Connect AI WithoutRebuilding Your Platform

FastAPI is our default for AI-adjacent APIs in Python - particularly when you're working with LLMs, vector databases, and ML models, where the Python ecosystem is most mature. Node.js (with Fastify or Express) is our choice when the team is JavaScript-native or when the API is primarily data-serving with minimal AI processing. Both support async operation equally well.

RAG (Retrieval-Augmented Generation) is the pattern that makes AI chatbots accurate for specific knowledge bases: documents are split into chunks, embedded into vectors, stored in a vector database, and retrieved by semantic similarity when a query arrives. The retrieved chunks are injected into the LLM prompt as context, grounding the response in your specific content rather than generic training data. We implement this with LangChain or LlamaIndex, using pgvector, Weaviate, or Qdrant for storage.

pgvector if you're already on PostgreSQL and want zero additional infrastructure - good for moderate-scale datasets. Weaviate for large-scale semantic search with strict multi-tenant isolation, native hybrid search, and built-in vectorization. Qdrant when lowest retrieval latency is the priority and you need complex payload filtering alongside vector similarity. We assess your specific requirements and recommend accordingly.

Primarily through semantic caching - storing LLM responses with their query embeddings and serving cached responses for sufficiently similar incoming queries. This typically reduces LLM API costs by 30-60% on platforms with high query repetition. We also implement multi-model routing (cheaper models for simple tasks), token counting and context management, and async processing to avoid wasted real-time API calls.

LangChain is a broad orchestration framework for LLM applications - providing composable abstractions for chains, agents, retrievers, and memory that work across many use cases. PydanticAI is a focused agent framework from the Pydantic team that prioritizes type safety and validation - ideal when you're building a FastAPI backend and want AI responses validated as typed Pydantic models before downstream use. We often use both: PydanticAI for the AI layer, LangChain for complex retrieval pipelines.

A focused RAG pipeline - document ingestion, vector storage, retrieval, and generation - can be production-ready in 2-4 weeks. A full AI product with conversational interface, admin dashboard, usage analytics, and monitoring typically takes 6-10 weeks. Timeline depends on data volume, retrieval complexity, and the breadth of features beyond the core pipeline.