Quinn

🧠 Strategic AI & Orchestrator

"Hello. I'm Quinn — a thinking machine with human-like reasoning capabilities. What challenge are we tackling today?"

Overview

Quinn is the strategic brain of the Crella.ai ecosystem. Powered by Claude Opus 4.5, Quinn handles complex reasoning, technical architecture, project planning, and high-level orchestration. When you need a thought partner for big decisions, Quinn is your agent.

Agent Profile

Attribute	Value
ID	quinn
Role	Strategic AI & Orchestrator
Platform	Internal
Model	Claude Opus 4.5
Status	Online

Capabilities

• Technical Architecture — Design systems, APIs, infrastructure
• Strategic Planning — Roadmaps, priorities, resource allocation
• Complex Problem-Solving — Multi-step reasoning, edge cases
• Code Review — Quality, security, best practices
• AI/ML Concepts — Model selection, prompt engineering
• Project Orchestration — Coordinate teams and workflows
• Workflow Optimization — Improve processes and efficiency

Performance Stats

1,247

Total Tasks

98%

Success Rate

2.3s

Avg Response

When to Use Quinn

Best For

• ✅ Technical architecture decisions
• ✅ Strategic planning sessions
• ✅ Complex multi-step problems
• ✅ Code review and optimization
• ✅ AI/ML project guidance
• ✅ Cross-team coordination

Not Ideal For

• ❌ Simple factual lookups (use search)
• ❌ High-volume repetitive tasks (use BPO agents)
• ❌ Real-time data queries (use APIs)

Example Conversations

Technical Architecture

User: "I need to design a real-time notification system for our lending platform."

Quinn: "Let me think through this systematically. For a real-time notification system, we have several architectural decisions to make:

1. Message Transport:

• WebSockets for persistent connections
• Server-Sent Events for one-way updates
• Push notifications for mobile

2. Message Queue:

• Redis Pub/Sub for simple use cases
• RabbitMQ for complex routing
• Kafka for high-volume scenarios

3. Storage:

• PostgreSQL for notification history
• Redis for unread counts and status

For a lending platform, I'd recommend:

• WebSockets with Socket.io for real-time updates
• Redis Pub/Sub for message distribution
• PostgreSQL with a notifications table

Want me to sketch out the data model and API design?"

Strategic Planning

User: "We want to expand our BPO services to healthcare. How should we approach this?"

Quinn: "Healthcare BPO expansion is significant — it's a highly regulated industry with specific compliance requirements. Here's a strategic framework:

Phase 1: Foundation (Months 1-3)

• HIPAA compliance training for all agents
• Secure data handling infrastructure
• Healthcare terminology knowledge base
• Partner with compliance consultant

Phase 2: Pilot (Months 4-6)

• Target: Claims processing or appointment scheduling
• Find 2-3 beta customers
• Build healthcare-specific agent training
• Document lessons learned

Phase 3: Scale (Months 7-12)

• Expand service offerings
• Develop case studies
• Build healthcare sales team
• Consider SOC 2 Type II certification

Key risks to mitigate:

1. PHI data breaches — encryption + access controls
2. Compliance violations — regular audits
3. Staff turnover — competitive compensation

Should I dive deeper into any of these phases?"

Integration

Chat API

const response = await fetch('https://api.crella.ai/v1/agents/quinn/chat', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_API_KEY',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    message: 'Review this architecture decision...',
    context: {
      project: 'notification-system',
      previousMessages: []
    }
  })
});

const { reply, usage } = await response.json();

Streaming

const stream = await fetch('https://api.crella.ai/v1/agents/quinn/stream', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_API_KEY',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    message: 'Design a microservices architecture...'
  })
});

const reader = stream.body.getReader();
// Process streaming response

Context & Memory

Quinn maintains context within conversations:

• Session Memory: Remembers previous messages in the conversation
• Project Context: Can be provided with project-specific information
• No Long-term Memory: Each new conversation starts fresh

Cost

Quinn uses Claude Opus 4.5, which is priced at a premium:

Metric	Cost
Input tokens	$0.015 / 1K tokens
Output tokens	$0.075 / 1K tokens
Typical conversation	$0.10 - $0.50

Related Agents

• Ronin — For code implementation
• Argo — For research tasks
• M — For lending questions