AI / Vision
Computer vision, machine learning, and AI integration — from real-time image processing to production inference pipelines.
PyTorch
Our primary deep learning framework. Used for object segmentation, pose prediction, and training custom vision models in production.
TensorFlow
Deep learning for image classification, object detection, and deploying models via TensorFlow Serving and TFLite.
OpenCV
Image processing, feature detection, and real-time video analysis. The foundation of our custom vision pipelines.
Hugging Face
Pre-trained transformer models for NLP, image segmentation, and domain-specific fine-tuning on custom datasets.
ONNX
Cross-platform model deployment — train in PyTorch, run optimized inference anywhere in production.
OpenAI
GPT and multimodal APIs for natural language features, document understanding, and intelligent automation.
scikit-learn
Classical ML for tabular data, feature engineering, and lightweight predictive models that don't need a GPU.
MLOps
End-to-end model lifecycle management — automated training, artifact collection, versioning, and deployment monitoring.
Frontend
Modern web interfaces built for speed, accessibility, and real-time data — from static marketing sites to complex annotation platforms.
React
Component-driven UI for interactive dashboards, annotation tools, and complex client applications.
TypeScript
Type safety across the entire stack — fewer runtime bugs, better DX, easier refactoring.
Next.js
Full-stack React framework for SSR, API routes, and applications that need SEO and performance.
Vue.js
Lightweight reactive framework — great for embedding interactive features into existing applications.
Astro
Zero-JS-by-default static sites with island architecture. What this site is built on.
Tailwind
Utility-first CSS for rapid, consistent styling without fighting specificity.
WebRTC
Real-time video streaming for live camera feeds, pose prediction, and peer-to-peer communication.
Backend
APIs, services, and server-side logic — built for reliability, throughput, and clean integration with AI workloads.
Python
The backbone of our AI/ML work — data pipelines, model serving, image processing, and automation.
Node.js
Event-driven runtime for APIs, real-time services, and serverless functions. Fast iteration, massive ecosystem.
C#/.NET
Enterprise-grade backend for high-throughput APIs, WPF desktop apps, and legacy system modernization.
ASP.NET
Web APIs and MVC applications for enterprise environments with role-based access control.
FastAPI
High-performance Python API framework with automatic OpenAPI docs — ideal for ML model serving.
Express
Minimal Node.js server for REST APIs, webhooks, and lightweight microservices.
Data
Persistent storage, caching, and analytics — choosing the right database for the workload, not the hype cycle.
PostgreSQL
Our default relational database. JSONB support, full-text search, and rock-solid reliability.
MySQL
Battle-tested relational DB used in production ML platforms and web applications.
SQL Server
Enterprise data platform for .NET ecosystems, reporting, and legacy system integration.
Redis
In-memory cache and message broker — session storage, rate limiting, real-time leaderboards.
MongoDB
Document store for unstructured data, rapid prototyping, and content-heavy applications.
ETL Pipelines
Custom data ingestion and transformation workflows — cleaning, normalizing, and loading data for analytics and ML training.
Tools & Infra
Deployment, CI/CD, and development infrastructure — automated, reproducible, and production-ready from day one.
Docker
Containerized builds for consistent dev/prod environments and easy deployment of ML models.
AWS
EC2, Lambda, S3, SageMaker — full cloud infrastructure for compute, storage, and model hosting.
Cloudflare
Edge hosting, Workers, Pages, and DNS — fast global delivery with built-in security.
Vercel
Zero-config frontend deployments with edge functions, preview branches, and instant rollbacks.
GitHub Actions
CI/CD pipelines for automated testing, linting, building, and deployment on every push.
Linux
Production server environment. Comfortable with Ubuntu, Debian, Alpine, and custom AMIs.
Jupyter
Interactive notebooks for data exploration, model experimentation, and client-facing analysis reports.
Git
Version control with clean branching strategies, code review workflows, and automated releases.
Project-based engagements and part-time retainers — no full-time contracts.