Modernize your Enterprise SaaS!
Whether greenfield development or legacy modernization, Simplicitize helps companies move from AI experiments to production systems: LLMs and generative AI, Retrieval-Augmented Generation, and Deep Learning. These are served by proven JVM (Java, Kotlin, Scala), Python, and Rust microservices, using frameworks such as Spring Boot, FastAPI or Axum, and built with the engineering discipline that enterprise software demands.
What we do
Expert Level Software Engineering
Distributed systems on AWS, engineered to stay resilient and observable at scale. Polyglot by design: we work in Java, Kotlin, Scala, Python, and Rust, picking the right language for each problem.
AI Native Development
We work in Claude Code and Cursor every day. We transform existing codebases into AI-native systems, vibe code with professional guardrails and review discipline, and fix the AI slop unsupervised agents leave behind.
GenAI & Agentic AI
Production applications built on large language models: chat assistants, copilots, document intelligence, and autonomous agents that plan multi-step work and call tools to carry it out.
Retrieval-Augmented Generation (RAG)
Connect your proprietary knowledge to AI safely with retrieval-augmented generation and vector search. Semantic search matches on meaning rather than exact keywords, so users find answers even when they phrase things differently.
Deep Learning
Custom neural networks with PyTorch. We handle model training, fine-tuning, and inference optimization for computer vision and transformer architectures.
Forward Deployed Engineering
We embed a senior engineer with your team, onsite or remote, who works from your backlog and ships AI features in your codebase from week one.
Rust
Rust alongside the JVM and Python: memory-safe, high-performance services as part of a polyglot distributed-systems toolkit.
Software Architecture
System design that scales with your business: domain-driven boundaries, event-driven architectures, and pragmatic technology choices that keep large codebases evolvable for years.
ML Ops CI/CD Pipeline
GitHub Actions CI/CD pipelines and MLOps workflows. Evaluation, monitoring, and cost control keep your AI systems reliable after launch.
Containerization
Ship the same artifact everywhere: lean Docker images, multi-stage builds, and Docker Compose environments, so the build that runs on a laptop runs identically in production.