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Taishi H.
Taishi H.

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Building an AI Document Analysis Platform with RAG and Multiple LLMs

#rag #llm #nextjs #ai

As a software engineer, I enjoy building AI applications that solve real business problems.
One of my projects is DocumentAI Analysis Platform, an AI powered platform that helps users analyze documents and find information quickly.
The goal is simple: instead of manually reading long documents, users can upload files and ask questions in natural language.

The platform supports PDF, Word, Excel, and scanned documents. It uses Retrieval Augmented Generation (RAG) to retrieve relevant information and generate answers using AI models such as OpenAI, Claude, and Gemini.

The project is built with Next.js, React, TypeScript, LangChain, Pinecone, and AWS S3.

Key Engineering Challenges and Solutions

  1. Processing Large Documents Efficiently

Challenge: Large documents can take a long time to process, and retrieving useful information from them can be difficult.

Solution: I designed a document processing pipeline that extracts content from different file formats, divides documents into smaller chunks, and stores vector embeddings for efficient retrieval. The project targets processing 100 page PDFs in under 30 seconds.

  1. Improving Answer Relevance

Challenge: An LLM may generate incorrect or irrelevant answers when it does not have the right information from a document.

Solution: I used RAG with Pinecone vector search to retrieve relevant document content before sending the context to the AI model. This helps ground answers in the source material instead of relying only on the model's general knowledge.

  1. Handling AI Model Failures

Challenge: Depending on a single AI provider can create reliability problems when a model is unavailable or returns an error.

Solution: I incorporated multiple AI providers and an intelligent fallback approach. This allows the application to switch models when needed, improving resilience and reducing dependence on a single provider.

  1. Building a Maintainable Full Stack Application

Challenge: Combining document processing, AI integrations, storage, authentication, and user facing features can make an application difficult to maintain.

Solution: I organized the application into separate modules for document processing, AI workflows, database access, vector search, API routes, and UI components. I also included unit, integration, and end to end testing support.

What I Learned

This project reinforced an important lesson: building a useful AI product requires more than connecting an LLM to a user interface.

Document processing, retrieval quality, error handling, security, and system architecture are equally important.

I enjoy solving these engineering problems and building AI powered products that are useful, reliable, and maintainable.

Looking for Similar Solutions?

I'm available for freelance projects involving AI applications, RAG systems, LLM integrations, backend development, and full stack engineering.

If you're planning to build an AI product or improve an existing application, feel free to connect with me.

GitHub: https://github.com/goal31121

AI RAG LLM SoftwareEngineering WebDevelopment

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