Artificial intelligence and Machine Learning are already widely used in business applications across industries. Automation, data analytics, and natural language processing are streamlining operations and improving efficiencies. Automation alleviates repetitive or even dangerous tasks. Data analytics provides businesses with insights never possible. Natural language processing allows for intelligent search engines, helpful chatbots, and better accessibility.
Build an intelligent application from scratch or add machine learning to a pre-existing software application by implementing general machine learning, or more specific deep learning capabilities, such as natural language processing, computer vision, and speech recognition.
- Automation of repetitive tasks —help automate tedious actions that employees are required to do on a day-to-day basis.
- Intelligent decision-making — helps human beings make intelligent decisions by providing analytical proof and predicted outcomes.
- Personalization — Create a high level of personalization by offering unique experiences. Creating applications that recognize users and their interactions allows for powerful recommendation systems help personalize recommendations for products
- Creating conversational interfaces –Implementing speech recognition into a software can allow users to interact with the application in a streamlined, unique manner.
- Build intelligent conversational chatbots and voice skills.
How Businesses Use A.I. Today
Artificial intelligence and Machine Learning are already widely used in business applications across industries. Automation, data analytics, and natural language processing are streamlining operations and improving efficiencies.
Automation alleviates repetitive or even dangerous tasks. Data analytics provides businesses with insights never before possible. Natural language processing allows for intelligent search engines, helpful chat-bots, and better accessibility.
- Transferring and cross-referencing data; updating files
- Consumer behaviour forecasting and product recommendations
- Fraud detection
- Personalised advertising and marketing messaging
- Customer service via telephone or chat bots
- To build intelligent conversational chat-bots and voice skills.
How Alphanova can help you
Artificial Intelligence
Alphanova will help you spot AI candidate areas. With our expertise in applied AI and software development you can build intelligent systems that do mimic human intelligence and improve greatly .
Machine learning
Our team sets up self-learning mechanisms that can minimise errors and maximise accuracy with time. Systems powered by ML analyse data and learn new things from them delivering without any human intervention.
Data mining
We convert large amounts of raw data, unstructured data, data hard to access into clean set of standardised data, meaningful information. We collect raw data, examine and segment them, and deliver it to you in a suitable format so you can collate this information and generate insights.
Data science
Once a set of data has been defined, the next step is to find patterns and extract relevant insights by using statistical methods. Our science specialists, building analytical data and predictive models using machine learning algorithms.
AI & Machine learning Services
Alphanova consultants will build with you the best solution from business requirements to the delivery of a working solution. The data science team and machine learning consulting experts have experience in programming languages (R/Python), Apache Spark, Hadoop, and Scikit-Learn data science tools, and Tensorflow, Keras or PyTorch deep learning frameworks.
Requirements
Alphanova will analyse your requirements and find out if AI/ Machine Learning will bring a noticeable improvement or if it can be address with a traditional development approach. If it is the case the team will clarify requirements and setup the project.
Data gathering and Pattern Analysis
The second step is to gather a set of data ready for analysis. These data are cleaned and processed to find initial patterns and correlations.
Modelling & prototyping
Once the data is clean, the most challenging part of the services starts. Learning model development involves a lot of experimentation and discovery. This will be iterated until a proper mechanism is ready.
Production
When the prototype model satisfactory and addresses the business requirement, it is integrated in the application for production. Data cleansing and conversion as well as the learning and triggering modules are optimized
Optimisation
The new application will run on real data and the data analyst will fine tune the engine to get the best results.
Seems like a right fit? Get in touch with our experienced Consultants