--- a
+++ b/scripts/serve.py
@@ -0,0 +1,86 @@
+from typing import List
+
+import spacy
+from fastapi import FastAPI
+from pydantic import BaseModel
+
+import edsnlp
+
+app = FastAPI(title="EDS-NLP", version=edsnlp.__version__)
+
+nlp = spacy.blank("eds")
+
+nlp.add_pipe("eds.sentences")
+
+config = dict(
+    regex=dict(
+        covid=[
+            "covid",
+            r"covid[-\s]?19",
+            r"sars[-\s]?cov[-\s]?2",
+            r"corona[-\s]?virus",
+        ],
+    ),
+    attr="LOWER",
+)
+nlp.add_pipe("eds.matcher", config=config)
+
+nlp.add_pipe("eds.negation")
+nlp.add_pipe("eds.family")
+nlp.add_pipe("eds.hypothesis")
+nlp.add_pipe("eds.reported_speech")
+
+
+class Entity(BaseModel):  # (2)
+
+    # OMOP-style attributes
+    start: int
+    end: int
+    label: str
+    lexical_variant: str
+    normalized_variant: str
+
+    # Qualifiers
+    negated: bool
+    hypothesis: bool
+    family: bool
+    reported_speech: bool
+
+
+class Document(BaseModel):  # (1)
+    text: str
+    ents: List[Entity]
+
+
+@app.post("/process", response_model=List[Document])  # (1)
+async def process(
+    notes: List[str],  # (2)
+):
+
+    documents = []
+
+    for doc in nlp.pipe(notes):
+        entities = []
+
+        for ent in doc.ents:
+            entity = Entity(
+                start=ent.start_char,
+                end=ent.end_char,
+                label=ent.label_,
+                lexical_variant=ent.text,
+                normalized_variant=ent._.normalized_variant,
+                negated=ent._.negation,
+                hypothesis=ent._.hypothesis,
+                family=ent._.family,
+                reported_speech=ent._.reported_speech,
+            )
+            entities.append(entity)
+
+        documents.append(
+            Document(
+                text=doc.text,
+                ents=entities,
+            )
+        )
+
+    return documents