AI & Machine Learning Integration

Practical AI Integration, Not Science Projects

The most valuable AI features are often the least flashy. Automatically categorizing incoming support tickets saves your support team hours daily. Extracting line items from invoices eliminates manual data entry. Summarizing long documents helps your team process information faster. These are not moonshot projects — they are practical applications of existing AI capabilities.

We focus on use cases where AI provides clear, measurable value. Before building anything, we validate the approach with a small-scale test: can the AI handle your specific data with acceptable accuracy? What is the cost per operation? What happens when the AI gets it wrong? These questions get answered before we commit to a full implementation.

Our rule of thumb: if a human can do the task in under 30 seconds, AI can probably automate it reliably. If it requires significant judgment, domain expertise, or creativity, AI can assist but should not replace the human entirely.

RAG: Grounding AI in Your Data

The biggest limitation of language models is that they do not know your business. They can write fluent English, but they cannot answer questions about your products, policies, or internal documentation without context. Retrieval-Augmented Generation (RAG) solves this by giving the model access to your data at query time.

Our RAG architecture works in four steps: your documents are chunked and converted to vector embeddings (stored in pgvector or Pinecone), user queries are also converted to embeddings, the most relevant document chunks are retrieved via similarity search, and those chunks are provided as context to the language model along with the user query.

The result is an AI assistant that answers questions based on your actual documentation, with citations pointing to the source material. We implement this for customer support knowledge bases, internal documentation search, legal document analysis, and product information chatbots.

Engineering for Production AI

Production AI is 20% model selection and 80% engineering. We handle the engineering that makes AI features reliable: structured output parsing with Zod validation so your code can depend on consistent response formats, streaming responses for real-time UX, retry logic for rate limits and timeouts, and cost monitoring with per-user and per-feature budgets.

For latency-sensitive applications, we implement response caching for repeated queries, model routing (using faster, cheaper models for simple tasks and more capable models for complex ones), and parallel processing for batch operations. These optimizations can reduce both latency and costs by 50-80%. We also implement comprehensive logging of AI interactions — inputs, outputs, latency, token usage, and user feedback — creating a dataset that lets you evaluate model performance and continuously improve prompt designs.