I help teams integrate LLMs and AI capabilities into real products and business workflows — from AI APIs and RAG to structured workflows, validation, and production backend integration.

The goal is not just to make an AI demo work, but to connect AI capabilities with the application logic, data, workflows, and reliability needed for real product use.

LLM & AI API Integration Integrate LLM and AI model APIs into existing products and backend systems, including structured outputs, data flows, application logic, and error handling.

AI Workflow Development Build multi-step AI workflows with routing, validation, state management, human-in-the-loop steps, and business rules around model outputs.

RAG & Knowledge Retrieval Build retrieval-based AI features including document ingestion, chunking, search and retrieval, grounded responses, source references, and evaluation.

Backend & Product Integration Connect AI capabilities with existing APIs, databases, authentication, business workflows, and frontend applications so they become part of the product rather than an isolated AI feature.

Reliability & Evaluation Add validation, failure handling, logging, monitoring, and evaluation to make AI-powered workflows more observable, testable, and maintainable.

My experience includes building AI-assisted decision workflows, RAG applications, and a multi-step AI product with human clarification and persistent workflow state.

My primary implementation stack includes Python / FastAPI, Next.js, Java / Spring Boot, and LLM APIs, depending on the product and existing system.

This service is best suited for teams that already have a product, workflow, or AI use case and need the engineering work to turn it into a reliable, integrated feature.