AI / ML Capabilities
We develop intelligent solutions that leverage the power of Artificial Intelligence and Machine Learning to drive automation, uncover insights, and create transformative user experiences. Our expertise spans the full AI/ML lifecycle, from data strategy to production deployment.
Generative AI
We build proof-of-concept and production-grade generative AI solutions—such as content synthesis, code generation assistants, or data augmentation pipelines—using state-of-the-art models. By integrating with APIs or custom fine-tuned models, we help businesses automate creative tasks, personalize user experiences, and accelerate ideation cycles.
NLP (Natural Language Processing)
From text classification and sentiment analysis to chatbots and document understanding, our NLP capabilities leverage transformer-based models and open-source libraries. We preprocess corpora, fine-tune models for domain-specific language, and deploy scalable inference services that power search, recommendation, or conversational interfaces.
Statistical Modelling
Grounded in robust statistical techniques, we develop models for forecasting, trend analysis, and hypothesis testing. By combining domain expertise with time-series analysis or econometric methods, we provide data-driven insights that inform strategic decisions and detect patterns in business metrics.
Deep Learning (DNN, CNN, LSTM, GCN)
Using deep neural networks—whether convolutional architectures for image or signal processing, recurrent/LSTM for sequence data, or graph convolutional networks for relational insights—we tackle complex tasks such as computer vision, anomaly detection, or network analysis. Our pipelines include data preprocessing, model training, hyperparameter tuning, and production deployment with monitoring for drift.
Computer Vision
We implement image and video analytics solutions—object detection, segmentation, OCR, or visual quality inspection—using modern frameworks. By combining pre-trained models with custom datasets, we enable automated visual workflows (e.g., defect detection, scene recognition) that boost accuracy and speed in operational contexts.
sklearn, PyTorch, TensorFlow, Keras, Pandas
Our data science toolkit spans scikit-learn for classic ML tasks, PyTorch/TensorFlow/Keras for deep learning, and Pandas for data manipulation. This versatile stack allows us to prototype quickly, iterate experiments, and productionize models with reliable pipelines that integrate into broader applications.
Docker
Containerizing ML services ensures consistency between development and production environments. We package models and dependencies into Docker images, enabling scalable deployment on Kubernetes or serverless platforms and simplifying reproducibility for experiments and updates.
Flask
Lightweight Flask-based APIs allow rapid wrapping of ML models into inference endpoints. This enables quick integration of predictive features into web or mobile applications, with options to scale via container orchestration or serverless functions as usage grows.
Git / PyCharm
Version control of model code, data preprocessing scripts, and experiments via Git ensures reproducibility and collaboration. Using IDEs like PyCharm, our engineers efficiently develop, debug, and refactor complex pipelines. Combined with CI practices, this fosters reliable delivery of AI/ML components into production.