PyTorch
Research-grade flexibility and production-grade performance. Our primary training framework for everything from lightweight classifiers to large-scale transformer pre-training across multi-GPU clusters.
Hugging Face
Transformers, diffusers, and the full NLP/GenAI ecosystem in one trusted hub. We fine-tune, evaluate, and serve foundation models using the Hugging Face stack — from BERT to Llama to Stable Diffusion.
Ray & Spark
Distributed compute for large-scale training and real-time feature pipelines. Ray handles heterogeneous ML workloads with actor-based parallelism while Spark powers our batch feature engineering at petabyte scale.
MLflow / W&B
Experiment tracking and model registry for fully reproducible ML. Every run is logged — parameters, metrics, artefacts — so any result can be reproduced, audited, or handed off without ambiguity.
Kubernetes
Container orchestration for portable, scalable AI deployment anywhere. We run GPU-accelerated inference workloads on K8s with auto-scaling, rolling updates, and zero-downtime model swaps in production.
AWS/GCP/Azure
Multi-cloud strategies leveraging the best managed ML services across providers. We design cloud-agnostic architectures so clients are never locked in — and can shift workloads as costs or requirements evolve.
LangChain
Orchestration framework for building robust LLM applications and RAG pipelines. Combined with LangGraph and LangSmith, it gives us the tooling to build, trace, and evaluate complex multi-step AI workflows reliably.
Vector DBs
Pinecone, Weaviate, and pgvector for high-performance semantic search and retrieval. The backbone of every RAG system we build — enabling sub-50ms similarity search across millions of embedded documents.