aisure

An AI Optimization Advisor (aisure), that gives users advice on how they can use AI in order to optimize the things that they want optimized, for business or personal process optimization.

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Inspiration

What it does# aisure: AI AI Advisor

Inspiration

I created aisure after noticing how difficult it is for people to navigate the complex landscape of AI tools and services. Many businesses and individuals know they could benefit from AI but struggle to identify which specific tools would help them optimize their processes. I wanted to build an advisor that could bridge this knowledge gap by providing tailored, actionable recommendations.

What it does

aisure is an AI Optimization Advisor that recommends specific AI tools and technologies to improve business or personal processes. Key features include:

  • Users describe their process and receive AI tool recommendations
  • Adjustable detail level (1-5) controls recommendation specificity
  • Knowledge base integration provides context-aware suggestions
  • Document processing allows for customized recommendations
  • Azure AI Search powers relevant information retrieval

How I built it

I built aisure using a pragmatic hybrid approach:

  • Next.js 15.2 with TypeScript and Tailwind CSS for the frontend
  • Azure OpenAI with GPT-4o for generating recommendations
  • LangChain.js for orchestrating AI workflows
  • Azure AI Search for knowledge base retrieval
  • Custom prompt engineering focusing on AI tool recommendations

Challenges I ran into

  1. Time constraints: I had to focus more time on explaining GitHub Copilot usage rather than adding more features
  2. Knowledge freshness: Ensuring recommendations include the latest AI tools was difficult
  3. Balancing detail levels: Creating a system that adjusts between strategic and detailed recommendations required careful prompt engineering
  4. Integration complexity: Combining Azure services with LangChain required thoughtful architecture decisions

Accomplishments that I'm proud of

Mainly that I created an instructional video

What I learned

  1. How to effectively use RAG patterns to enhance AI recommendations with relevant knowledge
  2. Techniques for prompt engineering that enable adjustable response detail levels
  3. Methods for integrating document intelligence into AI workflows
  4. The value of hybrid architectures that combine Azure services with LangChain

What's next for aisure

  1. Expanding specialized knowledge: Adding more comprehensive coverage of AI tools and implementation patterns
  2. Implementation tracking: Adding features to track implementation progress and measure impact
  3. Fine-tuning for specific cases: Creating versions focused on particular sectors
  4. Integration capabilities: Building connectors to common business process tools

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