How AI Boosted the Architectural Design Process for Mission Control

This article continues our series on harnessing LLMs for real-world product development through our Mission Control project. As we've established throughout this series, building production-grade software demands more than flashy demos—it requires deliberate architectural decisions that stand up to real-world demands.

Two of our four North Star strategic goals directly shaped every architectural choice we made. 

• First, we needed an architecture that delivers Reliability, Performance, and Scalability from day one. 
  
• Second, our tech stack had to enable Flexibility and Extensibility as our product evolves. 

These weren't abstract principles—they were concrete requirements that guided every decision from backend infrastructure to frontend frameworks.

The stakes were clear: if Mission Control couldn't demonstrate these capabilities, it would fail as a genuine test of real-world product development. Our architectural planning process needed to prove that LLMs could accelerate not just code generation, but the strategic thinking that creates lasting technical foundations.

The Decision Framework: From Breadth to Depth

Our methodology followed the same decision framework we've refined throughout this series: systematic "shootouts" between multiple LLM models using identical prompts to research architectural options. This wasn't random experimentation—it was structured evaluation designed to surface the best solutions quickly.

The process began with broad research prompts sent simultaneously to Claude, ChatGPT, CoPilot, and Perplexity. Each LLM received the same context: our product requirements, performance expectations, and scalability needs. The goal was maximum coverage of potential solutions in minimum time.

From there, we moved through an iterative breadth-to-depth process. We narrowed prompts to focus on the most promising options while eliminating LLMs that consistently delivered weaker responses. This natural selection approach quickly identified which models excelled at architectural analysis versus surface-level suggestions.

The framework ultimately narrowed to two primary LLM chat threads conducting deep research across architectural options. Each generated detailed decision documents that became our evaluation foundation. These documents included five critical components:

• Technology Capability Assessment: What each component could actually do, not marketing promises but real-world functionality based on documentation and case studies.
  
• Mission Control Alignment: How each option supported our specific product requirements, from user authentication to content management to B2B SaaS capabilities.
  
• Multi-Criteria Analysis: Pros and cons evaluated against selection criteria including costs, extensibility, performance, robustness of capabilities, and integration complexity.
  
• Risk Assessment: Potential failure points and mitigation strategies, because every architectural decision carries technical debt implications.
  
• Community and Documentation Evaluation: The strength of community support and quality of documentation—a factor that would prove crucial for LLM productivity gains later in development.
  

This systematic approach transformed what could have been weeks of research into days of focused analysis. The LLMs didn't just accelerate information gathering—they structured it for better decision-making.

Critical Architecture Decisions

Our research process required decisions across five major architectural domains: backend infrastructure, database technology, authentication systems, service layer architecture, and frontend frameworks. Each decision interconnected with the others, creating a web of technical dependencies that demanded careful consideration.

• Backend Infrastructure: Evaluated AWS, GCP/Firebase, and Azure, focusing on startup needs like limited budgets, small teams, rapid iteration, and future scalability. 
  
• Database Decisions: Considered relational, document, and hybrid databases, as well as deployment options like managed services, self-hosted, and containerized solutions. Modeled scenarios from launch to scaling with LLMs. 
  
• Authentication and User Management: Explored built-in solutions, third-party services, and hybrid approaches to balance robust user experiences with strong security. 
  
• Service Layer: Assessed REST APIs, GraphQL, and hybrid options to weigh performance, flexibility, and development complexity. 
  
• Frontend Frameworks: Balanced short-term productivity with long-term goals, including potential expansion to mobile, desktop, and new platforms.
  

Throughout this process, our third North Star goal remained paramount: we needed to build something that demonstrated Real-World, Universal Capabilities. Every product team faces the same core requirements—user authentication, profile management, roles and permissions, content management, search and tagging, and B2B SaaS features like customer data segregation.

Our tech stack choices had to support all these capabilities without creating architectural bottlenecks. This wasn't about building the minimum viable product—it was about creating a foundation that could evolve from MVP to enterprise-grade platform.

The Winning Tech Stack

After extensive research and evaluation, our analysis yielded a specific technology combination: GCP/Firebase for backend infrastructure, containerized PostgreSQL for the database, Directus as our headless CMS, Hasura for the API layer, and Flutter for the frontend framework.

These choices weren't made in isolation—each component strengthened the others while addressing our strategic goals.

Google Cloud / Firebase emerged as our backend infrastructure winner for several compelling reasons. The Firebase ecosystem provides cost-effective document-based data management with authentication and user management built in. Google Analytics 4 integration gives us powerful analytics capabilities without additional complexity. Storage capabilities for assets and messaging systems come standard.

The cost structure perfectly aligns with startup realities—essentially free until significant traction develops, then backed by GCP's enterprise-grade scalability for future growth. The usability factor proved decisive; Firebase enables rapid development without sacrificing professional capabilities. Google's backing ensures long-term viability, while robust documentation and community support maximize LLM productivity gains.

PostgreSQL represented the safe, scalable choice for our primary database. As a battle-tested relational database with excellent performance characteristics and virtually unlimited scalability, PostgreSQL offers the reliability our first North Star goal demanded. The extensive community support and documentation also aligned with our LLM productivity requirements.

Directus solved our headless CMS needs with robust content management capabilities and cost-effective pricing. Among all CMS technologies we evaluated, Directus offered the best balance of functionality and affordability. Its integration capabilities with Firebase/GCP and Flutter sealed the decision, enabling us to demonstrate a complete content pipeline within Mission Control.

Hasura provided our API layer solution with strong PostgreSQL and Firebase integration. The GraphQL flexibility it offers enables efficient frontend queries while maintaining backend performance. Hasura's approach to API generation dramatically reduces boilerplate code while providing the extensibility our second North Star goal required.

Flutter won our frontend framework evaluation as a cross-platform solution that enables immediate web app development while preserving mobile expansion options. Flutter's growing ecosystem, strong Firebase/GCP integration, and compatibility with our other tech stack components made it the clear choice. The ability to reach iOS, Android, and web platforms from a single codebase offers exactly the flexibility and extensibility we needed.

The Community Support Advantage

One crucial insight emerged from our architectural planning process: when building systems you plan to enhance with LLM intelligence, choose well-supported, well-documented technologies. This principle became a key selection criterion that influenced every decision.

LLMs are only as capable as the training data they've learned from. Technologies with extensive documentation, active communities, and widespread adoption generate more training examples, leading to better LLM responses and higher productivity gains during development.

This insight proved prescient during actual development. Throughout the Mission Control build process, I received more consistent and substantive assistance from LLMs when working with Firebase, PostgreSQL, Hasura, and Flutter. These technologies have extensive documentation and active communities, resulting in training data that enables LLMs to provide detailed, accurate, and contextually appropriate guidance.

Directus presented the exception that proved the rule. As the least supported component in our tech stack, Directus consistently generated weaker LLM responses. Documentation gaps and smaller community adoption meant fewer high-quality examples for LLMs to learn from. This translated to more trial-and-error development, more debugging time, and lower overall productivity gains.

The lesson is clear: architectural decisions should factor in LLM ecosystem support as a legitimate selection criterion. In an AI-accelerated development world, choosing technologies that maximize LLM effectiveness becomes a competitive advantage.

Results and Recommendations

The process of using LLMs (large language models) to assist in architectural planning and tech stack selection delivered impressive results. By accelerating research and decision-making, we achieved significant productivity gains and created a strong, scalable foundation for development. Here are the key recommendations based on our experience:

• Accelerate research and evaluation: Use LLMs to speed up initial research, compare options, analyze criteria, and make informed choices faster. 
  
• Structure decision-making: Adopt a systematic framework to evaluate risks, integration challenges, and scalability considerations upfront rather than encountering surprises later. 
  
• Leverage LLMs as partners, not decision-makers: Use LLMs to gather data, structure comparisons, and highlight trade-offs, but keep final strategic decisions aligned with your specific goals in human hands. 
  
• Build for scalability and adaptability: Prioritize architectural decisions that support long-term growth, adaptability, and extensibility under real-world pressures. 
  
• Focus on foundations: A solid tech stack and well-thought-out architecture make everything built on top more reliable, scalable, and successful.
  

By combining the speed and insights of AI with human strategic judgment, teams can make better architectural decisions in less time. This approach not only accelerates development but also ensures the foundation is strong enough to support future growth and innovation.

Next up: we'll dive into how LLM assistance boosted the productivity of the development process and what we learned along the way to maximize results and avoid rollbacks.