20% of C-level executives (across 10 countries and 14 different industries) report that they are using machine learning as a core part of their business. (Mckinsey)
Robotic process automation was the flavor during and after the pandemic for tech companies. They’re better than business process automation tools, as the robots (as in, the code on server) acts like a human inputting and consuming data from multiple systems. As technology will improve, RA projects are likely to lead to some job losses in the future.
Algorithms are widely useful in detecting patterns in vast volumes of data and interpreting their meaning accurately. Moreover, cognitive insights provided by ML are better from traditional analytics as their ability to use new data to make accurate predictions or put things into categories only improves over time.
Businesses that engage with their customers using natural language processing intelligent agents, chatbots, and machine learning are on steady rise. However, many multi-national businesses use cognitive engagement technologies more to interact with employees rather than with customers.
We analyze your user case to develop an AI-driven software so that we can ensure its positive user buy-in. In the process, we gauge your businesses’ readiness for AI-driven automation and take you through every stage of adopting artificial intelligence in processes and operations.
We offer our AI audit services for businesses whose AI systems fails to meet desired results. We review the algorithm (code), its architecture, the underlying business logic, and its usability. Our team then works on redesigning, correcting, or upgrading the software as per the audit findings.
We help in scaling your AI-adoption from a single purpose to company-wide business solutions. Or AI consultants will work to create a sustainable, AI-driven ecosystem for your business keeping in mind future-expansions of services, teams, and broader changes happening in the industry.
Understanding what the client exactly wants from the business perspective is crucial as it is always helpful to have a clear understanding of all the expectations right from the beginning. Business understanding allows us to determine KPIs – to have clarity whether the customer wishes to make predictions or wants to improve sales or minimize the loss or optimize any particular process etc.
Much of the data is collected from the various processes followed in an enterprise. At many levels, the data is recorded in various software systems used in an organization and it helps in understanding the process followed from the product development to deployment. In addition, transactional data also plays a vital role as it is collected on a daily basis. Thereafter, we apply methods to the data to extract the important information related to the business or project.
Data preparation is the process of structuring and organizing the collected data so it can be used in business intelligence (BI), analytics and data visualization applications in later stages. A few components of data preparation include data preprocessing, profiling, cleansing, validation and transformation.
Exploratory Data Analysis refers to the process of performing initial investigations on collected data in order to discover patterns, to spot abnormality, to test and check assumptions with the help of statistics and graphical representations.
So, we’ve reached a stage where we can prepare a descriptive diagram of relationships between various types of information that are to be stored in a database. One of the primary goals of data modeling is to create the most efficient method of storing information and simultaneously providing complete access and reporting.
Here we use different evaluation metrics to understand a machine learning model’s performance- its strengths and weaknesses. Model evaluation is important to gauge the efficacy of the said model during initial research phases, and it also plays a role in model monitoring. This is the last stage in the data science life cycle and sometimes can be one of the most cumbersome.
Now since we’ve tested the ML model – it’s time to expose the ML model to real use. In this stage, we integrate the machine learning model into the existing production environment to make practical business decisions based on data.
I help clients accelerate their digital growth through software engineering and digital product development.
I also act as an advisor to help guide technology decisions and assess digital growth.
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We partner with individuals and organizations on their journey to digital transformation. See how startup founders around the world have leveraged our services to build great products and even stronger relationship with their customers.
AI ML represents an important evolution in computer science technology and data processing that is transforming a large range of industries across the globe.
As businesses undergo digital transformation, they’re faced with mountains of data that is at once incredibly valuable and burdensome to collect, process and analyze. New tools and methodologies are needed to manage the vast quantity of data being collected, to mine it for insights and to act on those insights and make real-time business decision. And this is where artificial intelligence and machine learning come in.
AI enables better and fast decision making which in turn, is based on speedy feedback mechanism. It also brings precision, speed, and efficiency to the entire software development lifecycle. AI-powered tools help find errors and fix bugs in the code. This, in turn, ensures smooth functioning in all the running environments.
The average cost to develop an AI ML app would range anywhere between $100,000 to $200,000. However, this is a very rough estimate. The final price mostly depends on the features to be added and complexity of your app. If you want to get an estimate of developing an AI app or software for your business, contact us.
Adaptive ML is a more advanced solution that takes real-time data collection and analysis seriously. As its name suggests, it easily adapts to new information and provides insights almost instantaneously.
Adaptive learning collects and analyzes data in sequential order, not all at once. This enables these adaptive ML models to monitor and learn from the changes in both the incoming and outgoing data; it allows the model to adapt its data collection, grouping, and analysis methods based on new information.
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