AI Engineering

• Agentic AI

  Autonomous agents that plan multi-step work, call tools and APIs, and iterate toward a goal instead of stopping at generated text. Built with LangChain and LangGraph, the Model Context Protocol, and the Claude ecosystem (Agents, Skills, and Sub-Agents), and served through AWS Bedrock, so foundation models run fully managed inside your own AWS environment with enterprise security, compliance, and scaling.

• Retrieval-Augmented Generation (RAG)

  Retrieval-augmented generation over your proprietary data: ingestion pipelines, embeddings and vector databases, evaluation harnesses, and privacy guardrails. Semantic search matches on meaning rather than exact keywords.

• Deep Learning

  Custom neural networks with PyTorch: NLP (natural language processing) for text understanding, convolutional neural networks (CNNs) for computer vision, RNNs for sequence modeling, GANs for generative tasks, and RLHF (reinforcement learning from human feedback) for aligning models with human preferences. Each is trained, fine-tuned, and served in production.

• GenAI

  Custom LLM-powered products and internal tools: chat assistants, copilots, document understanding, and content generation, built type-safe and tested. Every feature ships with structured outputs, evaluation harnesses, and guardrails, so generative quality holds up in production.

• 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.

• MLOps & Production Operations

  Deployment, observability, cost and latency optimization, regression evaluation, and CI/CD for AI systems, so quality holds up in production.

• Forward Deployed Engineering

  A senior engineer embedded directly with your team, onsite or remote, who ships AI features inside your codebase and workflows instead of working at arm's length behind a statement of work.

• Agentic AI toolchain

  The plumbing behind our agentic work: Anthropic's SDKs and the Model Context Protocol for tool-using agents, orchestration frameworks for multi-step workflows, and vector databases for retrieval.

  • Anthropic SDK & API
  • Model Context Protocol (MCP)
  • MCP Inspector
  • Python MCP SDK
  • Agent Skills & Subagents
  • Claude Code
  • Cursor
  • Amazon Bedrock
  • LangChain / LangGraph
  • LangChain4J
  • LlamaIndex
  • Prompt engineering
  • Vector databases (Pinecone, Weaviate, Milvus, Qdrant)

Distributed Systems

• Enterprise Integration & Modernization

  Deep roots in enterprise distributed systems: RESTful APIs, event-driven microservices, and real-time WebSockets engineered for global scale. We build greenfield systems and modernize legacy platforms into AI-ready architectures.

• Security-Minded Delivery

  Identity management, session hardening, and injection-proof validation: enterprise experience applied to every AI surface we ship.

• 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.

• Polyglot Engineering, JVM at the Core

  Deep JVM expertise in Java, Kotlin, and Scala, complemented by Python, Rust, Go, and TypeScript. Whether it's high-performance systems, memory-safe services, or rapid ML and product iteration, each is built with the language best suited to the domain.

• Kotlin

  We reach for Kotlin first on the modern JVM: coroutines for structured concurrency, Ktor for lightweight services, Arrow and Exposed for a pragmatic functional style, and test discipline from Kotest and MockK.

  • Coroutines
  • Ktor
  • Serialization
  • Arrow
  • Exposed
  • Koin
  • Kotest
  • MockK
  • Clikt

• Java

  Our Java experience runs from Java EE and RESTful web services through modern cloud-native microservices, with Hibernate/JPA persistence, Lombok, and a JUnit and Mockito testing culture, built with Maven and Gradle.

  • Java 8 – 25
  • Spring Boot Microservices
  • RESTful APIs
  • Spring AI
  • Hibernate / JPA
  • Lombok
  • JUnit & Mockito
  • Maven & Gradle

• Spring

  Most of our JVM services run on Spring: Spring Boot microservices and Spring MVC through Reactive Spring (Reactor, WebFlux, RSocket, Netty) and real-time messaging over STOMP WebSockets.

  • Spring Boot microservices
  • Spring MVC
  • Spring JDBC
  • Reactive Spring (Reactor, WebFlux)
  • RSocket & Netty
  • STOMP WebSockets
  • Hibernate / JPA

• Python

  Python carries our AI work: PyTorch and TensorFlow for modeling, FastAPI and Pydantic for typed services, all run on the Astral toolchain (uv for fast, reproducible environments, ruff for linting and formatting, and ty for type checking).

  • PyTorch
  • TensorFlow
  • Astral toolchain (uv, ruff, ty)
  • FastAPI
  • Pydantic
  • SQLAlchemy
  • Django
  • NumPy
  • Jupyter

• Rust

  Memory-safe systems programming for performance-critical services. Ownership-driven design eliminates whole classes of bugs. We build with Cargo and deploy alongside our JVM and Python services.

  • Cargo
  • Memory-safe services
  • High-performance APIs

• Scala

  Functional programming on the JVM: the Typelevel stack (Cats, Cats Effect, http4s, circe) plus reactive architecture, including domain-driven design, CQRS, event sourcing, and distributed messaging patterns.

  • Cats & Cats Effect
  • http4s
  • circe
  • Typelevel stack
  • Reactive microservices
  • CQRS & event sourcing
  • Domain-driven design

Advisory

• AI Strategy & Advisory

  Opportunity assessment, build-vs-buy analysis, model and vendor selection, governance and risk review, and a prioritized roadmap your team can execute.

• Technical Due Diligence

  Independent, senior-level review of architectures, codebases, and AI vendor claims before you commit budget to them.

• Agile Coaching & Team Training

  Highly experienced in Extreme Programming and Scrum. Hands-on team training, from sprint mechanics to test-driven, evaluation-driven AI development, proven with enterprise teams from startups to Fortune 100s.