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+++ b/snips_inference_agl/intent_parser/intent_parser.py
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+from abc import abstractmethod, ABCMeta
+
+from future.utils import with_metaclass
+
+from snips_inference_agl.common.abc_utils import classproperty
+from snips_inference_agl.pipeline.processing_unit import ProcessingUnit
+
+
+class IntentParser(with_metaclass(ABCMeta, ProcessingUnit)):
+    """Abstraction which performs intent parsing
+
+    A custom intent parser must inherit this class to be used in a
+    :class:`.SnipsNLUEngine`
+    """
+
+    @classproperty
+    def unit_name(cls):  # pylint:disable=no-self-argument
+        return IntentParser.registered_name(cls)
+
+    @abstractmethod
+    def fit(self, dataset, force_retrain):
+        """Fit the intent parser with a valid Snips dataset
+
+        Args:
+            dataset (dict): valid Snips NLU dataset
+            force_retrain (bool): specify whether or not sub units of the
+            intent parser that may be already trained should be retrained
+        """
+        pass
+
+    @abstractmethod
+    def parse(self, text, intents, top_n):
+        """Performs intent parsing on the provided *text*
+
+        Args:
+            text (str): input
+            intents (str or list of str): if provided, reduces the scope of
+                intent parsing to the provided list of intents
+            top_n (int, optional): when provided, this method will return a
+                list of at most top_n most likely intents, instead of a single
+                parsing result.
+                Note that the returned list can contain less than ``top_n``
+                elements, for instance when the parameter ``intents`` is not
+                None, or when ``top_n`` is greater than the total number of
+                intents.
+
+        Returns:
+            dict or list: the most likely intent(s) along with the extracted
+            slots. See :func:`.parsing_result` and :func:`.extraction_result`
+            for the output format.
+        """
+        pass
+
+    @abstractmethod
+    def get_intents(self, text):
+        """Performs intent classification on the provided *text* and returns
+        the list of intents ordered by decreasing probability
+
+        The length of the returned list is exactly the number of intents in the
+        dataset + 1 for the None intent
+
+        .. note::
+
+            The probabilities returned along with each intent are not
+            guaranteed to sum to 1.0. They should be considered as scores
+            between 0 and 1.
+        """
+        pass
+
+    @abstractmethod
+    def get_slots(self, text, intent):
+        """Extract slots from a text input, with the knowledge of the intent
+
+        Args:
+            text (str): input
+            intent (str): the intent which the input corresponds to
+
+        Returns:
+            list: the list of extracted slots
+
+        Raises:
+            IntentNotFoundError: when the intent was not part of the training
+                data
+        """
+        pass