Java Microservices & Custom Enterprise Systems
High-throughput Spring Boot 3 / Java 21 microservices, Apache Kafka event pipelines, legacy monolith modernisation, and custom operational backends.
- CTOs and VPs of Engineering untangling slow legacy Java/.NET monoliths or brittle batch jobs
- Logistics, fintech, and B2B operations teams outgrowing off-the-shelf ERP/CRM limits
- Engineering managers needing senior Spring Boot & Kafka capacity without a 4-month hiring cycle
Technical bottlenecks we resolve
Concrete operational and architectural issues we eliminate for engineering teams.
Tightly coupled monoliths where every release risks production regressions
Incremental strangler-fig service extraction with contract tests and isolated databases.
Dropped webhooks, duplicate orders, or out-of-sync ERP inventory under peak load
Partitioned Kafka topics paired with transactional outbox consumers and dead-letter queues.
Per-seat SaaS licensing sprawl and manual spreadsheet handoffs across ops teams
Role-based internal workflow engines built around your exact approval and audit rules.
Distributed Java 21 & Spring Boot 3 Service Architecture with Apache Kafka
API Gateway & IAM
Rate limiting, role-based access claims, and request validation.
Event Broker
Partitioned topics, transactional outbox pattern, and dead-letter recovery.
Domain Services
Clean domain-driven services using virtual threads for high I/O concurrency.
ACID Databases & Cache
Relational integrity, audit logging, and low-latency read caching.
Java 21 Virtual Threads
High request concurrency on lean compute footprints without complex reactive callback chains.
Custom Operational Workflows
Replace brittle spreadsheet glue and per-seat vendor bottlenecks with maintainable domain services.
At-Least-Once Event Consistency
Transactional outbox and idempotent Kafka consumers engineered to prevent silent message drops.
Automated Integration Testing
Testcontainers suites spin up real PostgreSQL and Kafka instances in CI on every pull request.
Start with one bounded workflow before any ongoing commitment
Clear deliverables, client prerequisites, exclusions, and acceptance criteria so expectations match on both sides.
- Clean hexagonal Spring Boot 3 service repository in your Git org
- Kafka producer/consumer setup with dead-letter queue and schema validation
- Automated Testcontainers integration tests for PostgreSQL and Kafka flows
- Docker image, Helm/Compose config, and OpenTelemetry tracing hooks
One bounded domain service or Kafka event pipeline with PostgreSQL schema and OpenAPI spec.
Sample payload schemas, access to target staging repo/cloud, and a 60-minute Day 1 domain walkthrough.
Full multi-year monolith migration, historical data cleansing, and third-party ERP vendor support tickets.
Passing Testcontainers integration suite, documented load benchmark, and live staging endpoint. Backed by a 10-business-day defect remediation window. 100% of IP assigns upon pilot invoice settlement.
High-Throughput Fleet & Dispatch Telematics Engine
Completed Client Engagement (Client anonymized under mutual NDA)
Client Problem
A commercial fleet operator relied on polling-based REST endpoints and separate Android/iOS codebases. During peak transit hours, database lock contention delayed dispatch updates by over 45 seconds, and drivers frequently lost job status updates on low-signal highway stretches.
What We Delivered
- Partitioned Apache Kafka GPS & status ingestion pipeline with dead-letter recovery
- Spring Boot 3 (Java 21) dispatch allocation and corridor geofencing microservices
- Shared Kotlin Multiplatform (KMP) offline SQLDelight sync engine for Android & iOS driver apps
- Grafana / Prometheus operational latency and consumer-lag alerting
Measured Results & Basis
Measured by comparing p95 server-timestamp-to-driver-device-ack latency across 4 weeks pre-cutover vs. 60 days post-rollout.
Calculated via Lines-of-Code ratio in the shared `:core` KMP module vs. platform-specific SwiftUI/Compose UI modules.
Common technical & commercial questions
Key implementation, security, and pilot sprint answers for your engineering team.
Can your engineers embed directly into our existing GitHub/GitLab workflow and PR standards?↓
Yes. We work inside your repositories from day one, adhering to your branch protection, SonarQube/lint checks, and code review gates.
Do you rewrite monoliths from scratch or migrate incrementally?↓
Incremental strangler-fig extraction is our default. Extracting one high-value domain slice first reduces operational risk and proves throughput gains early.
Scope a Backend & Distributed Systems sprint with Taimoor Hassan
Your selected service carries automatically into our discovery scheduler or enquiry form.