Streamlining Customer Queries Using AI

Not everyone has millions to spend on AI. There are A LOT of costs you can save. Here's something to think about: You have a simple question ("What time is it in Tokyo?") and a complex one ("Design a supply chain optimization strategy for our Southeast Asian operations"). Should both go to your strongest LLM at premium prices? In a new paper, researchers from Microsoft and Fujitsu treat this as what casinos call a "multi-armed bandit problem" - where you're constantly learning which slot machine (or in this case, which AI model) gives the best payoff for each type of query. The traditional approach has been exhausting: test every query on every model, label the best match, train a router. It's like having to taste every dish at every restaurant before deciding where to eat. Their system, called PILOT, learns from simple thumbs up/down feedback, adapting in real-time to figure out which queries need expensive models and which don't. Initially trained on general preferences about which models handle which types of questions well, PILOT then learns from actual user feedback. The result? They achieved 93% of GPT-4's performance while using only 25% of the compute budget. Think about how you are solving problems: You don't exhaustively test every option, you build intuitions from past experience and refine them through feedback. For simple customer service queries, PILOT learned to route to smaller models. For complex reasoning tasks requiring nuance, it chose a GPT model. For math problems, it discovered Claude performed just as well at lower cost. While cutting costs by up to 75% is amazing, on a fundamental level, this is about systems that learn and adapt like organizations do - starting with general principles but evolving based on specific needs. In the future, this kind of intelligent routing will be invisible but essential, like how internet packets find their way to your device. The broader question this raises: If we can teach AI systems to be smart about using other AI systems, what else can we make self-optimizing? ↓ 𝐖𝐚𝐧𝐭 𝐭𝐨 𝐤𝐞𝐞𝐩 𝐮𝐩? Join my newsletter with 50k+ readers and be the first to learn about the latest AI research: llmwatch.com 💡

I never thought anonymous chatbot chats could rewrite an SME's sales playbook—until I saw it happen in Liechtenstein. This regional producer of specialty retail products struggled to understand their customers. Expensive customer research? Out of reach. Complicated products meant lost sales in their webshop. That's when we built a simple AI chatbot to guide buyers. It wasn't fancy. Just helpful. Running on N8N for privacy – safe server. We evaluated anonymized conversations. Patterns emerged fast. Common queries revealed unmet needs – like finding the right product fast. One finding: Many asked about sustainable product features. This triggered action. First, a revamped Q&A doc for the site. Clearer answers cut bounce rates. Then, input for social media strategies. Posts now addressed those exact pain points. Engagement spiked 30%. Product development? Insights sparked a new line extension covering those needs. No more guessing customer wants. AI turned chats into knowledge gold. Research shows this works across Europe. A 2025 study on AI in SME marketing highlights chatbots for customer insights, boosting creativity and personalization: https://lnkd.in/djvP57tM Another on AI adoption dynamics notes knowledge management gains for small firms: https://lnkd.in/dTMQX4Pf And MDPI's review details AI's role in customer functions for SMEs: https://lnkd.in/dFCGGN7c Your takeaway: Start learning more about your customers with AI today. It's affordable, ethical, and transformative. What's one customer question that's stumped your team? Share below—let's brainstorm. ♻️ Repost to help your network achieve success. And follow Hartmut Hübner, PhD for more. #AI #SMEs #Customers #Innovation #Growth

Why Traditional Call Centers Are Transitioning to AI-First Support Customer expectations have evolved. They now demand instant responses, round-the-clock availability, and consistent experiences across every channel. Traditional call-center models cannot meet these requirements at scale - AI can. Key Drivers Behind the Shift Rising Customer Expectations Customers prefer real-time support over waiting on hold. AI enables instant, accurate responses across chat, voice, and digital channels. Increasing Operational Costs Recruitment, training, and agent attrition create ongoing cost pressures. AI manages repetitive queries at near-zero marginal cost, allowing organizations to scale efficiently. High Volume of Repetitive Queries Up to 70% of support requests are routine (order updates, resets, FAQs). AI resolves these immediately, allowing human agents to focus on complex, high-value interactions. 24×7 Availability Is Now Essential While human agents work in shifts, customers expect continuous support. AI ensures uninterrupted service - even during nights, weekends, and peak times. Faster Resolution, Better CX AI can instantly search knowledge bases, suggest responses, and predict next issues, reducing handling time and minimizing customer frustration. Seamless Omnichannel Experience AI connects conversations across chat, email, voice, WhatsApp, and in-app channels, ensuring context moves with the customer. AI Enhances Human Capability AI is not replacing human agents - it is augmenting them. AI handles scale and speed. Humans handle empathy and complex decision-making. The result: higher customer satisfaction and more empowered support teams.

Clients don’t lose trust because you got the answer wrong. They lose trust because it took three days to get the answer at all. Every team knows the pain: • Clients digging through endless email threads. • “Hey, can you resend that report?” messages stacking up. • Account managers burning hours answering the same questions. • Slow replies making a partner look… unreliable. AI changes this dynamic. It makes it possible to give every client a personal, proactive agent that doesn’t just answer FAQs—it knows their history, anticipates needs, and speaks in your brand’s voice. Here’s the progression we’re seeing: • Start simple with FAQs and reactive support. • Move into proactive nudges and recommendations. • Connect it to your live data—orders, tickets, product usage. • Shape its tone and boundaries so it feels like your brand, not generic AI. At Flow Digital, we’re building one now. Launching Q4, our concierge will be able to answer questions like: → “What work did Flow Digital do for our CRM setup project last week?” → “How many hours are left on my retainer this month or package?” → “When is my retainer renewal date?” → “Can you pull the transcript from our last session?” → “Which automations were worked on this month?” → …and much more. No more waiting days for an answer AI can deliver in seconds. Clients get instant, accurate responses—direct from their own data. The result is you stop losing trust (and revenue) to delays, and start looking like the partner that always has the answer. — 🔔 Follow Nathan Weill for no-fluff posts on automation, GTM systems, and AI that actually ships work.

Imagine you lead customer support operations for a fast-growing software company. You receive thousands of support tickets a day, and triaging them accurately by topic is a constant struggle. In the past, to figure out what a ticket was about (e.g., Billing, Login Issues, Bug Report, Feature Request), you had two bad options. You either wrote a massive, fragile SQL query with hundreds of lines of CASE statements and complex regex trying to catch every possible keyword, or you had to wait months for the data science team to build, train, and maintain a custom machine learning pipeline. It was slow, expensive, and constantly broke when customers used new phrasing. Using AI.CLASSIFY, you open BigQuery and write a standard SQL query. You pass the raw support ticket text into the new AI.CLASSIFY function, followed by a simple list of your desired categories: ['Billing', 'Login', 'Bug', 'Feature']. The results! With zero machine learning experience and no data movement, you hit "Run." BigQuery automatically uses Google's Gemini models behind the scenes to understand the actual meaning of the text—not just the keywords—and tags every single ticket with the correct category. What used to require a dedicated ML team and complex infrastructure now takes 30 seconds of standard SQL. #BigQuery #GoogleCloud #AI #MachineLearning #DataAnalytics #CustomerSuccess #SQL

Two weeks ago I said AI Agents are handling 95% of our sales and support and I replaced $300k of salaries with a $99/mo Delphi clone. 25+ founders DM’d me… “HOW?” Here’s the 6 things you MUST do if you want to run your entire customer-facing business with AI: 1. Create a truly excellent knowledge base. Your AI is only as good as the content you feed it. If you’re starting from zero, aim for one post per day. Answer a support question by writing a post, reply with the post. After 6mo you have 180 posts. 2. Have Robb’s CustomGPT edit the posts to be consumed by AI. Robb created a GPT (link below) that tweaks posts according to Intercom’s guidance for creating content for Fin. The content is still legible to humans, but optimized for AI. 3. Eliminate recursive loops - because pissed off customers won’t buy If your AI can’t answer a question but sends the customer to an email address which is answered by the same AI, you are in trouble. Fin’s guidance feature can set up rules to escalate appropriately, eliminate loops, and keep customers happy. 4. Look at every single question every single day (yes, EVERY DAY). Every morning Robb looks at every Fin response and I look at every Delphi response. If they aren’t as good as they could possibly be, we either revise the response, or Robb creates a support doc to properly handle the question. 5. Make sure you have FAQs, Troubleshooting, and Changelogs. FAQs are an AI’s dream. Bonus points if you create FAQ’s written exactly how your customers ask the question. We have a main FAQ, and FAQs for each sub section of our support docs. Detailed troubleshooting gives the AI the ability to handle technical questions. Fin can solve 95% of script install issues because of our Troubleshooting section. Changelogs allow the AI to stay on top of what’s changed in the app to give context to questins about features and UI as it changes. 6. Measure your AI’s performance and keep it improving. When we started using Fin over 1y ago, we were at 25% positive resolutions. Now we’re above 70%. You can actively monitor positive resolutions, sentiment, and CSAT to make sure your AI keeps improving and delivering your customers an increasingly positive experience. TAKEAWAY: Every Founder wants to replace entire teams with AI. But nobody wants to do the actual work to make it happen. Everybody expects to flip a switch and have perfect customer service. The reality? You need to treat your AI like your best employee. Train it daily. Give it the resources it needs. Hold it accountable for results. Here’s the truth that the LinkedIn clickbait won't tell you… The KEY to successfully running entire business units with AI? Your AI is only as good as the content you feed it. P.S. Want Robb's CustomGPT? We just launched 6-part video series on how RB2B trained its agents well enough to disappear for a week and let AI run the entire business. Access it + get all our AI tools: https://www.rb2b.com/ai

Marketing Automation & Customer Service is no longer just about sending emails or filling out contact forms. With AI these flows can become journeys: interactive and truly personalized - unlocking new levels of engagement and conversion in Whatsapp or Chat. But where to start? Here’s a breakdown of the top journeys most e-commerce brands have implemented and how I rank their AI potential and impact: 1️⃣ Product Recommendations | AI Potential: High Helping your customer to make a choice and find the product that fits their needs. > Move beyond static scripts! AI can find best fitting products with LLM powered semantic search, resolve blockers, compare products and provide tailored suggestions. 2️⃣ Welcome Flow | High You offer an incentive, collect and opt-in and further into > With AI, this flow can become interactive: No form like answering all extrated from a normal informal conversation. Enrich their profiles for future personalization (email, birthday, ...) 3️⃣ Customer Service | High Taking care when your customers have a problem: > AI Agents will provide 24/7 multilingual support. Collect the info you need before handing over to a human if the certain problems still need the human insight, access, or touch. Save costs while enhancing customer experience. 4️⃣ FAQ Automation | Medium Make it easy for customers to find answers. > AI ensures responses are nuanced and personalized. 5️⃣ Abandoned Cart | Medium Customer is (almost) ready to buy, but got interrupted or needs a little nudge > Send a(i) personalized message based on the exact product they have in their cart. Highlight how it fits their preferences or past purchases. 6️⃣ Cross-Sell / Up-Sell | Medium Encourage customers to buy complementary products. > AI can craft compelling arguments for upgrades, bundles or next product to buy. 7️⃣ Birthday or Special Day Campaigns | Medium Send wishes and a little gift > Let AI create a personalized message, image, or video and send it via WhatsApp. 8️⃣ Winback / Replenishment | Low Remind customers to repurchase or return. > Personalization helps, but the core is timing. 9️⃣ Review Collection | Low Gather feedback and build trust with REVIEWS.io or alike > AI can personalize requests and handle negative feedback gracefully avoiding bad reviews. 🔟 Back-In-Stock | Low Notify customers when the product they wanted to buy is available again. > AI can add a personalized touch to the reminder [don't want to get out of stock? Talk to VOIDS] 1️⃣1️⃣Referral Programs | Low Encourage word-of-mouth with incentives for sharing. > AI can personalize referral messages for higher trust and conversion. 1️⃣2️⃣Fulfilment Updates | Low Keep customers informed about their orders. > Let AI add a personal touch related to the product shipped. [Want to turn into an upsell opportunity: Karla is doing a great job here] The future of e-commerce is about conversations, not campaigns. Which flow or journey are you excited to tackle first? #conversationalai

���𝐨𝐮𝐫 𝐋𝐋𝐌 𝐢𝐬 𝐧𝐨𝐭 𝐛𝐫𝐨𝐤𝐞𝐧. 𝐘𝐨𝐮𝐫 𝐪𝐮𝐞𝐫𝐲 𝐩𝐫𝐞𝐩 𝐢𝐬. Here is what nobody tells you about why your RAG system keeps hallucinating 👇 Most engineers obsess over prompts and temperature settings. Meanwhile, their queries are doing this: - Arriving vague and contextless - Missing critical semantic variations - Trying to answer 5 questions at once - Getting routed to the wrong knowledge base 𝐓𝐡𝐞 𝐟𝐢𝐱? 𝐒𝐭𝐨𝐩 𝐭𝐫𝐞𝐚𝐭𝐢𝐧𝐠 𝐪𝐮𝐞𝐫𝐢𝐞𝐬 𝐥𝐢𝐤𝐞 𝐭𝐡𝐫𝐨𝐰𝐚𝐰𝐚𝐲 𝐢𝐧𝐩𝐮𝐭𝐬. Here is the actual architecture that separates production RAG from toy demos: 𝟏. 𝐐𝐮𝐞𝐫𝐲 𝐑𝐞𝐰𝐫𝐢𝐭𝐢𝐧𝐠 Turn "API not working" into "authentication failure modes in REST endpoints" One captures intent. The other actually retrieves useful context. 𝟐. 𝐐𝐮𝐞𝐫𝐲 𝐄𝐱𝐩𝐚𝐧𝐬𝐢𝐨𝐧 Your vector DB does not know that "LLM" and "large language model" mean the same thing. Add variants. Boost recall. Stop missing obvious matches. 𝟑. 𝐐𝐮𝐞𝐫𝐲 𝐃𝐞𝐜𝐨𝐦𝐩𝐨𝐬𝐢𝐭𝐢𝐨𝐧 "How do I fine-tune Llama 3 for customer support and deploy it cost-effectively?" That is not one query. That is three. Break it. Parallelize it. Actually answer it. 𝟒. 𝐐𝐮𝐞𝐫𝐲 𝐀𝐠𝐞𝐧𝐭𝐬 This is where it gets interesting. Before you touch your retriever: - Analyze intent - Route intelligently  - Validate what came back - Decide if you even have enough to generate 𝟓. 𝐓𝐡𝐞 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐋𝐚𝐲𝐞𝐫 𝐄𝐯𝐞𝐫𝐲𝐨𝐧𝐞 𝐒𝐤𝐢𝐩𝐬 Weak context? → Loop back and refine Strong context? → Generate with confidence Incomplete? → Don't hallucinate. Go get more. 𝐇𝐞𝐫𝐞 𝐢𝐬 𝐭𝐡𝐞 𝐭𝐡𝐢𝐧𝐠: The best LLM systems don't start with "write a better prompt." They start with "did we even ask the right question?" Real talk: What breaks first in your system? - Query rewriting catching garbage input? - Retrieval returning irrelevant chunks?   - Orchestration making the wrong routing call? 𝐃𝐫𝐨𝐩 𝐲𝐨𝐮𝐫 𝐰𝐚𝐫 𝐬𝐭𝐨𝐫𝐢𝐞𝐬 𝐛𝐞𝐥𝐨𝐰. 𝐋𝐞𝐭'𝐬 𝐝𝐞𝐛𝐮𝐠 𝐭𝐡𝐢𝐬 𝐭𝐨𝐠𝐞𝐭𝐡𝐞𝐫. 👇 ♻️ Repost this to help your network get started ➕ Follow Anurag(Anu) Karuparti for more PS: If you found this valuable, join my weekly newsletter where I document the real-world journey of AI transformation. ✉️ Free subscription: https://lnkd.in/esF52fm5 #AgenticAI #AIAgents #AILLMS