We add production AI to your Java applications with Spring AI — retrieval-augmented generation, semantic and vector search, chat assistants and MCP servers — using OpenAI or self-hosted LLMs, without a separate Python service or a rewrite.
Spring AI is the Spring ecosystem's abstraction over large language models — the way Spring Data abstracts databases. It lets Java teams add chat, semantic search and retrieval-augmented generation to existing Spring Boot apps with portable, testable code, talking to a ChatClient and VectorStore rather than a vendor SDK. We design, build and harden those features end to end — and you keep the option to switch model providers later as a configuration change.
Ground LLM answers in your own data — docs, catalogues, tickets — so the model answers from facts, with citations, instead of hallucinating.
Natural-language search over your content using embeddings and a vector index (pgvector, Elasticsearch, Redis) instead of brittle keyword matching.
Production chat and assistant features built on Spring AI's ChatClient — typed, testable, and portable across model providers.
Expose your capabilities as Model Context Protocol tools so one well-tested toolset powers assistants, agents and self-serve experiences.
Add AI features inside the Spring Boot app you already run — no separate Python service, no rewrite. Your business logic talks to a ChatClient, not a vendor SDK.
The work that turns a demo into a system you can depend on: timeouts, fallbacks, token budgeting, caching and offline evaluation.
For a Java team, the practical choice for adding LLM features. We work with all three and recommend based on your stack, not dogma.
| Approach | What it is | Best for |
|---|---|---|
| Spring AI | Spring-native LLM abstraction (ChatClient, VectorStore, embeddings) with auto-configuration and testing. | Teams already on Spring Boot who want the least friction and provider portability. |
| LangChain4j | Framework-agnostic Java LLM library with its own chains, tools and memory abstractions. | Java teams not on Spring, or who prefer its specific abstractions. |
| Raw provider SDK | Calling the OpenAI/Azure/Vertex SDK or HTTP API directly from your code. | A single, simple call where an abstraction would be overkill — but it couples you to one vendor. |
We built OptaAI, an AI-native sourcing platform, on Spring AI, OpenAI embeddings and Elasticsearch vector search — with zero-downtime reindexing and a native MCP server serving both sales reps and customer self-serve. Read the case study →
Tell us about your app and what you want it to do — we'll map the fastest reliable path to production AI.