Areas of Competence
We combine a wide range of technologies and techniques such as machine learning, deep learning, natural language processing, computer vision, and robotics. These technologies enable machines to mimic human-like behavior and perform tasks autonomously.
AI finds applications in various domains such as virtual assistants, autonomous vehicles, healthcare diagnostics, recommendation systems, and gaming, among others. While AI often relies on data for training models and making decisions, its primary focus is on creating intelligent systems that can reason, learn, and act autonomously. PITS had built several solutions on top of existing and proven AI models.
We have been involved in POCs for the following industries:
Dental
Analyzed dental images, to assist dentists in diagnosing conditions like cavities, periodontal disease, or abnormalities in tooth alignment. We also developed virtual assistants that can provide patients with information about dental procedures, appointment scheduling, and post-treatment care, improving communication and patient satisfaction.
Insurance
Our AI-powered tools automated the claims processing workflow by analyzing claim documents, images, and other data to verify claim validity, estimate repair costs, and expedite claim settlements.
Garbage Collection
We created algorithms, that can optimize garbage collection routes based on factors such as location, traffic conditions, and waste volume, reducing fuel consumption, minimizing vehicle emissions, and improving operational efficiency.
Medicine
AI-powered systems leverage computer vision and machine learning techniques to identify and treat orthopedic problems in children. This is a very valuable tool especially in underdeveloped nations where real time access to medical experts are limited.
Our team of data science experts use statistical analysis, data mining, machine learning, and visualization techniques to understand complex datasets, identify trends, and make predictions or recommendations based on data patterns.
Some of the use cases we have developed are,
Fraud Detection
We can help companies identify fraudulent transactions by processing and cleaning large amounts of transactional data and building Machine learning models to uncover patterns of suspicious behaviour.
Customer Segmentation
We use data processing and cleaning techniques to group customers based on their demographic and behavioral characteristics, creating knowledge graphs to understand the relationships between different segments and tailor marketing campaigns accordingly.
Supply Chain Optimization
We have the skills in data processing and cleaning techniques to analyze supply chain data and create knowledge graphs that identify bottlenecks, inefficiencies, and opportunities for optimization.
Sentiment Analysis
PITS team use natural language processing techniques to clean and analyze large volumes of customer feedback data, creating ML models that identify the sentiment of customers towards different products, services, or brands.
Image Recognition
Our team use machine learning algorithms to process and clean image data and build Deep Learning models using tensorflow or pytorch, that help identify objects, people, and patterns within images.
Social Network Analysis
We have experience in processing and cleaning social media data, creating ML models that show the connections between different users, their interests, and the topics they discuss, helping companies understand their target audience better.
Predictive Maintenance
We use data processing and cleaning techniques to analyze sensor data from equipment and build ML models that identify patterns of failure, allowing companies to predict maintenance needs and minimize downtime.
Data Structuring
By collecting Unstructured data from websites and cleaning by implementing various NLP methods, the data could be transformed into structured form. These structured data could be stored and interlinked to each other using knowledge graphs which helps the business to gain insights.
