From Idea to Execution: How I Use AI Assistants as a Solutions Architect to Boost Productivity
As a Solutions Architect, I spend most of my day moving between code, design, and client communication. My work revolves around taking an abstract business problem and turning it into a clear, technically viable solution — complete with architecture diagrams, data flow maps, and prototype code.
Introduction: Living the Multi-AI Workflow
As a Solutions Architect, I spend most of my day moving between code, design, and client communication. My work revolves around taking an abstract business problem and turning it into a clear, technically viable solution — complete with architecture diagrams, data flow maps, and prototype code.
Over the past year, I experimented with several AI assistants — ChatGPT, Codex, Claude Web, Cursor, Perplexity, and Comet — to see how each could complement different parts of my workflow.
What started as curiosity evolved into a productivity revolution. Today, these assistants function almost like extended team members — helping me brainstorm, diagram, code, validate, and even communicate better with clients.
Phase 1: Using AI for Ideation and Conceptual Analysis
When starting with a new project, my first task is to analyze client requirements and visualize them in a way that both developers and business stakeholders can understand.
How I Use AI Assistants Here
- ChatGPT (Atlas): Acts as my strategic brainstorming partner. I describe a business problem in natural language, and ChatGPT breaks it down into system components, data flows, and integration points.
- I then ask it to generate flow diagrams — either by suggesting structured text (for tools like Mermaid or draw.io) or by explaining logical architecture layers.
- For example, when designing a property booking flow for a multi-tenant system, ChatGPT helped visualize data exchange between booking, lead, and supplier modules.
- Claude Web: Provides long-context reasoning for research-heavy projects. When I need to assess the impact of new technologies (e.g., integrating Okta SSO across multiple platforms), Claude helps analyze policy documentation or technical standards and distill them into concise, stakeholder-friendly summaries.
- Perplexity: Acts as my AI search companion — perfect for verifying facts, finding reference architectures, or checking current API standards. It provides source-cited answers, ensuring credibility.
Result: Within hours, I can move from idea → analysis → architecture outline — a process that used to take days.
Phase 2: Translating Architecture into Actionable Design
Once the architecture is defined, I need to produce flow diagrams, component relationships, and deployment blueprints to explain to both internal teams and clients.
Tools in Play
- ChatGPT + Claude for generating text-based diagram logic (like UML, Sequence, or ERD).
- Codex for generating infrastructure scripts (e.g., AWS CDK, Terraform, or Docker).
- Cursor for collaborative review — integrating directly with GitHub repositories.
I often create a technical flow using AI to generate the first draft of diagrams:
After AI generates this, I refine it manually, then use Claude or ChatGPT to validate scalability, latency, or potential single points of failure.
graph TD
A[Client Request] --> B[API Gateway]
B --> C[Application Layer]
C --> D[Database Cluster]
C --> E[External Services]
E --> F[Notification System]
Performance Note:
AI-generated architecture diagrams drastically improve client communication speed — clients visualize the concept instantly, leading to faster alignment and reduced revision cycles.
Phase 3: AI in Development — Coding, Reviewing, and Refactoring
This is where the developer-focused tools shine — particularly Codex, Cursor, and ChatGPT in code mode.
Codex — The Developer’s Engine
- Codex, integrated with GitHub Copilot, lets me move from architecture to code seamlessly.
- When I’m prototyping a microservice or data model, Codex suggests boilerplate code, validation logic, or quick test cases.
- It’s particularly strong with API automation, Python scripts, and SQL query generation.
For example:
I once built an integration service connecting a PostgreSQL database to AWS SageMaker inference endpoints. Codex generated the entire base code in minutes — freeing me to focus on the model pipeline logic.
Cursor — Smart Collaboration
- Cursor integrates directly with GitHub and GitLab, providing AI-assisted code reviews and multi-document context understanding.
- When managing multiple branches or reviewing large pull requests, Cursor provides explanations, impact summaries, and even refactoring suggestions.
This allows our development teams to focus on the why of changes rather than the what.
ChatGPT & Claude — Debugging and Refactoring
When code reviews flag an issue, I use ChatGPT to isolate performance bottlenecks or generate alternative logic. Claude, with its long-context understanding, helps refactor larger codebases while maintaining logic consistency.
Result: Code review sessions that once took 3–4 hours now finish in under an hour, with better-quality documentation and improved team understanding.
Performance & Productivity Insights
Overall, integrating these AI assistants cut down project turnaround time by nearly 50%, while increasing clarity and documentation quality across teams.
Security & Data Awareness
As a Solutions Architect, I also evaluated how secure and compliant these tools are — especially while handling client or code data.
- Codex & Cursor: Safe within IDE/workspace; minimal external data transfer.
- Claude & ChatGPT: Provide opt-in memory and strict privacy modes for enterprise use.
- Perplexity: Read-only and citation-based; no personal data retained.
- Comet: More caution needed for agentic browsing due to automation risk.
These checks are vital before allowing AI to interact with live systems or sensitive repositories.
Lessons Learned and Best Practices
- Use the right AI for the right task.
Codex for coding, Claude for reasoning, ChatGPT for brainstorming, Cursor for reviews.
- Visualize everything early.
Generating diagrams upfront prevents misalignment later.
- Keep humans in the loop.
AI is a copilot — not an autopilot. Manual oversight ensures quality and accountability.
- Document as you build.
AI-generated documentation improves transparency for both clients and new developers.
Conclusion: Building Faster, Smarter, and More Visually
The real advantage of using AI assistants as a Solutions Architect isn’t just speed — it’s clarity.
Each assistant plays a unique role in transforming abstract ideas into executable systems, and when used together, they create a continuous flow of innovation.
By combining:
- ChatGPT for ideation,
- Claude for reasoning,
- Codex & Cursor for code execution and collaboration, and
- Perplexity for real-time factual grounding,
—you can elevate your workflow from reactive problem-solving to proactive solution design.
AI assistants don’t replace architects; they amplify them.
References