AI Solutions for Streamlining Customer Support Processes

Generative AI has been making waves in the industry for over two years, revolutionizing how businesses engage with customers. In this blog, the Engineering team at Noom shares how they developed their AI-powered customer support solution. Noom is a digital health company offering a subscription-based mobile app that helps users achieve their wellness goals, and it relies heavily on its chatbot for customer interactions. While directly leveraging ChatGPT-4 for customer chats was a promising first step, the team identified several challenges: issues with hallucinations, a lack of customization to user needs, and a mismatch with Noom's unique communication style. To address these challenges, the team developed a customized solution. They started by using Prompt Instruction with GPT-4 to form the foundation of their AI assistant. Next, they implemented Prompt Augmentation with Noom's Knowledge Base (RAG), Dynamic Prompts based on user data, and JSON Format Responses. These elements enabled the system to accurately process user messages, understand their needs, and deliver tailored responses. Furthermore, recognizing the importance of human connection, the team integrated classification models with LLMs to identify when a human touch was needed, ensuring users felt understood and valued. This approach is a great example of companies leveraging generative AI to create customized solutions that address their unique challenges. #datascience #machinelearning #generative #LLM #chatGPT #customer #chatbot – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gvJg5tMK

Over 50% of our support chats were resolved by our AI assistant last week. No human intervention! This didn’t happen by accident. For small business owners looking to automate support, the real work happens before you flip the AI switch. It starts with building a strong foundation, and getting your team onboard. Here’s how we did it: The Process 1. Audit your support history We analyzed thousands of past tickets and chats to identify the most common and repetitive questions. Yes, we did this with AI. 2. Build (or expand) your knowledge base We created over 1,000 new help articles in a single quarter—filling gaps, refining answers, and making sure every article was easy to follow. Yes, we also created new articles with AI. 3. Train the AI assistant We integrated our knowledge base with our AI assistant and ran extensive testing to improve responses and coverage. 4. Educate and align the team We openly communicated how AI would help, not replace our support team. We showed how it would reduce mundane work and free them up to focus on more strategic, meaningful customer conversations. 5. Monitor, learn, and iterate We continuously tracked resolution rates, flagged weak responses, and kept refining the system. The Results • Faster, more consistent support for customers • 50% drop in manual support chats • A more energized support team, now focused on deeper issues, proactive outreach, and customer success initiatives The Takeaway AI isn’t just a tool. It’s a mindset shift. If your team sees it as a threat, you’ll hit resistance. But if you bring them along—show them how it removes the boring parts of the job so they can focus on the impactful ones, you unlock a whole new level of engagement. The real power of AI isn’t about replacement. It’s about elevation. Elevate your team. Serve your customers better. And don’t skip the groundwork. #AI #CustomerSupport #Automation #SmallBusiness #SaaS #Leadership #CustomerSuccess #ecommerce

Last month our team of 5 did the work of 50 people. While taking every weekend off. The secret? We replaced the most expensive job with code: Let me show you exactly how this works. Three features transformed our support from "answers questions" to "handles entire workflows": 1. Guidance: When someone requests a feature, our AI: - Thanks them properly - Logs feedback automatically - Directs to feedback portal - Updates them on progress - Maintains perfect brand voice 2. Processes: Take a refund requests. Our AI: - Checks account status - Verifies eligibility - Processes through Stripe - Sends confirmation - Logs everything instantly 3. Actions: This is the real power. AI can: - Pull CRM data - Process payments - Update records - Trigger notifications - Make API calls Real workflow example: Customer: "Can I get a refund?" AI: - Verifies account instantly - Checks eligibility - If eligible: processes immediately - If not: explains why + alternatives - Everything logged in seconds What used to take 30 minutes Now happens automatically. Start with feature requests. It's simple but shows the power. Then you'll want to automate everything. Because your support team should solve problems. Not copy-paste responses at 3AM. What would you automate first? Interested in seeing how Helply can do this for you? Shoot me a DM

🧠 AI-First Use Cases for Customer Success, Account Management & Support It's not just sales that can benefit from AI-powered automation. We're also thinking on the customer experience and how we can better serve our customers leveraging AI in our workflows at Vanta: 🆕 Onboarding & Activation - Agentic AI-led Customer Onboarding – An autonomous AI agent walks customers through onboarding, dynamically adjusting based on user behavior, role, and progress. - Automated Customer Onboarding – AI sends tailored welcome messages, interactive walkthroughs, training content, and milestone reminders, with personalized progress tracking. - Onboarding Risk Prediction – AI flags customers likely to stall during onboarding based on usage signals, role, and industry, prompting human intervention at the right moment. 📊 Customer Health, Retention & Expansion - AI-generated Customer Health Scores – AI continuously monitors product usage, NPS scores, ticket volume, and sentiment to produce a dynamic, predictive health score. - AI-powered Renewal & Expansion Insights – Predictive models surface customers likely to churn or ready to expand based on product adoption, engagement signals, and historical behavior. - Automated QBR Generation – AI creates tailored quarterly business review decks using real-time usage data, benchmarks, and suggested action items for growth or risk mitigation. 🗣️ Feedback & Voice of the Customer - AI-powered Customer Feedback Collection & Tracking – AI gathers structured feedback from NPS, CSAT, support tickets, onboarding surveys, and calls, and categorizes it into themes for PM and GTM teams. - Product Feedback Loop Automation – When a customer submits a product request, AI logs and categorizes it, tracks request status, and automatically follows up when the request is fulfilled or addressed. 💬 Support & Issue Resolution - AI-driven Support Ticket Triage – AI prioritizes and routes incoming tickets by urgency, topic, and customer tier, suggesting answers or tagging the appropriate team. - Self-service AI Knowledge Assistant – A conversational AI assistant that provides customers with instant, contextual answers based on docs, past tickets, and product updates. - Auto-Response Suggestions – AI drafts first-response templates to support tickets, tailored to ticket context and customer profile, saving agents significant time. 🎯 Proactive Engagement - AI-Powered Play Recommendations – AI suggests proactive outreach plays for CSMs and AMs based on customer lifecycle stage, feature usage, or risk indicators. - Milestone Celebration Automation – Automatically send personalized emails or in-app messages when customers hit key milestones (e.g., passed audit, integrated first vendor), boosting engagement. - Usage Pattern Anomaly Detection – AI spots abnormal drops or spikes in usage and alerts the account team to investigate. Interested in solving these problems with us? Check out our Founder in Residence role opening! 🚀

I automated 95% of our customer support flow over the weekend (for real) thanks to AI tools that turn anyone into a technical product builder. There are 3 steps to the workflow: First is a RAG AI agent that's trained on our user guide and a corpus of customer support email threads. When a support ticket comes in, I just copy it in here and get an email response that I can send back to the user This agent works because it's built on a continuously-improving knowledge base, curated by a second AI system. This system periodically reads through a database of support interactions to find new learnings to add, which in turn makes the RAG agent more capable over time At the bottom is a system that collects new support email threads that I resolve and adds them to a database. I have third AI system running in the background that automatically classifies and tags threads in my inbox for this purpose These 3 AI systems working together take all of the CS heavy lifting off my shoulders, allowing me to spend valuable time elsewhere We don't automate to remove people from the equation - we do it to leverage ourselves better where we're needed It's how we manage to scale so effectively at Aomni as a 5-person team