AI solutions for handling email attachments in service requests

🚀 Unlocking Multi-Level Email Attachments in Pega: A Practical Solution Dealing with nested or multi-level email content in Pega can pose a unique challenge, especially when your process requires extracting and attaching all underlying attachments from an email to a case. Many in our Pega community, including experts and enthusiasts alike, have encountered this scenario, where attachments within an attached email (.EML file) need to be extracted and appropriately attached to the case for comprehensive processing. Currently, while Pega's Email Channel and NLP capabilities proficiently handle direct attachments to an email, they may not automatically delve into attachments nested within other attachments, such as PDFs attached to an .EML file within the original email. This limitation necessitates a more customized approach to ensure no data is missed during case processing. Here's How to Tackle This: 1. Utilize Custom Activities: Create a custom activity that triggers when an email is received. This activity should check for .EML attachments and, upon finding any, parse these files to identify further attachments within. 2. Implement Java Code: Within the custom activity, leverage Java code designed to read .EML files. This code should extract any attachments found within these files (e.g., PDF2 and PDF3 from the scenario) and prepare them for attachment to the case. 3. Attachment Handling: Once extracted, use Pega APIs to attach these newly extracted files to the relevant case, mimicking the behavior seen with directly attached files. 4. Testing and Iteration: Rigorously test this custom solution across various email scenarios to ensure reliability. Be prepared to iterate based on findings to refine the process. Expert Insight: Addressing this requirement may involve diving deeper into Pega's capabilities and potentially extending them with custom code. It's a testament to the flexibility and extensibility of Pega's platform, allowing solutions to be tailored to specific business needs. While Pega offers robust tools for email processing, certain complex scenarios like nested attachments require a blend of out-of-the-box features and custom development. By adopting a creative approach, leveraging Pega's extensible architecture, and engaging with the vibrant Pega community for insights and support, organizations can effectively meet and exceed their case management requirements. Note: This solution highlights a pathway to extend Pega's native email handling capabilities to meet specialized requirements, showcasing the adaptability of Pega's platform and the value of community collaboration. #PegaCommunity #casemanagement #EmailProcessing #PegaSolutions #digitaltransformation

🧵 Real Stories of Generative AI in Action (Feature 6 of a multi-part series, you can access the full series at #AWSGenAIinAction) 🗣️ For all organizations with a customer service function, efficient classification and routing of queries is critical to ensure customers receive a speedy and accurate service experience. Recently, the AWS Generative AI Innovation Center (#GenAIIC) collaborated with leading property and casualty insurance carrier Travelers to address this challenge. 🏷️ Classify to accelerate: Travelers receives millions of emails a year with agent or customer requests to service policies, with 25% of emails containing attachments (e.g. ACORD insurance forms as PDFs). Requests involve areas like address changes, coverage adjustments, payroll updates, or exposure changes. The main challenge was classifying emails received by Travelers into service request categories. 👩💻 Designing an efficient system: To achieve the optimal balance of cost and accuracy, the solution employed prompt engineering on a pre-trained Foundation Model (FM) with few-shot prompting to predict the class of an email, all built on Amazon #Bedrock using Anthropic #Claude models. The teams manually analyzed 4K+ email texts and consulted with business experts to understand the differences between categories to provide sufficient explanations for the FM, including explicit instructions on how to classify an email. Additional instructions showed the model how to identify key phrases that help distinguish an email’s class from the others. The workflow starts with an email, then, given the email’s text and any PDF attachments, the email is given a classification of 13 defined classes by the model. ✅ Getting it right, and saving time: The Travelers solution (with prompt engineering, category condensing, document processing adjustments, and improved instructions) yielded classification accuracy to 91%, a 23 pt improvement vs. the original solution with just a pre-trained FM. Using the predictive capabilities of FMs to classify complex, and sometimes ambiguous, service request emails, the system will save tens of thousands of hours of manual processing and allow Travelers to redirect that time toward more complex tasks. 🌟 What I find most inspiring about this project is how it demonstrates the practical application of #GenAI to augment human capabilities. It's a great example of how organizations can use technology to optimize operations while maintaining focus on customer experience. Read more here: https://lnkd.in/et3P5XYp #AWS #CustomerService #Innovation #DigitalTransformation #Insurance #TechInnovation #AmazonBedrock

From AR Inbox Overload to Instant Response: How AI Is Transforming Enterprise AR Here’s an example of how Emagia is helping a global enterprise improve order-to-cash customer experience with AI agents. The challenge: - More than 20 email inboxes receive accounts receivable correspondence from customers across multiple business units and product lines. - The company receives thousands of customer emails every day, which are manually handled by hundreds of AR staff. - Each email required the team to research and find the correct supporting documents, seek approvals from managers, and respond to the emails manually. The result? Long response times and a subpar customer experience from the AR team. The AI-first Solution approach: The CFO and VP of Finance adopted an AI-first strategy to enhance the customer experience with the AR process. Enter Gia—Emagia’s AI copilot for finance teams. Gia now:  ✅ Reads all AR inboxes and categorizes all incoming AR emails  ✅ Identifies requests (statements, invoices, disputes, remittances, credit, etc.)  ✅ Drafts accurate, same-day replies using generative AI  ✅ Attaches the right supporting documents  ✅ Routes exceptions to human agents with governance controls in place Guardrails are in place to govern Gia’s responses—including approved content templates and clear policies on when to reply automatically and when to escalate to a human AR agent’s queue. 🚀 The Results:  ✅ 80%+ of customer emails now get same-day responses  ✅ Improved customer experience with faster, more accurate communication  ✅ Reduced load on AR, sales, and support teams dealing with customer-related issues AR teams then shift their focus from manual work to improving strategic business outcomes.  This is what autonomous finance looks like in practice—AI not just automating but transforming how enterprise finance operates to serve customers better.  👉 Build your AI Agent: https://lnkd.in/gr9AYPd4   The future isn’t coming. It’s already here—with Emagia. #AgenticAI #Emagia #GiaOrchestrationStudio #AutonomousFinance #AIinFinance #CFO #Financeinsights #Thoughtleadership #EmagiaAI