Kiryl Rusanau

I offer six services focused on Java + AI integration: Java Stack AI Assessment (identify where LLMs add value in your Spring Boot or Quarkus services), AI Integration Roadmap (phased plan with framework recommendations), LLM Integration Sprint (2-4 week engagement to ship a specific AI feature), Java AI Workshop (hands-on team training with LangChain4j and Spring AI), AI-Ready Architecture Review, and AI Code & Cost Review. All services are built around existing Java systems.

The easiest way is to book a free consultation directly through my calendar (link available on this page). You can also reach me via email at [email protected] or through LinkedIn. I typically respond within 24 hours.

I have production experience integrating LLM capabilities into enterprise Java applications using LangChain4j and Spring AI frameworks. This includes building RAG (Retrieval-Augmented Generation) pipelines with vector databases, implementing prompt chains for structured output parsing, and deploying conversational AI systems. My AI work spans both enterprise integrations and consumer-facing AI products.

My primary AI toolkit includes LangChain4j for Java-based LLM integration, Spring AI for enterprise Spring Boot applications, and LangGraph (Python) for multi-agent workflow orchestration. I work with various LLM providers and have experience implementing vector stores, embeddings pipelines, and production-grade prompt engineering patterns.

I follow a structured approach: designing RAG architectures for domain-specific knowledge retrieval, implementing vector embeddings with appropriate chunking strategies, building prompt chains that produce reliable structured outputs, and ensuring proper error handling and fallback mechanisms. The integration respects enterprise concerns like security, observability, and cost optimization.

I built CartSync, a conversational AI grocery planning assistant on Telegram (now completed) that demonstrated practical AI integration in consumer applications. I'm also developing Quibench, an AI-powered productivity platform — you can see a demo at https://youtu.be/16qD_FMklvA. Both projects showcase real-world AI integration patterns.

Quibench is an AI productivity platform I'm developing that focuses on workflow optimization using LLM technologies. It aims to enhance team collaboration and individual productivity through intelligent automation and AI-assisted task management. Watch the demo: https://youtu.be/16qD_FMklvA

I have 7+ years of experience as a full-stack engineer with deep expertise in Java (8-21), Spring Boot ecosystem, React with TypeScript, and AWS cloud infrastructure. My background includes building microservices at scale, implementing OAuth2/OIDC authentication systems, and working with event-driven architectures using Kafka.

I spent nearly 4 years at Azati Corporation working on financial technology projects including cryptocurrency custody platforms with Fireblocks integration, securities registration systems for regulatory compliance, and banking authentication systems managing identity across multiple services. This experience shaped my understanding of security-first development and compliance requirements.

Yes, I offer six consulting services focused on AI integration for Java-powered businesses: Java Stack AI Assessment, AI Integration Roadmap, LLM Integration Sprint, Java AI Workshop, AI-Ready Architecture Review, and AI Code & Cost Review. Book a free consultation through the link on this page, or email [email protected].

I'm based in Poland and work with clients worldwide. The best way to start is by booking a free consultation (link available on this page). You can also reach me via email at [email protected] or through LinkedIn.