Industries and Use Cases

AI technology is changing all industries including life science, healthcare, marketing, banking and finance, automobile, telecommunication, manufacturing, defense and military, entertainment and media, education, etc.

Use Case -

Life Science​

In the 1990s, the Human Genome Project drastically changed the way we perceive life and researchers working on the Human Microbiome Project has identified more than 100 trillion microbes that may have either positive or negative effects on our health.

By setting up sophisticated AI and machine learning tools and models, the enormous amount of unstructured data consisting of text, images, numerical data and sounds can be comprehended in a faster and more efficient manner.

Using AI and machine learning the huge amount of unstructured data consisting of text, images, numerical data and sounds can be comprehended in a faster and more efficient manner.

Use Case -

Marketing and Branding

Natural language processing (NLP), also known as text-mining or language analytics is an artificial intelligence technology that harness insights from large amounts of unstructured text, emails, social media conversations, online charts, survey responses and other forms of textual data.

It can be used in marketing, to extract customers motivations or intentions, from very large textual data sets (ie: customers feedback), and assist your teams on branding, content strategy, customer experience and lead generation. NLP can also help to produce and automate sentiment analysis, text classification, chatbots, text summarization, urgency detection, speech recognition, auto-correction, named entity recognition, etc.

Sentiment Analysis
Text Classification
Text Summarization
Named Entity Recognition
Customers Feedback
Content Strategy
Urgency Dection
SEO
Branding
Lead Generation
Chatbots
Auto-Correction
Use Case -

Anomaly Detection​

Anomaly detection is a data mining process used to determine types of anomalies found in data sets. Organizations need to keep track of anomalies and to study these anomalies to determine details about their occurrences.

Some organizations hire people to manually tag data and identify outliers, but this approach is limited by availability of people, time to label and budget, particularly when we are talking about TBs of data per day. For these cases, we need to label data and anomalies programmatically.

Automated anomaly is the next big thing for digital business and each business incident discovered could be an opportunity to save money, or to potentially create new business opportunities.

Project Flow -

Typical AI Project Life Cycle

Our platform will help you to deploy AI models more rapidly.

01

Scoping

Project understanding and planning, what resources to employ to accomplish the project.

02

Data Collection

Data collection, preparation, cleaning, augmentation, preparation, annotation, wrangling, etc.

03

Model Training

Experiment, train, evaluate and tune different types of machine learning and AI models on collected data.

04

Deployment

Model deployment, web portal, API and maintenance in the production environment.

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