Timely, actionable insights can ensure project success in a business environment that is volatile, uncertain, complex and ambiguous (VUCA). For meaningful insights, analysis of large amount of data (past, present and near real-time) is necessary; this obviously is humanly not possible. But now, for the first time, grappling with myriad business complexities in real-time is possible with RISHI-XAI! RISHI-XAI our new XAI based enterprise project intelligence product is integrated with factory models of multiple project types, like Agile, Maintenance, Testing, DevOps, etc. So this gives RISHI-XAI the ability to continuously learn and adapt to more business-critical situations.
Decision makers can set macro to micro level goals across projects by using RISHI-XAI’s powerful ‘What-If Scenario Analysis’ feature. This would help the users with different roles to track project goal deviations, on a near real-time basis. RISHI-XAI suggests actions that users could implement to reach the ‘set goals’. Further, they can visualize the impact of these actions before actually implementing them.
RISHI-XAI’s “What-If” feature helps you answer questions like, “Can you suggest performance improvement measures while keeping resource utilization constant?” or “By increasing quality effort by 20% across the board, how is project performance affected?” (Learn more about RISHI-XAI)
Ditto is an AI-powered similarity engine. It uses various AI/ ML techniques to find similar tickets that have been previously resolved. Agents can search for similar tickets based on the ticket subject or description.
Ditto harnesses the power of Natural Language Processing (NLP) to decipher the semantics of the ticket subject and description, and uses that to find historical tickets with similar meaning or context. This helps agents find existing solutions that worked for similar, historical tickets that can possibly be applied to their new tickets.
Having details of similar tickets handy for reference helps Zendesk agents get a better understanding of the work at hand and how work of that nature has been dealt with earlier. Ditto’s AI engine will recommend similar tickets (that are context-specific) for the current ticket and agents can refer to them to get work done in a more efficient manner.
kAIron is a web based microservices driven suite that helps train contextual AI assistants at scale. It is designed to make the lives of those who work with AI-assistants easy by giving them a no-coding web interface to adapt, train, test and maintain such assistants. Learn more about kAIron here.
We all understand the impact of a happy self-organizing team delivering value with high quality. The opposite is also true. Unhappy teams deliver bad quality in unacceptable timelines. Our products now come with the power of Team Dynamics.
Using Artificial Intelligence and advanced NLP techniques including Sentiment Analysis, we can help you understand how your teams perform and react across projects, in real-time.
Multidimensional Team Dynamics Metrics (TDM) are generated by analyzing the sentiments, tones, disagreements, trust, and conflicts in team conversations within the project, using Advanced AI/ML to give you insight on how happy or unhappy the team is feeling. By connecting with DeepAffects, TDM provides insights into the team’s dynamic state (Performing, Storming, Outperforming, etc.), trust network, conflicts & more. This module is available on our SaaS instances, as an add-on to any of our products.
Leveraging the power of conversational AI, the SwiftEnterprise Chatbot on Slack lets you know what’s in your inbox and enables you to perform routine actions on them like route, reject and log time, right from your Slack channel. The AI assistant works based on context and intelligent dialogue management to ensure that you have smarter dialogue every time you interact with it, while the BOT continues to learn & improve through real conversations. This module is available as an add-on and is currently in BETA stage.