AI ModelMatch

Multi-AI Model Response Analyzer - AI ModelMatch is a platform that allows users to submit a single prompt and instantly view responses and model’s reasoning process from 4 AI models side by side.

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Inspiration

Curiosity about ai model distillation. and to know whether a smaller model can outperform bigger model if given better data. short answer -> (yes)

What it does

AI ModelMatch is a platform that allows users to submit a single query and instantly view responses from four distinct AI models side by side. Beyond simply displaying answers, the Analyzer provides transparent insights into each model’s reasoning process, making it possible to observe how different algorithms interpret data, weigh evidence, and arrive at conclusions.

Key Features -

  • Simultaneous Multi-Model Comparison: Enter a prompt and receive detailed outputs from four leading AI models, enabling rapid benchmarking and qualitative assessment.

  • Customizable Prompt Scenarios: Design and input custom prompts or business scenarios relevant to your workflows, products, or customer interactions. you can now tailor test cases and queries to reflect your unique operational challenges and industry-specific needs.

  • Effortless Prompt Response Sharing: Instantly share the responses generated by different AI models with your team or stakeholders using our built-in sharing functionality. Our platform makes it simple to distribute prompt responses —accelerating decision-making and fostering collaboration across your organization.

Business Advantage

  1. Enhanced Decision-Making
  2. Benchmarking & Validation: Businesses can objectively compare model outputs to select the most reliable or context-appropriate AI for their needs, reducing the risk of bias or error.
  3. Faster Model Selection: Quickly identify which model performs best for specific tasks, accelerating deployment cycles and improving time-to-market.
  4. AI Model Distillation Support
  5. Knowledge Transfer: By analyzing how each model reasons, organizations can facilitate the distillation process—transferring knowledge from larger, complex models (teacher models) to smaller, more efficient ones (student models).
  6. Performance Optimization: Insights from model comparisons help teams design distilled models that retain accuracy while being faster and more cost-effective to deploy, especially in resource-constrained environments or edge devices.

How we built it

The entire backend is a big serverless app using mostly all aws services. The application uses several AWS resources, including Lambda functions, API Gateway RESTAPIs, WebSocketAPI, Bedrock, Cognito, DynamoDB, SAM, Cloudformation, cloudfront, and S3.

Challenges we ran into

Developing a serveless architecture is really hard. I have developed a few lambda functions before, but never a complete app like this. this is my first time developing a full-fledged serverless app.

Accomplishments that we're proud of

Developed and deployed an entire web application using serverless architecture. I am really proud of myself :)

What we learned

SAM, Bedrock, Cognito, WebSocket API Gateway.

What's next for AI ModelMatch

If I win, I will purchase a domain name, add stripe payment, and launch the product officially. It is still deployed though, but I need AWS credits to run this app which i currently don't have. and I don't know how and where to get it. I am using a lot of AWS services. It is tough to support an ai project without any support. I would really appreciate if somebody from AWS can help me out in this process.

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