Intelligence with context
Spring AI, retrieval and governed tool use turn enterprise knowledge into useful application behaviour.
SOFTWARE ARCHITECTURE × ENTERPRISE AI
Intelligence,engineered.
From enterprise knowledge to intelligent products. From Java services to resilient platforms. Architecture that connects the whole system.
ONE CONNECTED ENGINEERING DISCIPLINE
JavaSpringSpring AIKafkaRedisKubernetes
01 / THE ARCHITECTURE MINDSET
Complexity is inevitable.Clarity is designed.
The architect’s work is to make the important decisions visible: where intelligence lives, how services communicate, who owns the data, and how the system behaves under pressure.
Boundaries with purpose
Domain-led services, explicit API contracts and event flows make distributed systems understandable.
Operations by design
Delivery, security, resilience and observability are part of the architecture from the first decision.
02 / INTERACTIVE ARCHITECTURE STUDIO
See the system.Explore the decisions.
Three architecture patterns. Select a component to reveal its responsibility, technology and design decisions.
Knowledge → context → useful intelligence
Retrieval, model orchestration and governed tools within a secure application boundary.
Select a component to explore8 components · 7 connections
Compact view · component connections are listed in the inspector.
03 / INTELLIGENCE WITH ENTERPRISE CONTEXT
Your knowledge.A new interface.
SPRING AI / JAVA
A language model becomes valuable when it can retrieve the right information, respect access boundaries and connect to real business capabilities. Spring AI brings that orchestration into the Java ecosystem.
RAG / RETRIEVAL-AUGMENTED GENERATION
A grounded answer, step by step
Prepare
Parse, chunk and enrich enterprise content.
DocumentReader · TokenTextSplitterIndex
Store embeddings with metadata and access scope.
EmbeddingModel · VectorStoreRetrieve
Find relevant, authorized context for the question.
Retriever · Metadata filtersGenerate
Ground the prompt in retrieved source material.
ChatClient · AdvisorsEvaluate
Check source support, output structure and quality.
Evaluation · Validation
Designed around relevance, provenance and control.
BEYOND ANSWERS
Intelligence that can work.
Tool calling and MCP connect the model to explicitly permitted business operations. Validate inputs, enforce authorization and keep consequential actions under control.
Tool callingMCPStructured outputs
DEPLOYMENT IS AN ARCHITECTURE CHOICE
Choose where intelligence runs.
Hosted model APIs, Ollama or vLLM through compatible endpoints. Select the model and serving approach around data boundaries, latency, cost and workload.
OllamavLLMModel APIs
04 / TECHNOLOGY ATLAS
[ 57 ]Deep in the stack.Clear on the choices.
Explore the ecosystem by responsibility. Each technology has a place, a reason and a relationship to the rest of the system.
AI & INTELLIGENCE
From enterprise context to model-powered behaviour.
No matching technology. Try a different term.
Spring AI
Java-native orchestration for chat models, retrieval, tools and structured responses.
Design note
Use ChatClient and Advisors to keep model interaction within application contracts.
AI & intelligence
Spring AI · RAG · Embeddings · Vector search · pgvector · Qdrant · Tool calling · Model Context Protocol · Structured outputs · AI evaluation · Ollama · vLLM
Java & backend
Java 17 / 21 · Spring Boot · Spring Data JPA · Hibernate
APIs & identity
REST · gRPC / Protobuf · GraphQL · SOAP · Spring Security · OAuth 2.0 · OpenID Connect · JWT
Spring Cloud
Spring Cloud Gateway · Spring Cloud Config · Spring Cloud LoadBalancer · Spring Cloud Stream · Spring Cloud CircuitBreaker · Resilience4j · Spring Cloud Sleuth
Data & caching
Oracle · MySQL · Sybase · MongoDB · PostgreSQL · Redis
Events & messaging
Apache Kafka · Apache ActiveMQ · Spring for Apache Kafka · Spring JMS
Delivery & quality
Docker · Kubernetes · Jenkins · Maven · Gradle · JUnit · Mockito · Apache JMeter · Git
Observability
Micrometer · Micrometer Tracing · OpenTelemetry · Prometheus · Grafana · ELK stack · Zipkin
05 / DECISIONS BEFORE CODE
The architectureis in the decisions.
A platform is more than its diagram. The quality is in the boundaries, contracts and trade-offs that make it work in production.
Service boundaries
Model business ownership, data boundaries and change frequency before dividing the deployment.
API or event?
Use REST or gRPC for direct interactions. Use events when workflows should progress independently.
Identity & access
Separate sign-in, API authorization and service identity. Enforce permissions at every relevant boundary.
Data consistency
Prefer owned transactions. Add outbox, idempotency and compensating workflows when the system needs them.
Resilience under pressure
Design timeouts, bounded retries, circuit breakers and isolation as a coherent failure strategy.
Grounded intelligence
Retrieve authorized context, preserve source references and validate model outputs before business use.
Where the model runs
Choose hosted or private inference around data boundaries, model fit, serving capacity and workload.
Evidence in production
Connect metrics, traces and logs with AI evaluations so decisions can be tested against real behaviour.
06 / YOUR ARCHITECTURE PARTNER
Software Architect & Consultant
Fahri Kaya.
I bring more than 16 years of enterprise software engineering experience to architecture, implementation and technical consulting. Through BNTech, I help teams connect established Java/Spring platforms with intelligent capabilities and a clear path to delivery.
“The best architecture makes the next decision easier.”
- Architecture & technical direction
- Hands-on engineering
- Enterprise AI integration
- Modernization & mentoring
07 / LET’S BUILD WHAT COMES NEXT
A complex challenge?Let’s architect it.
An AI initiative, a distributed platform, or a system ready for its next chapter. Tell me what you’re building.