AI Solution Navigator
The Problem
Every week, business leaders face the same question: "Should we build custom AI, buy an existing solution, or take a hybrid approach?"
It sounds simple, but the decision is surprisingly complex:
- Build offers maximum customization but requires technical talent, time, and ongoing maintenance
- Buy gets you to market faster but may not fit your exact needs or integrate with existing systems
- Hybrid can offer the best of both but adds architectural complexity
Most organizations get this wrong. They either over-invest in custom development for problems that off-the-shelf tools already solve, or they buy solutions that can't adapt to their specific requirements. Both mistakes are expensive.
The challenge is compounded by a fast-moving market. New AI tools launch weekly. Pricing models vary wildly. Capabilities that required custom development six months ago are now available via API. Keeping up is a full-time job.
The result: Decision paralysis, wasted vendor evaluations, or costly pivots mid-implementation.
The Insight
After advising multiple clients on AI strategy, I noticed a pattern. The build-vs-buy decision isn't actually that hard if you ask the right questions in the right order:
- How sensitive is the data involved?
- How unique are the requirements?
- What's the integration landscape?
- What's the realistic timeline and budget?
- Does this need to be a competitive differentiator, or just work?
These questions, combined with current market knowledge, reliably point toward the right approach. The problem wasn't the decision logic. It was that most people didn't have a structured way to think through it, and they lacked visibility into what solutions already existed.
The Solution
I built the AI Solution Navigator to codify this decision process and pair it with real-time market research.
How it works
Users complete a structured intake covering their problem, constraints, and requirements. The tool then:
- Analyzes the inputs using Claude to reason through the build-vs-buy tradeoffs specific to their situation
- Researches the market using Perplexity to identify existing solutions, recent funding activity, and competitive positioning
- Generates a structured assessment with a clear recommendation, supporting rationale, and responsible AI considerations
The output isn't a generic framework. It's a tailored report that accounts for their specific data sensitivity, integration needs, timeline, and budget.
Technical Approach
The architecture reflects a core belief: use the right model for each task.
Claude handles the recommendation logic. It excels at reasoning through nuanced tradeoffs, weighing competing factors, and generating coherent explanations. This is a synthesis task, not a search task.
Perplexity handles the market research. It's purpose-built for web search with source attribution. When the user needs to know "who are the existing players in clinical documentation AI," Perplexity retrieves and synthesizes current information with citations.
This dual-model approach delivers better results than either could alone. Claude doesn't have real-time market data. Perplexity doesn't reason as deeply about user-specific context. Together, they cover both.
User Input → Claude (Analysis) → Recommendation
→ Perplexity (Research) → Competitive Landscape
→ Combined Report
The frontend is React with Tailwind, deployed on Vercel. The entire tool was built in under two weeks, demonstrating that meaningful AI products don't require months of development when scoped thoughtfully.
Design Decisions
Why a wizard instead of a chat interface?
Chat feels flexible but produces inconsistent outputs. A structured wizard ensures I collect the information needed to generate a useful recommendation. It also reduces user effort; answering six focused questions is faster than explaining your situation in freeform text.
Why include Responsible AI considerations?
Enterprise buyers increasingly require AI governance documentation. Surfacing bias risks, data handling implications, and compliance considerations upfront helps users anticipate procurement and legal requirements. It also signals that this tool was built by someone who understands enterprise deployment realities.
Why give away this analysis for free?
The tool demonstrates a methodology. It delivers real value, but it's a starting point, not a complete consulting engagement. Users who find it helpful and need deeper support know where to find me.
What This Demonstrates
The AI Solution Navigator serves as both a functional tool and a portfolio demonstration of:
- Product thinking: Identifying a real problem and scoping a focused solution
- Technical execution: Multi-model orchestration, API integration, structured output generation
- Domain expertise: Encoding best practices for AI solution evaluation into the product itself
- Pragmatic AI philosophy: Using foundation model APIs to deliver value quickly, rather than over-engineering
What I Learned
Building this reinforced a few principles:
Scope ruthlessly. The initial concept included saved assessments, comparison features, and team collaboration. I cut all of it for V1. The core value is the assessment itself. Everything else can come later.
Two models beat one. The dual-API approach added complexity but meaningfully improved output quality. This pattern, using specialized models for their strengths, applies broadly.
Show, don't tell. This tool demonstrates AI product skills more effectively than any resume bullet point could. Working software is the best portfolio.