We offer a tailored AI consulting services for our clients. Our services include end to end AI discovery and implementation which provides maximum benefit to our clients.
AI powered chat bots can improve productivity by automating manual processes which are time consuming. Chat bots have 24/7 availability and can resolve most queries efficiently. Some examples are :
We can design and train chatbots which are customised to work with your organizations knowledge base and can be used to improve the response time for your customers.
Natural Langugage Processing in AI is the process of analyzing unstructured text in a business process.
NLP enables AI systems to analyse text written in natural language like an individual. This allows to us to automate the processing of text written in natural language like emails, documents or customer feedback.
Some examples of how NLP can be used to automate business processes are:
1. Extraction of keywords from like name, address, company name and business specific keywords from emails and documents.
2. Classification of emails into various categories, for example spam email detection.
3. Classification of feedback into positive or negative sentiments.
4. Classification of queries received from a website and directing them to the right department.
5. Summarization of text to get a better understanding. This is useful while analyzing large documents.
6. Translation of text into a different language which is useful while working with internation clients.
We can design and customize an NLP system according to your organisation's needs.
One example of an NLP system we have developed is the DIS Email Parser which is an Outlook addin available on the Azure Marketplace.
Please contact if you would like to know more about how NLP can help you to enhance your processes.
We can implement new AI capabilites as well as enhance existing AI systems. The processes we will use are:
This process includes identification of opportunities where AI can benefit the business. This process involves
Training the machine learning model with the prepared data.
Testing and refining the machine learning model.
Planning future training plans.
Planning release and maintenance.
Development of performance metrics for continous improvement.