How Konverso and Generative AI will add value to your Atlassian Platform?

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Introduction 

In the aftermath of the Dot-com bubble crisis, Scott Farquhar and Mike Cannon-Brookes founded Atlassian, likely unaware of the monumental impact their creation would have on the software development and collaboration landscape over the next 21 years. Their visionary endeavor has since transformed the industry, leaving an indelible mark on how teams work together and innovate 

We believe at Konverso that a new revolution is coming. This revolution has a name: Generative AI - a revolution that started a few months back with GPT-3, and now making its way to the concrete implementation and use cases. In fact, according to Gartner, generative AI is projected to represent a staggering 10% of all data generated by 2025, a significant increase from its current share of less than 1%. 

In this blog, we'll explore the capabilities of Generative AI, and how Konverso can help you take advantage of it on top of your Atlassian platform. 

What are the key capabilities of Generative AI 

 

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Greg Brockman the president, Chairman, and cofounder of OpenAI recently said in an interview “everything is language in an enterprise”. You can think of the information contained in various platforms such as Jira Service Management tickets, Confluence knowledge base, SharePoint, website content, and conversations in Microsoft Teams or Slack. However, to effectively process and utilize this information, you need a Conversational AI platform that leverages the best Generative AI models. This platform can help you extract insights from unstructured data, automate workflows, and improve customer service by providing accurate and timely responses. 

Generative AI models such as GPT-4 are powerful language models that can perform a wide range of natural language processing (NLP) tasks with high accuracy. Here are some specific tasks that Generative AI can do: 

  • Text generation: Generative AI can generate natural-sounding text in a variety of contexts, such as essays, stories, poems, and even computer code. 

  • Language translation: Generative AI can translate text from one language to another with high accuracy. 

  • Question-answering:Generative AI can answer questions based on a given context, such as a paragraph or a document.

  • Summarization: Generative AI can summarize long pieces of text into shorter, more concise summaries.

  • Sentiment analysis:Generative AI can analyze the sentiment of a given piece of text, determining whether it is positive, negative, or neutral.

  • Chatbot: Generative AI can be used as a chatbot to simulate human-like conversations with users, answering questions and providing assistance.

  • Text completion: GPT-3 can predict the next word or phrase in a sentence or paragraph, making it useful for tasks such as autocomplete and autocorrect.

  • Text classification:GPT-3 can classify text into different categories based on content, such as news articles, product reviews, or social media posts.

  • Language modelling: GPT-3 can be fine-tuned to generate text in a specific style or domain, such as technical writing, academic research, or creative writing. 

What are the use cases where Generative AI adds value on top of Atlassian

Generative AI becomes truly valuable when it creates meaningful outcomes for specific personas, such as employees, contact center agents, and customers, within their unique work contexts (reviewing a ticket in Jira Service Management for instance) and by utilizing relevant enterprise data.

Let's explore some use cases where Generative AI can deliver significant value. 

Agent Assist:  

Operating in a contact center can be demanding, as employees are tasked with delivering exceptional customer support and service, often in the face of challenging situations or interactions with frustrated customers. Furthermore, they must efficiently manage a high volume of tickets or calls while adhering to the expectations set by management. To excel in customer service, contact center employees need comprehensive training and the ability to navigate diverse scenarios. Equally important, they must demonstrate patience and maintain a courteous demeanor. 

In such a working environment, Agent Assist becomes an invaluable tool to lighten the workload and make being a call center agent more enjoyable.  

Agent assist employs Generative AI to offer real-time support to contact center agents during customer interactions, utilizing several methods to achieve this. For instance, Agent Assist can examine incoming tickets in Jira Service Management and generate a draft response by drawing from knowledge articles in Confluence. After the Generative AI completes these tasks, Agent Assist updates the ticket status in Jira Service Management. Contact center agents can then review and modify the content before sending the ticket to their customers, saving up to 70% of their time. This increased efficiency enables the workforce to meet service-level agreements (SLAs) and boost customer satisfaction. Improved first contact resolution (FCR) also encourages customers to return in the future, fostering long-term loyalty. 

Chatbots:  

Chatbots were once touted as the future of customer support. Around 2016-2017, the hype surrounding chatbots reached its peak with numerous businesses adopting them to enhance their customer service. Chatbots were expected to provide prompt, efficient, and personalized responses to customers' queries. However, despite the high expectations and positive intentions, chatbots for customer support failed to meet the expectations.   

With Generative AI, chatbots are back, and they offer a range of unique features:   

  • Large Scale Pre-Training: Generative AI has been pre-trained on a massive corpus of text data, allowing it to understand and generate responses to a wide range of topics and conversation styles.

  • Training Data: It is easy to create a fine-tuned model (custom) with your training data.

  • Generate responses: Generative AI leverages enterprise content such as Confluence to generate personalized responses. 

  • Multilingual Capabilities: Generative AI can understand and generate responses from Confluence in multiple languages, making it a more versatile conversational agent.  

Now, Intelligent Chatbot allows organizations to sidestep the 24/7 tsunami of internal and external correspondence by proactively responding and assisting customers or employees. In doing so, chatbot deflects approximately 40% of service management interactions. From IT & HR to Finance & Customer Services, Chatbots respond across all departments and becomes an invaluable solution, by quickly learning from user interactions via machine learning. 

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What does Konverso add?  

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Our core principle is to offer a robust, scalable & ready-to-use AI solutions that includes seamless integration with your Jira Service Management (JSM) and Confluence to quickly find information, automate repetitive tasks, reduce wait times, and improve customer satisfaction.  

We take the best of the Generative AI, and we combine it with our solutions and interface with your data, knowledge and apps in the Atlassian platform. With Konverso's range of virtual assistants, chatbots, and customer service automation solutions, businesses of all sizes can streamline their operations and enhance the employee experience. 

Knowledge retrieval for Confluence 

Generative AI has been trained on large amounts of text data, enabling it to generate human-like responses to a wide range of natural language inputs. However, to provide more contextual and accurate responses for your customers or employees, Generative AI needs to have access to your enterprise knowledge sources.  

Konverso delivers a comprehensive knowledge retrieval solution that seamlessly integrates with Confluence and extracts pertinent information. Knowledge retrieval involves using a variety of techniques, such as keyword matching, semantic analysis, and machine learning algorithms, to retrieve relevant information from Confluence. Knowledge retrieval can significantly enhance the performance and accuracy of Generative AI, enabling it to provide more informative and personalized responses to user queries. 

Integration with Jira service Management

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Konverso integrates with Jira Service Management seamlessly, providing an easy-to-use and user-centric solution for Service Management automation. Customers can deploy the Konverso platform to the relevant channels, such as web chat, mobile app, or voice assistant, to enable users to interact with Jira Service Management through the conversational interface. 

Prompt engineering

Prompt engineering is a process of designing and optimizing prompts to generate desired outputs from a language model such as GPT-3. It involves crafting specific prompts to guide the language model towards producing high-quality and relevant text based on the user's input or query.

Prompt engineering aims to improve the efficiency and effectiveness of natural language processing tasks by using carefully designed prompts that provide relevant context and constraints to the language model. This can help the model produce more accurate, coherent, and appropriate responses to user requests.

Prompt engineering involves several steps, including selecting the right input and output formats, creating training data, fine-tuning the model, and evaluating the results. It often requires domain expertise and knowledge of the target use case, as well as an understanding of the strengths and weaknesses of the language model being used.

Overall, prompt engineering is a crucial step in building robust and effective natural language processing systems that can support a wide range of applications, from chatbots and virtual assistants to language translation and content generation.

Access secured, GDPR compliant Generative AI models 

As an enterprise, you are very mindful of GDPR, security and ethics. Konverso leverages the best Generative AI models that are designed with compliance, privacy, and security in mind. For instance: 

  • Dedicated Training Data: We exclusively use customer-provided training data to finetune the customer's model, ensuring that it doesn't contribute to the training or improvement of any Generative models.

  • Rigorous Content Filtering: All data submitted to our service undergoes thorough content filtering and processing. We do not store prompts or completions in the model, nor do we use them to train, retrain, or improve our models.

  • User-Controlled Data Deletion: Fine-tuned models can be deleted by users at any time and are securely stored within the same region as the resource. Prompt and completions data may be temporarily stored in the same region as the resource for up to 30 days. This encrypted data is only accessible to authorized employees for debugging purposes or investigating patterns of abuse and misuse.

  • Uncompromising Customer Data Privacy: We do not use customer data to train, retrain, or improve the models.

  • Proactive Abuse Prevention: Our synchronous content filtering and retention of prompts and completions for up to 30 days enable us to monitor any content or behavior that suggests a violation of our product terms. This vigilance ensures that our service is used responsibly and ethically. 

Conclusion: The Benefits of using Generative AI 

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By harnessing the unparalleled capabilities of the most advanced large language models through the Konverso's platform, you can tailor solutions to your distinct use-cases. This competitive advantage enables you to elevate and simplify user experiences, optimize workflows, and deliver superior results to your clients - all while freeing up your support team to focus on more complex issues. 

The Konverso platform, integrated with generative AI, is a groundbreaking solution that harnesses the immense power of Generative AI to deliver unparalleled user experiences. Its flawless integration with Jira Service Management (JSM) empowers support teams to provide instant assistance, alleviating the strain of repetitive requests. The platform’s natural language processing prowess makes navigating institutional knowledge and policies in Confluence a breeze, ensuring users can effortlessly locate the information they need. 

The latest release of the Konverso platform goes above and beyond, offering the ability to summarize and extract information from content, as well as generate new content using natural language prompts. By leveraging generative AI, the platform produces personalized responses that consider the customer's emotions, allowing human agents to focus on more complex issues. 

The advance language processing and machine learning capabilities of the Konverso- platform make it an invaluable addition to the Atlassian community. Its AI-driven functionality enables teams to optimize their operations and elevate the user experience, ultimately boosting productivity and customer satisfaction. 

As AI technology continues to evolve at lightning speed, we are ecstatic to bring the magic of Generative AI to the Atlassian community today, inspiring teams to unlock their full potential and think different.  

 

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SOURCES:  https://www.gartner.com/en/newsroom/press-releases/2021-10-18-gartner-identifies-the-top-strategic-technology-trends-for-2022 https://www.linkedin.com/pulse/azure-openai-integration-options-ankit-saxena/ https://medium.com/microsoftazure/azure-openai-and-langchain-eba69f18f050