Technology Stack.

Our core UI library for building component-driven interfaces. React's composable architecture lets us deliver complex, interactive experiences while keeping codebases maintainable across teams.

The production framework we pair with React for every web project. Server-side rendering, static generation, and edge middleware give us fine-grained control over performance and SEO.

Our go-to for cross-platform mobile development. A single codebase targeting iOS and Android lets us ship native-quality mobile apps at roughly half the cost of maintaining two native teams.

The managed toolchain we layer on top of React Native for faster iteration. Over-the-air updates and pre-built native modules dramatically shorten the feedback loop from commit to user device.

Every line of production code we write is TypeScript. Static typing catches entire categories of bugs at compile time and makes large codebases navigable for any engineer on the team.

Our default styling system for rapid, consistent UI development. Utility-first classes eliminate naming debates, reduce CSS bloat, and make design system tokens trivially enforceable.

Our primary runtime for API services and real-time backends. The non-blocking event loop handles high-concurrency workloads efficiently, and sharing TypeScript across the full stack reduces context switching.

The backbone of our data engineering and ML pipelines. Python's ecosystem for scientific computing and AI is unmatched, making it the natural choice when a project involves model training or data processing.

A minimal, battle-tested HTTP framework we reach for when we need a lightweight API layer without the overhead of a full application framework. Its middleware pattern keeps request handling composable and testable.

Our preferred Python framework for high-performance API services. Async support and automatic OpenAPI documentation generation make it ideal for ML model serving and data-intensive endpoints.

The enterprise-grade Node.js framework we use when projects demand strict architectural patterns. Dependency injection, decorators, and modular organization bring Angular-like structure to the server side.

Our default relational database for transactional workloads. Advanced features like JSONB columns, full-text search, and row-level security let us handle complex queries without bolting on extra services.

The document database we choose when schemas are evolving rapidly or data is inherently hierarchical. Flexible document models accelerate early-stage development without sacrificing query power.

Our in-memory data store for caching, session management, and real-time features. Sub-millisecond reads make it indispensable for rate limiting, leaderboards, and any hot-path data access.

AWS's managed NoSQL service that we deploy for workloads requiring single-digit millisecond latency at any scale. Its pay-per-request model keeps costs proportional to actual usage.

A purpose-built vector database we integrate for semantic search and AI-powered retrieval. It handles the heavy lifting of nearest-neighbor lookups so our RAG pipelines stay fast and accurate.

Our primary cloud platform for production deployments. Deep expertise across Lambda, ECS, S3, and the broader ecosystem lets us architect solutions that balance cost, performance, and operational simplicity.

Google Cloud is our choice when projects lean heavily on BigQuery analytics, Vertex AI, or tight integration with the Google ecosystem. Its networking and ML infrastructure are best-in-class.

We deploy on Azure when clients have existing Microsoft enterprise agreements or require Azure AD integration. Its hybrid cloud capabilities serve organizations transitioning from on-prem infrastructure.

Containerization is a non-negotiable part of our delivery pipeline. Docker ensures that every service runs identically from a developer's laptop through staging to production, eliminating environment drift.

The orchestration layer we use for complex, multi-service deployments. Auto-scaling, rolling updates, and self-healing give operations teams confidence in managing large distributed systems.

Infrastructure-as-code is how we manage every cloud resource. Terraform's declarative syntax and state management let us version, review, and reproduce entire environments with a single apply.

We build on OpenAI's GPT models for natural language processing, content generation, and intelligent automation. Fine-tuning and function calling let us tailor model behavior precisely to each client's domain.

Claude is our preferred model for tasks demanding nuanced reasoning, long-context analysis, and safety-conscious outputs. Its extended context window makes it exceptional for code review and document synthesis.

The orchestration framework we use to build multi-step AI pipelines. Chains, agents, and retrieval components let us compose sophisticated workflows that go far beyond single-prompt interactions.

Our framework for custom model training and fine-tuning when off-the-shelf APIs are not sufficient. PyTorch's dynamic computation graph makes experimentation fast and debugging straightforward.

We leverage TensorFlow for production ML deployments that benefit from its mature serving infrastructure and TFLite for on-device inference in mobile and edge computing scenarios.

Our CI/CD platform of choice, tightly integrated with every repository we manage. Custom workflows handle linting, testing, building, and deploying on every push without leaving the GitHub ecosystem.

The unified observability platform we use to monitor infrastructure, APM traces, and logs in one place. Custom dashboards and alerting give teams real-time visibility into system health.

Our error tracking and performance monitoring tool for both frontend and backend services. Source map integration and release tracking let us pinpoint regressions down to the exact commit.

The experiment tracking and model registry we use to bring discipline to ML development. Logging parameters, metrics, and artifacts ensures every model in production is reproducible and auditable.