Artificial intelligence is no longer a futuristic concept — it's a practical tool you can integrate into your web application today. Here's how we approach AI integration at QorLogics.
Choosing the Right AI Capability
Start by identifying a specific problem AI can solve for your users:
- Customer support — LLM-powered chatbot that answers questions from your documentation
- Content generation — Auto-generate product descriptions, email drafts, or report summaries
- Intelligent search — Semantic search that understands intent, not just keywords
- Data extraction — Parse invoices, resumes, or contracts automatically
The Tech Stack
For most integrations, you need three things:
- An LLM provider (OpenAI, Anthropic Claude, or open-source alternatives)
- A vector database for storing embeddings (pgvector, Pinecone, Qdrant)
- An orchestration layer (LangChain, or custom logic in Laravel/Python)
RAG Architecture
Retrieval-Augmented Generation (RAG) is the most practical pattern for production AI features. Instead of sending everything to the LLM, you:
- Chunk your documents into segments
- Generate embeddings for each chunk
- Store embeddings in a vector database
- At query time, find the most relevant chunks
- Send only those chunks + the user's query to the LLM
This approach is cost-effective, gives you control over the knowledge base, and eliminates hallucinations on your proprietary data.
Start Small
Pick one feature, prototype it in a week, and iterate. The barrier to entry has never been lower.