diff --git a/intelligence/deepdive/pipeline.py b/intelligence/deepdive/pipeline.py
new file mode 100644
index 0000000..ff15894
--- /dev/null
+++ b/intelligence/deepdive/pipeline.py
@@ -0,0 +1,286 @@
+#!/usr/bin/env python3
+"""Deep Dive Intelligence Pipeline - Reference Implementation Scaffold
+
+This is ARCHITECTURE PROOF code — not production-ready but demonstrates
+component contracts and data flow. Use as integration target.
+"""
+
+import asyncio
+import logging
+from dataclasses import dataclass
+from datetime import datetime, timedelta
+from pathlib import Path
+from typing import List, Optional, Dict, Any
+
+# Phase 1: Aggregation
+@dataclass
+class FeedItem:
+    title: str
+    summary: str
+    url: str
+    source: str
+    published: datetime
+    raw: Dict[str, Any]
+
+class RSSAggregator:
+    """Fetch and normalize RSS feeds."""
+    
+    def __init__(self, cache_dir: Path = None):
+        self.cache_dir = cache_dir or Path.home() / ".cache" / "deepdive"
+        self.cache_dir.mkdir(parents=True, exist_ok=True)
+        self.logger = logging.getLogger(__name__)
+    
+    async def fetch_feed(self, url: str, etag: str = None) -> List[FeedItem]:
+        """Fetch single feed with caching. Returns normalized items."""
+        # TODO: Implement with httpx, feedparser
+        # TODO: Respect ETag/Last-Modified for incremental fetch
+        raise NotImplementedError("Phase 1: Implement RSS fetch")
+    
+    async def fetch_all(
+        self,
+        sources: List[Dict[str, str]],
+        since: datetime
+    ) -> List[FeedItem]:
+        """Fetch all configured sources since cutoff time."""
+        all_items = []
+        # TODO: asyncio.gather() parallel fetches
+        # TODO: Filter items.published >= since
+        raise NotImplementedError("Phase 1: Implement parallel fetch")
+
+# Phase 2: Relevance
+@dataclass
+class ScoredItem:
+    item: FeedItem
+    score: float
+    keywords_matched: List[str]
+
+class RelevanceScorer:
+    """Score items by relevance to Hermes/Timmy work."""
+    
+    KEYWORDS = [
+        "LLM agent", "agent system", "tool use", "function calling",
+        "reinforcement learning", "RLHF", "GRPO", "PPO",
+        "transformer", "attention", "inference optimization",
+        "local LLM", "llama.cpp", "ollama", "vLLM",
+        "Hermes", "LM Studio", "open source AI"
+    ]
+    
+    def __init__(self, model_name: str = "all-MiniLM-L6-v2"):
+        self.model_name = model_name
+        self.model = None  # Lazy load sentence-transformers
+        self.keyword_embeddings = None
+    
+    def _load_model(self):
+        from sentence_transformers import SentenceTransformer
+        import numpy as np
+        
+        self.model = SentenceTransformer(self.model_name)
+        self.keyword_embeddings = self.model.encode(self.KEYWORDS)
+    
+    def score(self, item: FeedItem) -> ScoredItem:
+        """Calculate relevance score for item."""
+        if self.model is None:
+            self._load_model()
+        
+        # TODO: Encode title + summary
+        # TODO: Cosine similarity to keyword embeddings
+        # TODO: Calculate centroid match score
+        # TODO: Boost for high-signal terms in title
+        
+        raise NotImplementedError("Phase 2: Implement scoring")
+    
+    def rank(self, items: List[FeedItem], top_n: int = 10) -> List[ScoredItem]:
+        """Score all items, return top N."""
+        # TODO: Parallel scoring, cutoff at min_score
+        raise NotImplementedError("Phase 2: Implement ranking")
+
+# Phase 3: Synthesis
+@dataclass
+class Briefing:
+    date: datetime
+    headline_items: List[ScoredItem]
+    deep_dive: ScoredItem
+    summary_text: str
+    action_items: List[str]
+
+class SynthesisEngine:
+    """Generate briefing text via local LLM."""
+    
+    def __init__(self, model_endpoint: str = "http://localhost:4000/v1"):
+        self.endpoint = model_endpoint
+        self.system_prompt = """You are an intelligence analyst for the Timmy Foundation.
+Produce concise daily briefings on AI/ML developments relevant to:
+- LLM agent systems and architecture
+- Reinforcement learning for LLMs
+- Local inference and optimization
+- Open-source AI tooling
+
+Tone: Professional, tight, actionable."""
+    
+    async def synthesize(self, items: List[ScoredItem]) -> Briefing:
+        """Generate structured briefing from top items."""
+        # TODO: Format prompt with item data
+        # TODO: Call local LLM via OpenAI-compatible API
+        # TODO: Parse response into structured briefing
+        raise NotImplementedError("Phase 3: Implement synthesis")
+
+# Phase 4: TTS
+@dataclass
+class AudioResult:
+    path: Path
+    duration_seconds: float
+    word_count: int
+
+class TTSGenerator:
+    """Generate audio via local Piper TTS."""
+    
+    def __init__(
+        self,
+        model_path: Path,
+        voice: str = "en_US-amy-medium"
+    ):
+        self.model_path = model_path
+        self.voice = voice
+    
+    async def generate(self, text: str, output_dir: Path) -> AudioResult:
+        """Generate audio file from briefing text."""
+        # TODO: Split long text into chunks if needed
+        # TODO: Call Piper subprocess
+        # TODO: Optionally combine chunks
+        # TODO: Return path and metadata
+        raise NotImplementedError("Phase 4: Implement TTS")
+
+# Phase 5: Delivery
+class TelegramDelivery:
+    """Deliver briefing via Hermes Telegram gateway."""
+    
+    def __init__(self, bot_token: str, chat_id: str):
+        self.bot_token = bot_token
+        self.chat_id = chat_id
+    
+    async def deliver_voice(self, audio_path: Path) -> bool:
+        """Send voice message to Telegram."""
+        # TODO: Use python-telegram-bot or Hermes gateway
+        raise NotImplementedError("Phase 5: Implement voice delivery")
+    
+    async def deliver_text(self, text: str) -> bool:
+        """Send text summary to Telegram."""
+        # TODO: Truncate if > 4096 chars
+        raise NotImplementedError("Phase 5: Implement text delivery")
+
+# Orchestration
+@dataclass
+class PipelineResult:
+    success: bool
+    items_considered: int
+    items_selected: int
+    briefing: Optional[Briefing]
+    audio: Optional[AudioResult]
+    errors: List[str]
+
+class DeepDivePipeline:
+    """Full pipeline orchestrator."""
+    
+    def __init__(self, config: Dict[str, Any]):
+        self.config = config
+        self.aggregator = RSSAggregator()
+        self.scorer = RelevanceScorer()
+        self.synthesis = SynthesisEngine()
+        self.tts = TTSGenerator(
+            model_path=Path(config["tts"]["model_path"])
+        )
+        self.delivery = TelegramDelivery(
+            bot_token=config["delivery"]["bot_token"],
+            chat_id=config["delivery"]["chat_id"]
+        )
+    
+    async def run(
+        self,
+        since: Optional[datetime] = None,
+        deliver: bool = True
+    ) -> PipelineResult:
+        """Execute full pipeline."""
+        errors = []
+        
+        # Phase 1: Aggregate
+        try:
+            items = await self.aggregator.fetch_all(
+                self.config["sources"],
+                since or datetime.now() - timedelta(days=1)
+            )
+        except Exception as e:
+            return PipelineResult(False, 0, 0, None, None, [f"Aggregation failed: {e}"])
+        
+        # Phase 2: Score and rank
+        try:
+            top_items = self.scorer.rank(
+                items,
+                top_n=self.config.get("top_n", 10)
+            )
+        except Exception as e:
+            return PipelineResult(False, len(items), 0, None, None, [f"Scoring failed: {e}"])
+        
+        # Phase 3: Synthesize
+        try:
+            briefing = await self.synthesis.synthesize(top_items)
+        except Exception as e:
+            return PipelineResult(False, len(items), len(top_items), None, None, [f"Synthesis failed: {e}"])
+        
+        # Phase 4: Generate audio
+        try:
+            audio = await self.tts.generate(
+                briefing.summary_text,
+                Path(self.config.get("output_dir", "/tmp/deepdive"))
+            )
+        except Exception as e:
+            return PipelineResult(False, len(items), len(top_items), briefing, None, [f"TTS failed: {e}"])
+        
+        # Phase 5: Deliver
+        if deliver:
+            try:
+                await self.delivery.deliver_voice(audio.path)
+                await self.delivery.deliver_text(briefing.summary_text[:4000])
+            except Exception as e:
+                errors.append(f"Delivery failed: {e}")
+        
+        return PipelineResult(
+            success=len(errors) == 0,
+            items_considered=len(items),
+            items_selected=len(top_items),
+            briefing=briefing,
+            audio=audio,
+            errors=errors
+        )
+
+# CLI entry point
+async def main():
+    import argparse
+    import yaml
+    
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", default="config.yaml")
+    parser.add_argument("--since", type=lambda s: datetime.fromisoformat(s))
+    parser.add_argument("--no-deliver", action="store_true")
+    parser.add_argument("--dry-run", action="store_true")
+    args = parser.parse_args()
+    
+    with open(args.config) as f:
+        config = yaml.safe_load(f)
+    
+    pipeline = DeepDivePipeline(config)
+    result = await pipeline.run(
+        since=args.since,
+        deliver=not args.no_deliver
+    )
+    
+    print(f"Success: {result.success}")
+    print(f"Items: {result.items_selected}/{result.items_considered}")
+    if result.briefing:
+        print(f"Briefing length: {len(result.briefing.summary_text)} chars")
+    if result.audio:
+        print(f"Audio: {result.audio.duration_seconds}s at {result.audio.path}")
+    if result.errors:
+        print(f"Errors: {result.errors}")
+
+if __name__ == "__main__":
+    asyncio.run(main())