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AI-Powered Solutions


Conversational and Human Interaction

We employ AI, speech recognition and natural language processing to develop intelligent machines that can process and communicate language. This technology is being used by businesses to gain a competitive advantage and enhance customer service.


Chatbots are text-based conversational assistants that communicate with users through various channels such as web platforms, social networks and mobile apps. They examine human language and are capable of learning on their own. Chatbots for customer service can assist businesses in engaging customers by answering frequently asked questions and adding context to conversations. They can also be integrated into mobile apps, adding value to your business and users. AI-powered chatbots aid in increasing the productivity of the company internal workforce.

Voice Assistants

A voice assistant is an AI-powered software program that communicates with the user verbally and responds to spoken instructions. Today, people find what they are looking for online using voice assistants like Alexa or by instructing a search engine's voice function, such as Google voice search. Users can get personalized recommendations and offers from voice assistants, making them the main focus of many marketers today. Upselling and cross-selling are two marketing strategies that can be used more intuitively through voice.

Accessibility Assistants

Accessibility Assistants improve day-to-day activities and improve life quality for disabled users. They use a variety of solutions such as Image Recognition for visually impaired persons, Lip-Reading Recognition for people with hearing impairments, Text Summarization for individuals with mental impairments, Real-time subtitles or interpretations for people with hearing impairments, or even people who do not speak the language.



We leverage Computer Vision technology including facial recognition, body measurement and object recognition to solve complex business problems. The ever-expanding list of problems being tackled include security, safety, healthcare and fraud prevention.

Facial Recognition

Facial recognition is a sophisticated object detection technology that can not only recognize a human face in an image but also pinpoint a specific individual. The most common application of facial recognition for security is to protect smartphones. Advanced or sophisticated forms of facial recognition include security systems in residential or commercial buildings that verify an individual's identity using unique physiological features.

Object Recognition

Object recognition is the process of identifying objects in an image or a video feed. The bounding boxes are being defined and the object gets labeled appropriately. Businesses employ this technology for traffic control, self-driving cars navigation, public security, manufacturing quality control, and many other areas.

Body Measurements

AI tools can take a person's body measurements and convert them into highly accurate size recommendations, providing extremely valuable sizing data to your value chains.

Gesture Detection

Gesture detection is a Human-Machine Interaction (HMI) technology that uses mathematical algorithms to interpret human gestures. This perceptual user interface (PUI) component allows computers to capture and make sense of human gestures as commands. Today, gesture detection is being employed for a variety of purposes, including interpreting sign language.

Hand-Written Text Recognition

In Hand-Written Text Recognition, the device converts the user's handwritten words or characters into a computer-readable format (e.g., Unicode text). Handwriting recognition aids in the conversion of handwritten text into a text document format, also known as a readable electronic format. By minimizing paperwork, it allows businesses to save a significant amount of time and money.

Synthetic Image and Data Generation

In the data-hungry world of deep learning, synthetic data sets—computer-generated specimens with the same statistical properties as the real thing—are becoming increasingly common. These fabrications can be used to train AIs in situations where real data is limited or too sensitive to use, such as medical records or personal financial information. Saving money is only the beginning. Synthetic data is critical in dealing with privacy concerns and minimizing bias by guaranteeing data diversity to accurately reflect the real world. Since synthetic datasets are automatically classified and can include rare but critical corner cases, they are sometimes superior to real-world data.

Intelligent Public Data Search

Artificial intelligence-powered intelligent search breaks down data silos, allowing employees and customers to find the information they need more easily and quickly. End-users can use intelligent search to obtain data from any destination (within or outside the organization) and any data set, irrespective of format: on paper, big data in databases, webpages, digital content, document management systems, and anywhere else.


Predictive Analytics and Decision Support

We apply machine learning algorithms to optimize and uncover new statistical patterns to create intelligent predictive models.


The convergence of AI and Big Data not only improves how businesses operate but also improves their forecasting capabilities. Forecasting, a fundamental aspect of management, aids in estimating future events. Forecasts are created in organizations to help with planning and decision-making. Both of these processes are aided by AI, which organizes and provides relevant data and insights to businesses using either Neural Nets, Expert Systems, or Belief Networks.

Behavior Prediction

AI-based predictive analytics helps to analyze data based on current and historical scenarios to uncover patterns that can be used to predict future events more accurately. One of the most significant benefits of predictive analytics is accurately predicting customer behavior. Organizations can better understand their customers by using predictive analytics enabled by ML algorithms, which will allow them to improve their marketing campaigns and make better customer-related decisions.

Predictive Maintenance

Predictive maintenance is a method of detecting anomalies in your operation and potential faults in processes and equipment using data analysis tools and techniques, so you can fix them before they fail. Predictive maintenance seeks to maximize the efficiency of your maintenance resources. Maintenance personnel can plan maintenance work only when it is truly needed by knowing when a specific part is likely to fail. This helps avoid unnecessary maintenance while also preventing unforeseen equipment breakdowns.

Intelligent Process Automation

Predictive maintenance is a method of detecting anomalies in your operation and potential faults in processes and equipment using data analysis tools and techniques, so you can fix them before they fail. Predictive maintenance seeks to maximize the efficiency of your maintenance resources. Maintenance personnel can plan maintenance work only when it is truly needed by knowing when a specific part is likely to fail. This helps avoid unnecessary maintenance while also preventing unforeseen equipment breakdowns.



Machine learning is used to develop a unique profile of each individual that is then used to customize and personalize the user experience.

Personalized Product or Service Matching

Using recommendation engines, products or services tailored to individual customers' preferences can be offered on websites. An AI recommendation engine is a system that filters user information and can make recommendations to them based on their preferences, interests, and previous behavior. The specific interests/preferred items of a specific user can be predicted using this customer profile. Customers can easily and quickly find the items they want with recommendations systems, making the customer journey and user experience more seamless.

Personalized Content Generation

AI-powered solutions make it easier to create content marketing strategies that are tailored to specific individuals or group of people. They accomplish this by automating content types, communication channels, and even content delivery timing. Furthermore, AI enables marketers to create customized customer experiences by allowing them to analyze various data patterns in depth. Predictive personalization is the term for this. A travel company, for example, might use customer data such as browsing history, previously booked flights and hotels, and social media activity to come up with locations and types of activities that are likely to appeal to each customer and then customize marketing messages based on that.

Customized Product Functionality

AI and machine learning methods entail extracting information from large amounts of data to predict what consumers want, when they want it, and how they prefer to communicate. Predicting which products they are most interested in is one example. Companies can create customized product functionality by using AI and machine learning to predict what product features customers want and will be searching for online.


Pattern & Anomaly Detection

Machine learning and other cognitive approaches are used to identify patterns in the data and learn higher order connections between information that can provide insight into whether a given piece of data fits an existing pattern or is an outlier and doesn’t fit.

Fraud Detection

Anomaly detection, a technology based on artificial intelligence (AI), can detect unusual activity or anomalies in a data set captured. In most cases, these anomalies or irregularities can be turned into problems like fraud, errors, and design flaws. Anomaly detection will aid in the efficient detection of fraud and the discovery of suspicious behavior in large and complex Big Data sets. For example, by creating detailed risk profiles on clients and scoring them based on granular data, AI-Based Fraud Detection can assist banks and other financial institutions in preventing fraud and money laundering.

Risk Analysis

Anomaly details must be identified quickly to take effective steps for both risks and rewards in a timely and accurate manner. In AI-based risk analysis, anomaly detection entails identifying interest trends (deviations, idiosyncrasies, outliers, and so on) that differ from expected activity within a dataset (s). Decision makers who use these cognitive technologies to foresee and proactively manage risk can gain a competitive edge and use risk to propel their companies forward.

Intelligent Monitoring

End-to-end disparity-free tracking is used in anomaly detection systems to scrutinize data and find the tiniest anomalies that humans would miss. It is commonly referred to as intelligent monitoring. Intelligent Monitoring enables businesses to analyze raw data in order to improve decision-making, collaboration, and overall performance.


Goal-Driven Systems

We apply machine learning and other cognitive approaches to help find the optimal solution through iterations of trial and error.

Scenario Simulation

By analyzing existing simulation data, Artificial Intelligence (AI) is proving to be a viable approach for reducing the less essential simulation scenarios. To train an AI model, several machine learning algorithms are used, such as Markov decision-based artificial neural networks DQN (Deep Queue Networks), decision trees, fuzzy logic, random forests, and various other improvements beyond DQNs. AI Sampling will generate individual simulations to be used for a variety of purposes, such as training AI-models. These scenario simulations can aid a company's ability to move quickly and reduce risk by allowing them to create, test, and learn about possible improvements before they are implemented for customers and employees.

Resource Optimization

Resource optimization enables organizations to meet resource requests as efficiently as possible. Resource optimization compares all open resource requests to available resources and devises a strategy to help you achieve the company's goals. Adaptive workload management was developed to intelligently assign data resources across workflows, either automatically or by supplying an alert based on the preferences of the business. Machine learning is an essential component of this process, tracking anticipated and real runtimes to make predictions for future workflows and adapt resources accordingly.

Iterative Problem Solving

The software development lifecycle, which is essentially a process in which you are solving a problem—in well-defined steps—inspires iterative problem-solving in AI and machine learning. Problem-solving in machine learning and AI is an iterative process, similar to software development which looks to recognize the problem, review the dataset, establish a realistic end goal, list alternate solutions, select a solution, implement the solution, and evaluate to allow machine learning algorithms to understand a problem and then solve it iteratively. Organizations can use the iterative processes to reduce risk, boost productivity, and manage problems in a more dynamic and flexible manner.

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