Cookies are small text files that allow us to create the best browsing experience for you on our site. By continuing to use this website or closing this message without disabling cookies, you are agreeing to our use of cookies. To understand how we use cookies or for information on how to manage cookies, please see our cookies policy.
AI and machine learning are taking industries by storm, arming them with advanced predictive and state-of-the-art decision-making capabilities. Machine learning SDKs, frameworks, tools, and APIs are available today that make it easier to adopt these next-gen technologies. Yet a yawning gap in skill sets is frustrating the process for many, leading to wastage of resources. Our machine learning expertise and experience fill that shortage, accelerating the business value of investments in innovative technologies.
Deep Learning
Recognition of speech, sounds, and images comes naturally to the human brain. Machines can replicate the neural system of the brain with the aid of massive computational power and advanced algorithms. Multi-layered artificial neural networks can learn patterns from millions of images and sound samples they are trained on. Deep learning enables machines to understand spoken words in real time, recognize and describe images, play games, and even diagnose diseases. In some tasks, they even outperform humans. We work on deep learning frameworks to build business applications that can supplement human faculties.
Natural Language Processing (NLP)
Think intelligent chatbots that can reply to customer queries with precision; text analytics applications that can identify customer sentiments from thousands of emails, social media conversations, and surveys. The capability of machines to understand human language is so advanced today that it is possible to create a wide range of applications with text and voice processing capabilities. With our strong machine learning skills plus expertise in classification algorithms, entity extraction, sentiment analysis, topic modeling as well as Python and Java, we bring to life self-learning NLP applications focused on improving process efficiency, product experience, and customer engagement.
Cognitive Chatbots
Sentiment Analysis
Document Labeling
Information Extraction
Product Recommendation
Fraud Detection
Medical Text Annotation
Text-to-Speech
Translation Systems
Computer Vision
Gauge customer attributes and sentiments through real-time analysis of images. Optimize application/claim processing combining optical character Recognition (OCR) and robotic process automation (RPA). Add image description features to support low vision users. Image recognition and classification, once a computationally complex feat, is now an effortless task for machines thanks to revolutionary advances in computer vision. The applications powered by computer vision are now within your reach too. Automatic image processing, face detection/authentication, emotion analysis, text extraction, image captioning...these leading use cases of computer vision fall right within our expertise.
Object Detection and Tagging
Image Captioning
Face Detection/Recognition
Optical Character Recognition
Vision-Based Measurement
Vision-Based Quality Control
Video Analytics
Get a variety of customer insights, including in/out traffic, average visit time, dwell time, dwell areas, and customer demographics for real-time store/staff optimization. Protect people and high-value assets using AI-driven video surveillance that combines object and facial recognition and specific machine learning algorithms. These solutions can trigger alerts in real time on detecting unusual or suspicious behaviors in prohibited areas of hospitals, residential buildings, industrial complexes, etc. We also support our clients in other areas of video analytics application such as motion detection, queue/crowd management, monitoring traffic jams and parking violations, and attendance tracking.
Gain insights into the future using predictive analytics. Analysis of vast swathes of historical and real-time data can reveal patterns and relationships, from which future trends and behaviors can be accurately predicted. Through predictive modeling, our data scientists can help you determine risk scores and predict purchasing decisions and a range of other outcomes, minimizing the element of risk in critical decisions you take. Protect critical assets and reduce unplanned downtime by accurately predicting time to failure and undertaking preventive maintenance. Integrated with IoT, predictive analytics can be a game-changer for asset-intensive industries.
Anomaly Detection
Price Optimization
Demand Forecasting
Cross-Selling and Upselling
Predictive Maintenance
Targeted Marketing
Deep Learning
Recognition of speech, sounds, and images comes naturally to the human brain. Machines can replicate the neural system of the brain with the aid of massive computational power and advanced algorithms. Multi-layered artificial neural networks can learn patterns from millions of images and sound samples they are trained on. Deep learning enables machines to understand spoken words in real time, recognize and describe images, play games, and even diagnose diseases. In some tasks, they even outperform humans. We work on deep learning frameworks to build business applications that can supplement human faculties.
Natural Language Processing (NLP)
Think intelligent chatbots that can reply to customer queries with precision; text analytics applications that can identify customer sentiments from thousands of emails, social media conversations, and surveys. The capability of machines to understand human language is so advanced today that it is possible to create a wide range of applications with text and voice processing capabilities. With our strong machine learning skills plus expertise in classification algorithms, entity extraction, sentiment analysis, topic modeling as well as Python and Java, we bring to life self-learning NLP applications focused on improving process efficiency, product experience, and customer engagement.
Cognitive Chatbots
Sentiment Analysis
Document Labeling
Information Extraction
Product Recommendation
Fraud Detection
Medical Text Annotation
Text-to-Speech
Translation Systems
Computer Vision
Gauge customer attributes and sentiments through real-time analysis of images. Optimize application/claim processing combining optical character Recognition (OCR) and robotic process automation (RPA). Add image description features to support low vision users. Image recognition and classification, once a computationally complex feat, is now an effortless task for machines thanks to revolutionary advances in computer vision. The applications powered by computer vision are now within your reach too. Automatic image processing, face detection/authentication, emotion analysis, text extraction, image captioning...these leading use cases of computer vision fall right within our expertise.
Object Detection and Tagging
Image Captioning
Face Detection/Recognition
Optical Character Recognition
Vision-Based Measurement
Vision-Based Quality Control
Video Analytics
Get a variety of customer insights, including in/out traffic, average visit time, dwell time, dwell areas, and customer demographics for real-time store/staff optimization. Protect people and high-value assets using AI-driven video surveillance that combines object and facial recognition and specific machine learning algorithms. These solutions can trigger alerts in real time on detecting unusual or suspicious behaviors in prohibited areas of hospitals, residential buildings, industrial complexes, etc. We also support our clients in other areas of video analytics application such as motion detection, queue/crowd management, monitoring traffic jams and parking violations, and attendance tracking.
Gain insights into the future using predictive analytics. Analysis of vast swathes of historical and real-time data can reveal patterns and relationships, from which future trends and behaviors can be accurately predicted. Through predictive modeling, our data scientists can help you determine risk scores and predict purchasing decisions and a range of other outcomes, minimizing the element of risk in critical decisions you take. Protect critical assets and reduce unplanned downtime by accurately predicting time to failure and undertaking preventive maintenance. Integrated with IoT, predictive analytics can be a game-changer for asset-intensive industries.
Our machine learning services cover everything from the ground up—from developing a strategy, building, training, and deploying data models to architecting and developing domain-specific artificial intelligence solutions. Our data science team collects, preprocesses, and transforms data to create viable models that help you accurately predict outcomes. Skillful feature engineering ensures that both the data and the algorithms used are high performing and easy to maintain. Our data engineers deploy the models in production, taking care of all the integration requirements. Once deployed, the models are monitored over time. New features are added or the models are recreated to achieve better performance. As your business requirements change, the models are also retrained to explore emerging opportunities.
Machine Learning Expertise
Our expertise spans the entire machine learning ecosystem
Break the barriers to entry with custom-built machine learning solutions
Product Recommendation
Content-based and collaborative filtering algorithms can be used to generate user-specific recommendations. These recommendations may include a set of similar items based on the common features of products chosen by users as well as items preferred by similar users.
Sentiment Analysis
Gauging sentiments of people from voters to customers has become vital for campaigns in fields as diverse as politics and retail. Deploying natural language processing, sentiments can be mined to help build more responsive campaigns and modify brand positioning.
Targeted Marketing
Customer segmentation using clustering can reveal the different categories that make up the customer base of a business, including the high-value ones. Each segment can be targeted with the right campaigns and products to improve customer acquisition and retention.
Energy Demand Forecasting
Machine learning forecasting systems can predict future energy demand using past energy consumption data and weather parameters. Hybrid prediction models combining SARIMA models and machine learning techniques are also evolving. Power companies can optimize schedules and thus reduce costs and energy wastage.
Predictive Maintenance
Continuous monitoring of machines in geographically dispersed locations is no longer difficult. Detection algorithms can identify machine deterioration by analyzing real-time machine parameters against historical data. Operators can initiate predictive maintenance, preventing irreversible damage to assets.
Automated Document Processing
Applications that combine Optical Character Recognition, Intelligent Character Recognition, and RPA can speed up document-driven processes such as invoice processing, (supply chain management); claims handling, mortgage processing (insurance); and customer onboarding, loan processing (financial services).
Fraud Detection
Models built on known cases of legitimate and fraudulent transactions can assign suspicion scores for new transactions and identify credit card fraud. A host of algorithms including decision trees, neural networks, regression, SVM, and k-means clustering are used for this. Predictive modeling is also used for insurance fraud detection.
Insurance Underwriting
Predictive models based on diverse datasets (demographic and other traditional insurance data plus medical and social media data) can help underwriters determine individual risk more accurately and calculate optimum pricing. Predictive analytics can also reveal the right candidates for cross-selling.
Customer Engagement
Voice assistants and integrated NLP-powered cognitive chatbots can accurately decode human language (both voice and text) and interact intelligently and in real time with customers. Today's deep learning-based text-to-speech systems can also provide natural human-like voices.
Health Informatics
Knowledge created by medical research is more than what practitioners can cope up with. An intelligent system that incorporates NLP with semantic knowledge processing and machine learning can help practitioners look up research literature on specific problems.
Annotation of Medical Records
Although electronic health records are a rich source of patient data, they do not lend themselves to analysis as they are highly unstructured. Using NLP, entities such as symptoms, diseases, and treatments can be parsed and tagged making them easily retrievable at the time of clinical decision-making.
Medical Image Analysis
Supervised machine learning techniques are deployed in medical image analysis for computer-assisted diagnosis of certain brain disorders. Models trained on large datasets of labeled images (such as CT and MRI scans) can automatically detect indicators of disease and help doctors make a prognosis.
Product Recommendation
Content-based and collaborative filtering algorithms can be used to generate user-specific recommendations. These recommendations may include a set of similar items based on the common features of products chosen by users as well as items preferred by similar users.
Sentiment Analysis
Gauging sentiments of people from voters to customers has become vital for campaigns in fields as diverse as politics and retail. Deploying natural language processing, sentiments can be mined to help build more responsive campaigns and modify brand positioning.
Targeted Marketing
Customer segmentation using clustering can reveal the different categories that make up the customer base of a business, including the high-value ones. Each segment can be targeted with the right campaigns and products to improve customer acquisition and retention.
Energy Demand Forecasting
Machine learning forecasting systems can predict future energy demand using past energy consumption data and weather parameters. Hybrid prediction models combining SARIMA models and machine learning techniques are also evolving. Power companies can optimize schedules and thus reduce costs and energy wastage.
Predictive Maintenance
Continuous monitoring of machines in geographically dispersed locations is no longer difficult. Detection algorithms can identify machine deterioration by analyzing real-time machine parameters against historical data. Operators can initiate predictive maintenance, preventing irreversible damage to assets.
Automated Document Processing
Applications that combine Optical Character Recognition, Intelligent Character Recognition, and RPA can speed up document-driven processes such as invoice processing, (supply chain management); claims handling, mortgage processing (insurance); and customer onboarding, loan processing (financial services).
Fraud Detection
Models built on known cases of legitimate and fraudulent transactions can assign suspicion scores for new transactions and identify credit card fraud. A host of algorithms including decision trees, neural networks, regression, SVM, and k-means clustering are used for this. Predictive modeling is also used for insurance fraud detection.
Insurance Underwriting
Predictive models based on diverse datasets (demographic and other traditional insurance data plus medical and social media data) can help underwriters determine individual risk more accurately and calculate optimum pricing. Predictive analytics can also reveal the right candidates for cross-selling.
Customer Engagement
Voice assistants and integrated NLP-powered cognitive chatbots can accurately decode human language (both voice and text) and interact intelligently and in real time with customers. Today's deep learning-based text-to-speech systems can also provide natural human-like voices.
Health Informatics
Knowledge created by medical research is more than what practitioners can cope up with. An intelligent system that incorporates NLP with semantic knowledge processing and machine learning can help practitioners look up research literature on specific problems.
Annotation of Medical Records
Although electronic health records are a rich source of patient data, they do not lend themselves to analysis as they are highly unstructured. Using NLP, entities such as symptoms, diseases, and treatments can be parsed and tagged making them easily retrievable at the time of clinical decision-making.
Medical Image Analysis
Supervised machine learning techniques are deployed in medical image analysis for computer-assisted diagnosis of certain brain disorders. Models trained on large datasets of labeled images (such as CT and MRI scans) can automatically detect indicators of disease and help doctors make a prognosis.
Avail our machine learning expertise and accelerate business value.