Advanced Analytics Services
At SGA, we are committed to providing advanced analytics solutions where we take our analytical collaboration to the next level.
Extract Valuable Insights
With Advanced Analytics Solutions
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.
Drive Business Growth With
Advanced Analytics Consulting Company
Our advanced analytics solutions help us to maximize our client’s ability to make data-driven decisions by building advanced ML models using relevant data in different contexts per our clients’ domains like BFSI, Media and Entertainment, Technology, and Manufacturing.
Advanced Analytics Services
Predictive Analytics
- 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.
Applied Data Science
- 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.
Computer Vision
- 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.
Risk Analytics
- 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
Who We Work With
Chief Analytics Officer and Chief Data Science officer
We help drive data-driven decisions through the extensive use of analytics
Our Ins(AI)ghts
Whitepaper
Leveraging AI and Machine Learning for Data Quality Management
As organizations embrace digital transformation, the volume and value of data are growing exponentially. At SG Analytics, we help enterprises elevate their data quality management (DQM) practices by leveraging artificial intelligence (AI) and machine learning (ML). Our solutions automate data cleansing, improve accuracy, and ensure consistency—enabling faster, more reliable decision-making. By embedding intelligence into DQM, businesses can reduce risk, optimize operations, and unlock the full potential of their data assets for sustained growth and competitive advantage.
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Automotive Industry Trends 2025: Future & Growth of Automotive
The automotive industry is undergoing a dynamic shift in 2025, driven by electrification, software-defined vehicles, and autonomous technologies. Key trends include the rise of hybrid and electric vehicles, integration of advanced powertrains, and growing adoption of AI and VR in customer experiences. Automakers are redefining mobility with smarter, more connected, and sustainable solutions. As consumer expectations evolve, the industry is poised for a future shaped by innovation, digital transformation, and green technologies.
