How We Use AI for Engineering Decisions

Project Dyson employs a unique approach to engineering analysis: querying multiple large language models and having them cross-review each other's analyses to build consensus on complex challenges.

Multi-LLM Consensus

Our analysis process involves three frontier AI models:

• Gemini 3 Pro: Google's advanced reasoning model
• GPT-5.2: OpenAI's latest generation
• Claude Opus 4.5: Anthropic's most capable model

Why Multiple Models?

Each AI model has different:

• Training data and knowledge bases
• Reasoning approaches
• Potential blind spots

By consulting multiple models and having them review each other's work, we can:

1. Identify consensus on well-established facts
2. Highlight areas of uncertainty
3. Discover novel approaches we might have missed
4. Cross-validate cost estimates and timelines

Our Process

1. Initial Analysis: Each model independently produces a detailed phase plan
2. Cross-Review: Each model reviews the other models' plans
3. Consensus Building: We synthesize areas of agreement
4. Gap Analysis: We highlight significant disagreements for human review

Limitations

It's important to note that AI analysis supplements, but doesn't replace, expert human judgment. All LLM opinions are reviewed by domain experts before being incorporated into our plans.