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Advanced Analytics Services

At SGA, we are committed to providing advanced analytics solutions where we take our analytical collaboration to the next level.

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Leverage Advanced Analytics Solutions to Extract Valuable Insights

SGA's analytics team brings the data operations team data science and ML experts who are proficient in big data and ML tools and frameworks, including Python, R, TensorFlow, Keras, Pytorch, Databricks, Spark, Azure Machine Learning, and Amazon Sagemaker. We develop OCR- and NLP-based ML pipelines using our advanced analytics services to extract valuable insights from unstructured data across core data sources, such as financial data, reports, earnings summaries, and social media platforms.    

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Advanced analytics consulting Services

What We Offer

  • Classification and regression models using explainable and implementable advanced ML models like XgBoost, LightGBM, and decision trees.
  • NLP tasks such as text classification (multi-label), translation, and topic modeling. 
  • Training state-of-the-art models (BERT-based models, such as Distil Bert and Roberta, and GPT-based models like Latent Dirichlet Allocation) on cloud/on-premises environments, utilizing libraries such as NLTK, Gensim, Spacy, and TensorFlow. 
  • Recommendation systems such as content-based filtering, collaborative filtering, and hybrid algorithms.
  • Time series analysis and forecasting using ARIMA, LSTM, TFT, DeepAR, and other suitable techniques. 
  • Making use of Churn Attrition models to identify the risk of attrition accurately based on past data and profiling them into micro-segments to run promotional campaigns accurately to improve customer retention. 
  • Computer vision tasks, including image classification, object detection, and object tracking. 
  • Training state-of-the-art models (YOLOv5, Resnet50, VGG-16, and SORT) utilizing OpenCV, PyTorch, Keras, and TensorFlow.
  • Model deployment on edge devices/cloud/on-premises servers, involving environment setup, containerization, latency testing, multiprocessing, and model optimization. 
  • Model lifecycle management involving experiments tracking, monitoring (KPI drifts), and managing API endpoints on cloud/on-premises environments using MLOps tools (MLFlow, TensorFlow serve, and Kubernetes). 
  • Performing clustering analysis using density-based clustering and hierarchical clustering, with appropriate distance measures. 
  • Network analysis with Markov chains and BFS/A* search techniques.
  • Market survey designing using fractional factorial design and analyzing results of choice-based conjoint/max different surveys using hierarchical Bayesian models to determine individual and group utilities of the options. 
  • We develop credit lifecycle models (application behavior and collection) using explainable and robust ML algorithms like XgBoost and LightGBM. 
  • We design intelligent features using Bureau and other alternative data sources. We bring decades of credit risk management expertise across product lifecycles and geographies. 
  • We help reduce the model development and deployment lifecycle to 8–12 weeks. 

Why SGA

Capitalizing on our Expertise

Our predictive analytics solutions enable us to assist our clients in making the right decisions as well as improving their profitability and market share.

Driving Business Objectives

Leverage data science solutions to improve our customer experience, enabling us to deliver the best business results.

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Who We Work With

Insights

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CASE STUDIES

Transforming Investment Valuation: Leveraging Advanced AI and Generative AI Solutions for Improved Efficiency and Accuracy

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