diff --git a/healthchain/ai_integrations/baseintegration.py b/healthchain/ai_integrations/baseintegration.py
new file mode 100644
index 0000000..1bcf1ce
--- /dev/null
+++ b/healthchain/ai_integrations/baseintegration.py
@@ -0,0 +1,24 @@
+from typing import Optional
+
+
+class AIIntegrationBase:
+    def __init__(self, model_name: str, api_key: Optional[str]):
+        self.model_name = model_name
+        self.model = self.load_model(model_name)
+
+    def load_model(self, model_name: str, api_key: Optional[str]):
+        raise NotImplementedError("Subclasses should implement this method.")
+
+    def preprocess(self, data):
+        raise NotImplementedError("Subclasses should implement this method.")
+
+    def predict(self, data):
+        raise NotImplementedError("Subclasses should implement this method.")
+
+    def postprocess(self, prediction):
+        raise NotImplementedError("Subclasses should implement this method.")
+
+    def run(self, data):
+        preprocessed_data = self.preprocess(data)
+        prediction = self.predict(preprocessed_data)
+        return self.postprocess(prediction)
diff --git a/healthchain/ai_integrations/openai.py b/healthchain/ai_integrations/openai.py
new file mode 100644
index 0000000..0339a0d
--- /dev/null
+++ b/healthchain/ai_integrations/openai.py
@@ -0,0 +1,35 @@
+from healthchain.ai_integrations.baseintegration import AIIntegrationBase
+import openai
+
+
+class OpenAIIntegration(AIIntegrationBase):
+    def __init__(self, model_name: str, api_key: str, **params):
+        self.api_key = api_key
+        self.params = params
+        super().__init__(model_name)
+
+    def load_model(self, model_name: str):
+        openai.api_key = self.api_key
+        return model_name
+
+    def preprocess(self, data):
+        return data
+
+    def predict(self, data):
+        responses = []
+        if isinstance(data, list):
+            for item in data:
+                response = openai.Completion.create(
+                    model=self.model_name, prompt=item, **self.params
+                )
+                responses.append(response.choices[0].text.strip())
+        else:
+            response = openai.Completion.create(
+                model=self.model_name, prompt=data, **self.params
+            )
+            responses.append(response.choices[0].text.strip())
+        return responses
+
+    def postprocess(self, prediction):
+        # For simplicity, return the raw prediction
+        return prediction