The end users in the organization found it difficult & time consuming to raise an IT service ticket on the IT Service request tool. The IT service provider was hence looking for an innovative solution which will be easy for the users and improve user satisfaction.
Based on the business problem, we advised to have a Telegram APP where the users can quickly report the IT issue by simply mentioning about the issue.
We then developed an NLP, NLU, NLG & Deep Learning based Engine (BOT) to extract the user messages from the Telegram App, respond to user queries, create tickets in the ticketing tool (ManageEngine), update users about the ticket status.
The NLP Engine (BOT) was developed using the Custom AI Algorithms.
The customer is working with state level security agencies to provide intelligence from crime data. They were looking for social media data based insights for the habitual offenders or people with suspicious activity.
Python, Django, NLP using spaCY
The customer was a state level security agencies and already have a solution to register Crime data & criminal database. As an extension to current solution, the customer wanted to explore use of social media analytics to provide additional insights based on sentiment and network graph based user information. The solution is developed for Twitter data source. The solution could give 4 level deep twitter id connection, but also provided sentiment analysis of tweets posted by identified twitter handle. This was a PoC, and it is still under discussion.
The customer wanted to automate the note taking process of executive conference calls and sales review calls internal to their company. The current process was manual, which was error prone and time consuming.
Python, Django, Google Speech to Text API
The speech to text solution was developed using Python & Google speech to text API. The application frontend was developed using Python Django framework. These speech to text API provided good accuracy (~90%) for native English speaker and when number of speaker are less than 3. The accuracy reduced when multiple speakers with vernacular accent were speaking. Also the current version of Google API is not matured for speaker diarazation. Ran the pilot project and demonstrated results to the senior stakeholders