Enterprise AI Architecture: Model Routing, Autonomy & Hardware Control

1. Multi-Model Routing (Same Persona, Different Experts)

Enterprise AI should use specialized models behind a unified interface:

Architecture

User Request → Router → Appropriate Model → Unified Response

Model Specializations

• Qwen Coder: Code generation, debugging, architecture
• Qwen Base/Llama: Reports, emails, documentation, reasoning
• Vision Models: Image analysis, document processing
• Custom LoRA: Domain-specific expertise

Implementation

• Ollama supports multiple models simultaneously
• Router can be rule-based or small classifier model
• User experiences single "AI persona"
• Backend selects optimal model per task

2. Autonomous Problem Solving (Agentic Behavior)

The Thumbnail Example

Claude saw images were too large → Created thumbnail script → Ran it → Used results

Agentic Loop

1. OBSERVE - Recognize problem/limitation
2. THINK - Devise solution
3. PLAN - Break into steps
4. ACT - Execute (write code, run tools)
5. OBSERVE - Check results, loop if needed

Training for Autonomy

Provide: - Tools (code execution, file access) - Training examples of problem-solving - System prompts allowing autonomous action - Boundaries and safety rails

Can Qwen Coder Do This?

• Base Qwen: Somewhat, needs prompting
• Qwen + Training: Much better
• Qwen + Tools + Training: Yes, reliably

3. Robotics & Hardware Control

Yes, Trainable for Physical Systems

Training data includes: - GPIO documentation - Motor controller libraries - Sensor patterns - Safety protocols - YOUR hardware configs - Example control sequences - Error handling procedures - Communication protocols (I2C, SPI, UART)

Architecture for Robotics

Natural Language → LLM → Generated Code → Safety Check → Execute on Hardware

Example Training Pair

INPUT: "Start filling when bottle detected" OUTPUT: Complete Python with sensor reading, valve control, timing, safety checks

What's Trainable

• YOUR equipment configurations
• YOUR pin assignments
• YOUR safety protocols
• YOUR process sequences
• YOUR error handling

4. Enterprise Value Proposition

What Corlera Offers

• 100% On-Premise (nothing leaves building)
• Trained on client's data and processes
• Multiple specialized models, one interface
• Autonomous problem-solving
• Hardware integration ready
• Continuous learning capability

Why $30-40K Is Worth It

1. Privacy - Proprietary docs stay local
2. Customization - AI knows THEIR business
3. Capability - Beyond generic ChatGPT
4. Control - No API costs, no rate limits
5. Integration - Works with existing systems

5. Training Boundaries

CAN Train For

• Domain expertise (YOUR patterns)
• Process knowledge (YOUR workflows)
• Tool usage (YOUR integrations)
• Voice/style (YOUR communication)
• Safety protocols (YOUR requirements)

CANNOT Train For

• Raw intelligence beyond base model
• Capabilities with zero foundation
• 100% reliability (always need oversight)

Rule of Thumb

Training makes a model an EXPERT at your domain. It doesn't make a small model smarter than a large one.

Last updated: 2025-12-08 Session: s_801t