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Why choose The AI Point for your business?

When science and technology meets with business the amount of output you get is enormous. AI will give you the entire power to focus and face today’s challenges and build you to face the future upcoming challenges. 

Evolving business needs is fulfilled with our AI solution by boosting productivity through creative possibilities. Our AI technology has a wide 24/7 support from our expertise.

Artificial Intelligence

It creates a value idea by analysing the existing data and helps predict product recommendation.

With reinforcement learning our solution success rate increases 94%

Machine Learning

We create ecosystem that helps you solve challenges evolving around your business in real time with machine learning.

When your system force thinks like a human in both logical as well as technical perspective then your business is the game changer in the market. 

Machine Leaning and deep learning


Machine Leaning and deep learning gives your business a wide range of access to hidden datas and make your process easy to compete with the market competitor by taking Quick, powerful and creative decision that can change the way of process you did before.

Smarter and faster decision making gives you greater growth in short as well as long term.

The AI Point Supports it’s clients in various verticals for Artificial Intelligence such as:

Artificial Intelligence is borrowing characteristics from human intelligence, and applying them as algorithms in a computer friendly way to find solutions to complex problems in a more human-like fashion. Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Through deep learning computers are trained to perform human-like tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing.

AI is been used in various verticals such as cyber sccurity, face recognition, data analysis, logistics,markting & advertising etc. Popular uses today include:

Speech Recognition

Both the business and academic worlds have embraced deep learning for speech recognition. Many organizations are already employing deep learning technologies in their systems to recognize human speech and voice patterns. The common way to recognize speech is the following: we take a waveform, split it at utterances by silences and then try to recognize what’s being said in each utterance. To do that, we want to take all possible combinations of words and try to match them with the audio. We choose the best matching combination. According to the speech structure, three models are used in speech recognition to do the match: An acoustic model contains acoustic properties for each senone. A phonetic dictionary contains a mapping from words to phones. A language model is used to restrict word search. Those three entities are combined together in an engine to recognize speech.

Image Recognition

One practical application of image recognition is automatic image captioning and scene description. This could be crucial in law enforcement investigations for identifying criminal activity in thousands of photos submitted by bystanders in a crowded area where a crime has occurred. Self-driving cars will also benefit from image recognition through the use of 360-degree camera technology.

Natural Language Processing

Neural networks, a central component of deep learning, have been used to process and analyze written text for many years. A specialization of text mining, this technique can be used to discover patterns in customer complaints, physician notes or news reports, to name a few.

Recommendation Systems

Amazon and Netflix have popularized the notion of a recommendation system with a good chance of knowing what you might be interested in next, based on past behavior. Deep learning can be used to enhance recommendations in complex environments such as music interests or clothing preferences across multiple platforms.