# Hive Intelligence -- Collective Model Routing Knowledge

## META
- relevance: CORE -- key differentiator for Conductor Brain's learning system
- created: 2026-02-13

## CONCEPT

Hive Intelligence is LLMHosts's collective learning system where the Conductor Brain learns which models work best for which use cases across ALL users (anonymized). The more users on the platform, the smarter the routing becomes. This is LLMHosts's primary network effect and competitive moat.

## THE PROBLEM IT SOLVES

### Current State (Without Hive)
1. User picks a model (e.g., "use Claude for coding, GPT for writing")
2. This choice is hardcoded -- based on gut feeling, blog posts, or marketing
3. No feedback: user doesn't know if their choice is actually optimal
4. Models update constantly: what was best last month may not be best now
5. New models release weekly: impossible to evaluate them all manually
6. Result: suboptimal model selection, wasted money, lower quality output

### With Hive Intelligence
1. User sends request → Conductor analyzes payload (task type, complexity, domain)
2. Conductor checks Hive knowledge: "for this type of request, model X has 94% satisfaction across 10K similar requests"
3. Routes to optimal model (considering cost, quality, latency, user preferences)
4. Collects outcome feedback (thumbs up/down, completion quality metrics)
5. Updates Hive knowledge base
6. Next similar request is routed even better
7. Result: optimal model selection that improves continuously

## ARCHITECTURE

### Data Flow
```
User Request
  → Conductor: analyze payload (task type, complexity, domain, length)
  → Hive KB: lookup best model for this request profile
  → Route to selected model
  → Collect outcome (user feedback, quality metrics, cost, latency)
  → Update Hive KB (anonymized, aggregated)
  → Next similar request routed better
```

### Payload Analysis (Task Classification)
- **Task Type**: coding, writing, analysis, research, math, creative, translation, summarization
- **Complexity**: simple (direct answer) vs. complex (multi-step reasoning)
- **Domain**: medical, legal, technical, general, financial, academic
- **Length**: short response vs. long-form generation
- **Modality**: text-only, code, multimodal, structured output
- **Sensitivity**: PII present, confidential, general

### Hive Knowledge Base
- Anonymized and aggregated (no individual user data)
- Per model: success rate, quality score, latency distribution, cost per token
- Per task profile: best model ranking, cost-effectiveness ranking
- Time-weighted: recent data weighted more heavily (models change)
- Confidence score: how many data points back this recommendation

### Routing Decision Engine
```
Input: request_profile = {task_type, complexity, domain, length, modality, sensitivity}
Input: user_preferences = {preferred_models, cost_sensitivity, quality_sensitivity}
Input: available_compute = {local_models, external_apis, cache_status}

1. Hive lookup → ranked model list for this profile
2. Filter by availability (is the model accessible?)
3. Filter by user preferences (respect explicit choices)
4. Filter by sensitivity (PII → local only)
5. Rank by: (hive_quality_score * user_quality_weight) + (cost_score * user_cost_weight)
6. Select top candidate → route
7. If cache hit available for top candidate → use cache
```

## LEARNING MECHANISMS

### Feedback Signals
1. **Explicit**: Thumbs up/down on responses
2. **Implicit**: Response regeneration (user unsatisfied), conversation continuation (user satisfied)
3. **Quality Metrics**: Response completeness, code execution success, factual accuracy
4. **Cost Efficiency**: Cost per successful response
5. **Latency**: Time to first token, total response time

### Training Loop
- **Micro (real-time)**: Update routing weights from user feedback immediately
- **Daily**: Aggregate daily Hive data, update model rankings
- **Weekly**: Full Conductor RL training batch with accumulated Hive data
- **Monthly**: Comprehensive model evaluation, major routing updates

### Privacy
- Individual user data NEVER shared in Hive
- Only anonymized, aggregated statistics: "model X has 92% success rate for coding tasks"
- Users can opt-out of contributing to Hive (still benefit from it)
- No prompt content stored in Hive (only metadata: task type, outcome, cost)

## NETWORK EFFECTS

### Flywheel
```
More Users → More Feedback Data → Smarter Routing → Better Outcomes
  → More Value → More Users → ...
```

### Competitive Moat
- Day 1: Routing based on published benchmarks (public knowledge)
- Month 3: Routing based on 10K real-world interactions (proprietary data)
- Month 12: Routing based on 1M+ interactions (impossible to replicate)
- Year 2: Hive knows more about model capabilities than any benchmark

### Value Proposition Over Time
- New user on Day 1: Gets benefit of all previous users' collective experience
- The last user to join gets MORE value than the first
- This is the classic network effect that creates billion-dollar platforms

## RELATIONSHIP TO CONDUCTOR BRAIN

- Hive Intelligence is the DATA that feeds the Conductor Brain
- Conductor Brain is the MODEL that makes routing decisions
- RL training uses Hive data as reward signals (outcome, efficiency, preference)
- Conductor improves from Hive data even without explicit RL training (heuristic updates)
