Artificial Intelligence & Data Analytics

Our Artificial Intelligence(AI) Services

We at Innowares create data-driven, production-oriented, cloud-enabled artificial intelligence (AI) that is accessible at all times, on any device, and at any scale.

Your company is prepared for AI thanks to our worldwide knowledge, the newest know-how from technology partners, and hyperscalers. With automated procedures and contemporary data fabric, we accelerate data preparation.

We mix dependable, modular, and scalable solutions with your current data architecture to kickstart your data modernization journey.

The main drawback of describing AI as merely “creating machines that are intelligent” is that it fails to define AI and explain what constitutes an intelligent machine. Although there are many different approaches to the interdisciplinary science of artificial intelligence (AI), growth in deep learning and machine learning is causing a vital change in almost every area of the tech industry.

Machines with artificial intelligence can mimic mental capacities. AI is becoming increasingly prevalent daily, from the emergence of self-driving cars to the proliferation of intelligent assistants like Siri and Alexa. 

As a result, numerous IT firms, including Innowares, are making investments in artificial intelligence technologies. Creating intelligent machines that can carry out tasks that traditionally require human intelligence is the focus of the broad field of artificial intelligence in computer science.

Innowares Focuses On The Two Categories Of Artificial Intelligence

Based on the kinds and levels of difficulty of the tasks a system is capable of performing, Our AI solutions can be categorized into two major categories.

  • Weak AI

Artificial intelligence with little functionality is referred to as weak AI or narrow AI. The term “weak AI” describes the use of sophisticated algorithms for narrowly focused reasoning or problem-solving activities that do not fully exploit human cognitive capabilities. 

For instance, voice-based personal assistants like Siri and Alexa could be viewed as poor AI systems because they only perform a small number of pre-defined tasks, which means their responses are frequently pre-programmed.

  • Strong AI

Strong AI is a speculative artificial intelligence that promotes the idea that machines could one day achieve human consciousness on par with humans. Vital artificial intelligence (AI) refers to robots or programmes with a mind that can reason and carry out challenging tasks independently of human supervision. Strong AI has intricate algorithms that guide systems’ actions in many circumstances, and computers running on strong AI can make independent judgments without human input.

Data are converted into useful insights by our AI services. You can monitor real-time results, identify chances for growth, and make educated decisions using customizable analytics and readily available reporting tools like dashboards and scorecards.

Our Data Analytics Services

Innowares focuses on Data analytics as the practice of analyzing unprocessed data to make conclusions about such data. Many data analytics methods and procedures have been mechanized into mechanical techniques and algorithms that operate on raw data for human use.

The term “data analytics” is broad and covers many data analysis techniques. Data analytics techniques may be applied to any information to gain insight that can be utilized to make things more useful. Techniques for data analytics can make trends and indicators visible that might otherwise be lost in the sea of data. The efficiency of a business or system can then be improved by using this knowledge to optimize procedures.

For instance, manufacturing businesses frequently keep track of the runtime, downtime, and work queue for various machines, then evaluate the data to better schedule the workloads so the systems perform closer to peak capacity.

Types Of Data Analytics Offered by Innowares – 

Four fundamental categories of data analytics are distinguished in our services.

  • Descriptive Analytics

Analyzing historical data is descriptive. As the name implies, the goal of descriptive analytics is to describe what has occurred simply; it does not attempt to speculate on possible causes or create cause-and-effect connections. The only goal is to give a quick, digestible overview.

  • Diagnostic Analytics

Diagnostic analytics aims to probe more into the reasons behind events. The primary purpose of diagnostic analytics is to locate anomalies in your data and take appropriate action. For instance: You’ll want to learn why if your descriptive analysis reveals a 15% decline in sales for August. A diagnostic analysis should be carried out as the next logical step.

  • Predictive Analytics

The goal of predictive analytics is to foresee anticipated future events. Data analysts can create predictive models that calculate the chance of a future event or outcome based on patterns and trends in the past. Because it enables firms to prepare ahead, this is very helpful.

  • Prescriptive Analytics

To decide what should be performed next, prescriptive analytics examines what has already occurred, why it happened, and what might occur. Prescriptive analytics, in other words, demonstrates how to best benefit from the results that have been forecasted for the future. What actions can you perform today to prevent a future issue? What can you do to profit from a new trend?

Growth depends on your ability to comprehend the variables affecting your clients and goods. Gain a competitive edge with AI and data analysis solutions that may influence anything from patient outcomes and process efficiency to product quality and process effectiveness.