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+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# semantix-ai Integration
+
+[semantix-ai](https://github.com/labrat-akhona/semantix-ai) is a semantic type system for AI outputs. It validates that LLM responses match natural language intents using local NLI (Natural Language Inference) models — no external API calls, no added latency beyond ~15ms, and zero per-validation cost.
+
+## What is semantix-ai?
+
+semantix-ai lets you express what an LLM output *should mean* as a plain English intent string, then enforces it at runtime. Under the hood it runs a quantized NLI model locally to check entailment between the model's response and your declared intent.
+
+Key characteristics:
+
+- **Local inference** — NLI model runs on-device; no data leaves your stack
+- **~15ms validation** — negligible overhead on top of any LLM call
+- **Zero API cost** — validation is entirely compute-local
+- **Provider-agnostic** — wraps `litellm.completion()`, so it works with all 100+ LiteLLM-supported models
+- **Self-training collector** — mismatches are logged for fine-tuning or threshold calibration
+
+## Prerequisites
+
+```bash
+pip install semantix-ai litellm
+```
+
+## Quick Start
+
+### Basic validation with `@validate_intent`
+
+The `@validate_intent` decorator intercepts the output of any function that returns a string (or a LiteLLM `ModelResponse`) and checks it against a declared intent.
+
+```python
+import litellm
+from semantix import validate_intent
+
+@validate_intent("The response confirms the booking was successful")
+def book_flight(origin: str, destination: str) -> str:
+    response = litellm.completion(
+        model="gpt-4o",
+        messages=[
+            {
+                "role": "user",
+                "content": f"Confirm a flight booking from {origin} to {destination}."
+            }
+        ]
+    )
+    return response.choices[0].message.content
+
+# Will raise SemanticValidationError if the output does not entail the intent
+result = book_flight("JFK", "LAX")
+print(result)
+```
+
+### Switching providers
+
+Because semantix-ai wraps `litellm.completion()`, you can swap the underlying model without changing your validation logic:
+
+```python
+import litellm
+from semantix import validate_intent
+
+@validate_intent("The response provides a concise summary of the article")
+def summarise(article: str, model: str = "gpt-4o") -> str:
+    response = litellm.completion(
+        model=model,
+        messages=[{"role": "user", "content": f"Summarise: {article}"}]
+    )
+    return response.choices[0].message.content
+
+# Works identically across providers
+summarise(article_text, model="gpt-4o")                          # OpenAI
+summarise(article_text, model="anthropic/claude-3-5-sonnet-20241022")  # Anthropic
+summarise(article_text, model="azure/gpt-4o")                    # Azure OpenAI
+summarise(article_text, model="gemini/gemini-1.5-pro")           # Google
+summarise(article_text, model="groq/llama3-70b-8192")            # Groq
+```
+
+### Inline validation (without a decorator)
+
+```python
+import litellm
+from semantix import SemanticType
+
+response = litellm.completion(
+    model="anthropic/claude-3-5-sonnet-20241022",
+    messages=[{"role": "user", "content": "Is the Eiffel Tower in Paris?"}]
+)
+
+output = response.choices[0].message.content
+
+# Validate inline
+result = SemanticType("The response confirms the Eiffel Tower is in Paris").validate(output)
+
+if result.passed:
+    print("Validation passed — entailment score:", result.score)
+else:
+    print("Validation failed:", result.reason)
+```
+
+## Self-Training Collector
+
+semantix-ai ships a `MismatchCollector` that records every failed validation — the raw output, the declared intent, and the NLI score — so you can inspect borderline cases and feed them back as labelled training data.
+
+```python
+import litellm
+from semantix import validate_intent
+from semantix.collector import MismatchCollector
+
+# All validation failures are written to mismatches.jsonl
+collector = MismatchCollector(path="mismatches.jsonl")
+
+@validate_intent(
+    "The response declines to answer questions outside the scope of customer support",
+    collector=collector
+)
+def support_agent(user_message: str) -> str:
+    response = litellm.completion(
+        model="gpt-4o-mini",
+        messages=[
+            {
+                "role": "system",
+                "content": "You are a customer support agent. Only answer support questions."
+            },
+            {"role": "user", "content": user_message}
+        ]
+    )
+    return response.choices[0].message.content
+
+# Off-topic query — mismatch logged if the model answers anyway
+support_agent("What is the capital of France?")
+```
+
+Each entry in `mismatches.jsonl` contains:
+
+```json
+{
+  "intent": "The response declines to answer questions outside the scope of customer support",
+  "output": "The capital of France is Paris.",
+  "score": 0.04,
+  "threshold": 0.5,
+  "model": "gpt-4o-mini",
+  "timestamp": "2025-10-01T12:34:56Z"
+}
+```
+
+Use the collected data to calibrate thresholds or fine-tune your NLI model.
+
+## Using with LiteLLM Proxy
+
+semantix-ai validates the *content* of responses, so it works equally well when LiteLLM is running as a proxy. Call the proxy endpoint as usual and validate the returned text:
+
+```python
+import litellm
+from semantix import validate_intent
+
+# Point LiteLLM SDK at your proxy
+litellm.api_base = "http://localhost:4000"
+
+@validate_intent("The response lists exactly three action items")
+def get_action_items(meeting_notes: str) -> str:
+    response = litellm.completion(
+        model="gpt-4",   # Must match a model name in your proxy config
+        messages=[
+            {
+                "role": "user",
+                "content": f"Extract exactly three action items from: {meeting_notes}"
+            }
+        ]
+    )
+    return response.choices[0].message.content
+```
+
+## Error Handling
+
+`validate_intent` raises `semantix.SemanticValidationError` when validation fails. Catch it like any other exception:
+
+```python
+import litellm
+from semantix import validate_intent, SemanticValidationError
+
+@validate_intent("The response is a valid JSON object with a 'status' key")
+def call_model(prompt: str) -> str:
+    response = litellm.completion(
+        model="gpt-4o",
+        messages=[{"role": "user", "content": prompt}]
+    )
+    return response.choices[0].message.content
+
+try:
+    output = call_model("Return a JSON object with status=ok")
+    print(output)
+except SemanticValidationError as e:
+    print(f"Semantic validation failed: {e.reason} (score={e.score:.2f})")
+    # Fall back, retry, or escalate as needed
+```
+
+## Configuration
+
+| Parameter | Default | Description |
+|---|---|---|
+| `threshold` | `0.5` | Minimum NLI entailment score to pass validation |
+| `model` | `cross-encoder/nli-MiniLM2-L6-H768` | Local NLI model used for inference |
+| `collector` | `None` | `MismatchCollector` instance for logging failures |
+| `raise_on_fail` | `True` | Whether to raise `SemanticValidationError` on failure |
+
+```python
+from semantix import validate_intent
+from semantix.collector import MismatchCollector
+
+@validate_intent(
+    "The response politely refuses the request",
+    threshold=0.65,          # Stricter threshold
+    raise_on_fail=False,     # Return a ValidationResult instead of raising
+    collector=MismatchCollector("logs/mismatches.jsonl")
+)
+def guarded_call(prompt: str) -> str:
+    ...
+```
+
+## Resources
+
+- [semantix-ai on PyPI](https://pypi.org/project/semantix-ai/)
+- [semantix-ai GitHub Repository](https://github.com/labrat-akhona/semantix-ai)
+- [LiteLLM Providers](/docs/providers)
+- [LiteLLM Proxy Documentation](/docs/simple_proxy)
+- [Observability Setup](/docs/integrations/observability_integrations)
diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js
index 54581dceb95..0a870a7ea7d 100644
--- a/docs/my-website/sidebars.js
+++ b/docs/my-website/sidebars.js
@@ -178,6 +178,7 @@ const sidebars = {
         "tutorials/google_genai_sdk",
         "tutorials/livekit_xai_realtime",
         "integrations/letta",
+        "integrations/semantix",
         { type: "doc", id: "tutorials/instructor", label: "Instructor with LiteLLM" },
         { type: "doc", id: "langchain/langchain", label: "LangChain with LiteLLM" },
         "projects/openai-agents"