Real-World AI Use Cases
Complete technical stack breakdowns for production AI applications, from concept to deployment
Enterprise Software
High
3-6 months
$150K - $500K
E-commerce / SaaS
Medium
2-4 months
$75K - $200K
FinTech
High
6-12 months
$300K - $1M
Healthcare
Very High
12-18 months
$1M - $5M
Frontend Layer
Web Application
React-based UI with real-time chat
Mobile App
React Native for on-the-go access
Slack/Teams Integration
Bot interface for existing workflows
Processing Layer
API Gateway
Authentication, rate limiting, routing
LLM Service
GPT-4 with custom prompts and guardrails
Search Service
Hybrid vector + keyword search
Data Layer
Vector Database
Pinecone for semantic search
PostgreSQL
User data, metadata, analytics
File Storage
S3 for documents and assets
1. Query Processing
When a user asks "How do we handle customer refunds?", the system:
- • Converts question to embedding vector using OpenAI API
- • Adds metadata filters (department, document type)
- • Performs similarity search in Pinecone
2. Context Retrieval
The vector database returns relevant chunks:
- • Top 10 most similar document chunks
- • Associated metadata (source, date, author)
- • Confidence scores for each result
3. LLM Integration
The retrieved context is combined with the user's question in a carefully crafted prompt:
You are a helpful assistant for Acme Corp employees. Use the following context to answer the user's question. If the information isn't in the context, say so clearly. CONTEXT: [Retrieved document chunks with metadata] QUESTION: How do we handle customer refunds? Provide a clear answer with source citations.
4. Response Generation
- • GPT-4 processes prompt + context
- • Generates human-like response
- • Includes source citations
- • Follows company guidelines
5. Quality Assurance
- • Validates response against guidelines
- • Checks for hallucinations
- • Logs interaction for monitoring
- • Collects user feedback