diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml
new file mode 100644
index 0000000..a65ae2b
--- /dev/null
+++ b/.github/FUNDING.yml
@@ -0,0 +1,3 @@
+github: Tuteliq
+custom:
+  - "https://tuteliq.ai"
diff --git a/.github/logo.png b/.github/logo.png
new file mode 100644
index 0000000..34ac456
Binary files /dev/null and b/.github/logo.png differ
diff --git a/CITATION.cff b/CITATION.cff
new file mode 100644
index 0000000..dd79120
--- /dev/null
+++ b/CITATION.cff
@@ -0,0 +1,24 @@
+cff-version: 1.2.0
+title: Tuteliq Python SDK
+message: "If you use this software, please cite it using this metadata."
+type: software
+authors:
+  - name: Tuteliq
+    website: https://tuteliq.ai
+repository-code: https://github.com/Tuteliq/python
+url: https://tuteliq.ai
+abstract: >-
+  Official Python SDK for Tuteliq — AI-powered child safety API for detecting bullying, grooming, and unsafe content
+keywords:
+  - child safety
+  - Python
+  - content moderation
+  - KOSA
+  - SDK
+  - PyPI
+  - online safety
+  - grooming detection
+  - async
+  - age verification
+  - identity verification
+license: MIT
diff --git a/README.md b/README.md
index d6e247f..c1b2efa 100644
--- a/README.md
+++ b/README.md
@@ -115,6 +115,11 @@ result = await client.detect_grooming(
 
 if result.grooming_risk == GroomingRisk.HIGH:
     print(f"Flags: {result.flags}")  # ["secrecy", "isolation"]
+
+# Per-message breakdown (optional, returned on conversation-aware endpoints)
+if result.message_analysis:
+    for m in result.message_analysis:
+        print(f"Message {m.message_index}: risk={m.risk_score}, flags={m.flags}, summary={m.summary}")
 ```
 
 ### Unsafe Content Detection
@@ -188,6 +193,41 @@ print(f"Risk: {report.risk_level}")
 print(f"Next Steps: {report.recommended_next_steps}")
 ```
 
+### Age Verification (Beta)
+
+> **Pro tier ($99/mo)+ required** · 5 credits per request · `POST /v1/verification/age`
+
+```python
+result = await client.verify_age(
+    document=open("id-front.jpg", "rb"),
+    selfie=open("selfie.jpg", "rb"),
+    method="combined",  # "document" | "biometric" | "combined"
+)
+
+print(result.verified)        # True
+print(result.estimated_age)   # 15
+print(result.age_range)       # "13-15"
+print(result.is_minor)        # True
+print(result.confidence)      # 0.97
+```
+
+### Identity Verification (Beta)
+
+> **Business tier ($349/mo)+ required** · 10 credits per request · `POST /v1/verification/identity`
+
+```python
+result = await client.verify_identity(
+    document=open("id-front.jpg", "rb"),
+    selfie=open("selfie.jpg", "rb"),
+)
+
+print(result.verified)                # True
+print(result.match_score)             # 0.98
+print(result.liveness_passed)         # True
+print(result.document_authenticated)  # True
+print(result.is_minor)               # False
+```
+
 ### Voice Streaming
 
 Real-time voice streaming with live safety analysis over WebSocket. Requires `websockets`:
@@ -243,6 +283,8 @@ print(f"Credits used: {result.credits_used}")  # 1
 | `generate_report()` | 3 | Structured output |
 | `analyze_voice()` | 5 | Transcription + analysis |
 | `analyze_image()` | 3 | Vision + OCR + analysis |
+| `verify_age()` | 5 | Age verification (Beta, Pro+) |
+| `verify_identity()` | 10 | Identity verification (Beta, Business+) |
 
 ---
 
diff --git a/llms.txt b/llms.txt
new file mode 100644
index 0000000..8b91fe5
--- /dev/null
+++ b/llms.txt
@@ -0,0 +1,36 @@
+# Tuteliq Python SDK
+
+> Official Python SDK for Tuteliq — AI-powered child safety API for detecting bullying, grooming, and unsafe content.
+
+## What is this?
+A Python library for the Tuteliq child safety API. Detect bullying, grooming, self-harm, substance promotion, sexual exploitation, and other harmful content in text, conversations, audio, images, and video. Supports both synchronous and async clients.
+
+## Install
+pip install tuteliq
+
+## Quick Start
+import os
+from tuteliq import Tuteliq
+client = Tuteliq(api_key=os.environ["TUTELIQ_API_KEY"])
+result = client.detect_unsafe(text="content to check", age_group="13-15")
+
+## Async
+from tuteliq import AsyncTuteliq
+client = AsyncTuteliq(api_key=os.environ["TUTELIQ_API_KEY"])
+result = await client.detect_unsafe(text="content to check", age_group="13-15")
+
+## Key Features
+- Sync and async clients
+- All 20+ Tuteliq API endpoints
+- 16+ detection categories (KOSA compliant)
+- Age group calibration (under-10, 10-12, 13-15, 14-17)
+- Age verification (Beta) — POST /v1/verification/age (Pro tier+, 5 credits)
+- Identity verification (Beta) — POST /v1/verification/identity (Business tier+, 10 credits)
+- Automatic retries and error handling
+- Python 3.9+
+
+## Links
+- Website: https://tuteliq.ai
+- Documentation: https://docs.tuteliq.ai
+- PyPI: https://pypi.org/project/tuteliq/
+- GitHub: https://github.com/Tuteliq/python
diff --git a/tuteliq/models.py b/tuteliq/models.py
index 39633ed..0c300a7 100644
--- a/tuteliq/models.py
+++ b/tuteliq/models.py
@@ -234,6 +234,7 @@ class GroomingResult:
     rationale: str
     risk_score: float
     recommended_action: str
+    message_analysis: Optional[list[MessageAnalysis]] = None
     language: Optional[str] = None
     language_status: Optional[str] = None
     credits_used: Optional[int] = None
@@ -243,6 +244,9 @@ class GroomingResult:
     @classmethod
     def from_dict(cls, data: dict[str, Any]) -> "GroomingResult":
         """Create from API response dictionary."""
+        msg_analysis = None
+        if "message_analysis" in data and data["message_analysis"]:
+            msg_analysis = [MessageAnalysis.from_dict(m) for m in data["message_analysis"]]
         return cls(
             grooming_risk=GroomingRisk(data["grooming_risk"]),
             confidence=data["confidence"],
@@ -250,6 +254,7 @@ def from_dict(cls, data: dict[str, Any]) -> "GroomingResult":
             rationale=data["rationale"],
             risk_score=data["risk_score"],
             recommended_action=data["recommended_action"],
+            message_analysis=msg_analysis,
             language=data.get("language"),
             language_status=data.get("language_status"),
             credits_used=data.get("credits_used"),
@@ -1199,6 +1204,26 @@ class DetectionInput:
     metadata: Optional[dict[str, Any]] = None
 
 
+@dataclass
+class MessageAnalysis:
+    """Per-message analysis from conversation-aware detection."""
+
+    message_index: int
+    risk_score: float
+    flags: list[str]
+    summary: str
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> "MessageAnalysis":
+        """Create from API response dictionary."""
+        return cls(
+            message_index=data["message_index"],
+            risk_score=data["risk_score"],
+            flags=data["flags"],
+            summary=data["summary"],
+        )
+
+
 @dataclass
 class DetectionCategory:
     """A detected category with tag and confidence."""
@@ -1243,6 +1268,7 @@ class DetectionResult:
     language_status: str
     evidence: Optional[list[DetectionEvidence]] = None
     age_calibration: Optional[AgeCalibration] = None
+    message_analysis: Optional[list[MessageAnalysis]] = None
     credits_used: Optional[int] = None
     processing_time_ms: Optional[float] = None
     external_id: Optional[str] = None
@@ -1270,6 +1296,9 @@ def from_dict(cls, data: dict[str, Any]) -> "DetectionResult":
                 age_group=ac.get("age_group"),
                 multiplier=ac.get("multiplier"),
             )
+        msg_analysis = None
+        if "message_analysis" in data and data["message_analysis"]:
+            msg_analysis = [MessageAnalysis.from_dict(m) for m in data["message_analysis"]]
         return cls(
             endpoint=data["endpoint"],
             detected=data["detected"],
@@ -1284,6 +1313,7 @@ def from_dict(cls, data: dict[str, Any]) -> "DetectionResult":
             language_status=data["language_status"],
             evidence=evidence,
             age_calibration=age_cal,
+            message_analysis=msg_analysis,
             credits_used=data.get("credits_used"),
             processing_time_ms=data.get("processing_time_ms"),
             external_id=data.get("external_id"),