AI & machine learning
Generative AI, deep learning, and classical ML — built for real products
OpenAI GPT powers our generative AI work — copilots, conversational agents, document Q&A, summarization, and multimodal understanding. We pair GPT with retrieval grounding, tool calling, structured outputs, and evaluation so responses stay accurate, attributable, and safe enough for customer-facing products.
TensorFlow is one of the frameworks we use for deep learning — classification, forecasting, recommendation, and computer vision pipelines. From prototype models to production serving graphs, it helps us train, evaluate, and deploy neural networks that sit behind product features your users feel every day.
PyTorch is our go-to for flexible model research and custom neural architectures. We use it for fine-tuning, embedding models, ranking systems, and experiment-heavy ML work where rapid iteration matters — then harden the winning approach into a stable inference path for production.
Hugging Face gives us access to a wide ecosystem of transformers, embeddings, and open models. We use it to prototype NLP and GenAI features quickly, compare model quality, and host or fine-tune models when open weights fit better than a closed API for cost, privacy, or domain control.
LangChain helps us compose retrieval-augmented generation, agents, and multi-step LLM workflows. We use it to connect models to your documents, tools, and APIs — with memory, routing, and observability — so GenAI features can complete real tasks instead of only answering one-off prompts.
Scikit-learn covers classical machine learning when deep learning is not the right hammer — churn scoring, lead ranking, anomaly detection, clustering, and feature pipelines. We use it for fast, interpretable models that deliver value from structured business data without unnecessary complexity.
