Find the Right AI Model
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About this project
In one sentence, what are you building?
A constraint-driven recommendation system that helps you select the most appropriate generative AI model for your specific use case
What problem are you solving?
Problems This Project Solves 1. ❌ “Which model should I use?” confusion People are overwhelmed by the number of AI models and don’t know where to start. This project gives a structured way to decide. 2. ❌ Choosing models based on hype or popularity Most decisions are driven by Twitter, blog posts, or brand names. This replaces hype with requirements and constraints. 3. ❌ No clear way to compare models fairly Models are rarely compared using the same parameters. This standardizes comparison across cost, performance, security, and integration. 4. ❌ Black-box recommendations Most tools say “use this model” without explaining why. This shows exactly why a model was selected and what trade-offs were made. 5. ❌ Overpaying for powerful models Teams often use expensive models when cheaper ones would work. This helps users optimize for cost without sacrificing needs. 6. ❌ Ignoring production realities Latency, rate limits, reliability, and vendor lock-in are often discovered too late. This surfaces operational risks before deployment. 7. ❌ Security and compliance handled too late Data privacy is frequently an afterthought. This makes security and data usage a first-class decision factor. 8. ❌ Beginners don’t know what to ask Many users don’t understand context windows, fine-tuning, or RAG. The system guides them and explains assumptions in plain English. 9. ❌ Engineers and managers don’t align Technical decisions are hard to explain to non-technical stakeholders. This generates a shareable, explainable decision report. 10. ❌ Hard to test before committing Teams choose models without real evaluation. This supports side-by-side testing and cost estimation. 11. ❌ One-size-fits-all recommendations Most tools assume one “best” model. This focuses on best-fit based on your constraints. 12. ❌ Vendor lock-in surprises Switching models later is expensive and risky. This highlights lock-in and fallback readiness early.