Chatbots for Customer Engagement

Most Retrieval-Augmented Generation (RAG) pipelines today stop at a single task — retrieve, generate, and respond. That model works, but it’s 𝗻𝗼𝘁 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁. It doesn’t adapt, retain memory, or coordinate reasoning across multiple tools. That’s where 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗥𝗔𝗚 changes the game. 𝗔 𝗦𝗺𝗮𝗿𝘁𝗲𝗿 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗳𝗼𝗿 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 In a traditional RAG setup, the LLM acts as a passive generator. In an 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 system, it becomes an 𝗮𝗰𝘁𝗶𝘃𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺-𝘀𝗼𝗹𝘃𝗲𝗿 — supported by a network of specialized components that collaborate like an intelligent team. Here’s how it works: 𝗔𝗴𝗲𝗻𝘁 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿 — The decision-maker that interprets user intent and routes requests to the right tools or agents. It’s the core logic layer that turns a static flow into an adaptive system. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 — Maintains awareness across turns, retaining relevant context and passing it to the LLM. This eliminates “context resets” and improves answer consistency over time. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗟𝗮𝘆𝗲𝗿 — Divided into Short-Term (session-based) and Long-Term (persistent or vector-based) memory, it allows the system to 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲. Every interaction strengthens the model’s knowledge base. 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗟𝗮𝘆𝗲𝗿 — The foundation. It combines similarity search, embeddings, and multi-granular document segmentation (sentence, paragraph, recursive) for precision retrieval. 𝗧𝗼𝗼𝗹 𝗟𝗮𝘆𝗲𝗿 — Includes the Search Tool, Vector Store Tool, and Code Interpreter Tool — each acting as a functional agent that executes specialized tasks and returns structured outputs. 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗟𝗼𝗼𝗽 — Every user response feeds insights back into the vector store, creating a continuous learning and improvement cycle. 𝗪𝗵𝘆 𝗜𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 Agentic RAG transforms an LLM from a passive responder into a 𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗲𝗻𝗴𝗶𝗻𝗲 capable of reasoning, memory, and self-optimization. This shift isn’t just technical — it’s strategic It defines how AI systems will evolve inside organizations: from one-off assistants to adaptive agents that understand context, learn continuously, and execute with autonomy.

Last week, I shared how Gen AI is moving us from the age of information to the age of intelligence. Technology is changing rapidly and the way customers shop and buy is changing, too. We need to understand how the customer journey is evolving in order to drive customer connection today. That is our bread and butter at HubSpot - we’re deeply curious about customer behavior! So I want to share one important shift we’re seeing and what go-to-market teams can do to adapt. Traditionally, when a customer wants to learn more about your product or service, what have they done? They go to your website and explore. They click on different pages, filter for information that’s relevant to them, and sort through pages to find what they need. But today, even if your website is user-friendly and beautiful, all that clicking is becoming too much work. We now live in the era of ChatGPT, where customers can find exactly what they need without ever having to leave a simple chat box. Plus, they can use natural language to easily have a conversation. It's no surprise that 55% of businesses predict that by 2024, most people will turn to chatbots over search engines for answers (HubSpot Research). That’s why now, when customers land on your website, they don’t want to click, filter, and sort. They want to have an easy, 1:1, helpful conversation. That means as customers consider new products they are moving from clicks to conversations. So, what should you do? It's time to embrace bots. To get started, experiment with a marketing bot for your website. Train your bot on all of your website content and whitepapers so it can quickly answer questions about products, pricing, and case studies—specific to your customer's needs. At HubSpot, we introduced a Gen AI-powered chatbot to our website earlier this year and the results have been promising: 78% of chatters' questions have been fully answered by our bot, and these customers have higher satisfaction scores. Once you have your marketing bot in place, consider adding a support bot. The goal is to answer repetitive questions and connect customers with knowledge base content automatically. A bot will not only free up your support reps to focus on more complex problems, but it will delight your customers to get fast, personalized help. In the age of AI, customers don’t want to convert on your website, they want to converse with you. How has your GTM team experimented with chatbots? What are you learning? #ConversationalAI #HubSpot #HubSpotAI

🔮 Design Patterns For AI Interfaces (https://lnkd.in/dyyMKuU9), a practical overview with emerging AI UI patterns, layout considerations and real-life examples — along with interaction patterns and limitations. Neatly put together by Sharang Sharma. One of the major shifts is the move away from traditional “chat-alike” AI interfaces. As Luke Wroblewski wrote, when agents can use multiple tools, call other agents and run in the background, users orchestrate AI work — there’s a lot less chatting back and forth. In fact, chatbot widgets are rarely an experience paradigm that people truly enjoy and can fall in love with. Mostly because the burden of articulating intent efficiently lies on the user. It can be done (and we’ve learned to do that), but it takes an incredible amount of time and articulation to give AI enough meaningful context for it to produce meaningful insights. As it turned out, AI is much better at generating prompt based on user’s context to then feed it into itself. So we see more task-oriented UIs, semantic spreadsheets and infinite canvases — with AI proactively asking questions with predefined options, or where AI suggests presets and templates to get started. Or where AI agents collect context autonomously, and emphasize the work, the plan, the tasks — the outcome, instead of the chat input. All of it are examples of great User-First, AI-Second experiences. Not experiences circling around AI features, but experiences that truly amplify value for users by sprinkling a bit of AI in places where it delivers real value to real users. And that’s what makes truly great products — with AI or without. ✤ Useful Design Patterns Catalogs: Shape of AI: Design Patterns, by Emily Campbell 👍 https://shapeof.ai/ AI UX Patterns, by Luke Bennis 👍 https://lnkd.in/dF9AZeKZ Design Patterns For Trust With AI, via Sarah Gold 👍 https://lnkd.in/etZ7mm2Y AI Guidebook Design Patterns, by Google https://lnkd.in/dTAHuZxh ✤ Useful resources: Usable Chat Interfaces to AI Models, by Luke Wroblewski https://lnkd.in/d-Ssb5G7 The Receding Role of AI Chat, by Luke Wroblewski https://lnkd.in/d8xcujMC Agent Management Interface Patterns, by Luke Wroblewski https://lnkd.in/dp2H9-HQ Designing for AI Engineers, by Eve Weinberg https://lnkd.in/dWHstucP #ux #ai #design

Hey Salespeople: Here is a collection of current use cases for AI in sales & CS: ** GenAI in Sales ** --> Draft messaging for personalized email outreach --> Generate post-call summaries with action items; draft call follow ups --> Provide real-time, in-call guidance (case studies; objection handling; technical answers; competitive response) --> Auto-populate and clean up CRM --> Generate & update competitive battlecards --> Draft RFP responses --> Draft proposals & contracts --> Accelerate legal review & red-lining (incl. risk identification) --> Research accounts --> Research market trends --> Generate engagement triggers (press releases; job postings; industry news; social listening; etc.) --> Conduct role-play --> Enable continuous, customized learning --> Generate customized sales collateral --> Conduct win-loss analysis --> Automate outbound prospecting -->Automate inbound response --> Run product demos --> Coordinate & schedule meetings --> Handle initial customer inquiries (chatbot; voice-bot / avatar) --> Generate questions for deal reviews --> Draft account plans ** Predictive AI in Sales ** --> Score leads & contacts --> Score /segment accounts (new logo) --> Automate cross-sell & upsell recommendations --> Optimize pricing & discounting --> Surface deal gaps / identify at-risk prospects --> Optimize sales engagement cadences (touch type; frequency) --> Optimize territory building (account assignment) --> Streamline forecasting (incl. opportunity probabilities; stage; close date) --> Analyze AE performance --> Optimize sales process --> Optimize resource allocation (incl. capacity planning) --> Automate lead assignment --> A/B test sales messaging --> Priortize sales activities ** GenAI in CS ** --> Analyze customer sentiment --> Provide customer support (chatbot; voice-bot / avatar; email-bot) --> Draft proactive success messaging --> Update & expand knowledge base (incl. tutorials, guides, FAQs, etc.) --> Provide multilingual support --> Analyze customer feedback to inform product development, support, and success strategies --> Summarize customer meetings; draft follow-ups --> Develop customer training content and orchestrate customized training --> Provide real-time, in-call guidance to CSMs and support agents --> Create, distribute, and analyze customer surveys --> Update CRM with customer insights --> Generate personalized onboarding --> Automate customer success touch-points --> Generate customer QBR presentations --> Summarize lengthy or complex support tickets --> Create customer success plans --> Generate interactive troubleshooting guides --> Automate renewal reminders --> Analyze and action CSAT & NPS ** Predictive AI in CS ** --> Predict churn; score customer health; detect usage anomalies, decision maker turnover, etc. --> Analyze CSM and support agent performance --> Optimize CS and support resource allocation --> Prioritize support tickets --> Automate & optimize support ticket routing --> Monitor SLA compliance

AI Agents can’t fix everything — not every task needs one Identifying the right stage is what unlocks real value... Multiple reports suggest the issue isn’t AI workflows themselves, but how people design them. Often, you don’t need a complex agentic system for simple tasks like summarizing HR documents. That’s why it’s crucial to pick the right solution instead of chasing the biggest one. 📌 To make it clearer, let’s walk through the 5 stages: 1. Script Chatbots - Human Dependency (~90–80%): Almost fully dependent on humans to script every single response. - Autonomy: No real intelligence — purely rule-based workflows. - Scalability: Scales linearly, but only for repetitive, predictable tasks. - Use Case: Simple automations like email replies, FAQs, or support ticket routing. 2. LLM Chatbots - Human Dependency (~70–60%): Reduced, but still needs supervision. - Autonomy: Contextual understanding with natural conversations — but no planning ability. - Scalability: Expands easily for large customer support operations. - Use Case: Customer-facing chatbots that can hold human-like conversations, but can’t take autonomous action. 3. Modern RPA - Human Dependency (~50–40%): Handles repeated, structured tasks with less manual input. - Autonomy: Contextual but still repetitive — can trigger scripts and execute tools when prompted. Scalability: Great for high-volume, process-driven workflows. - Use Case: Hiring document processing, invoice scanning, compliance checks. 4. Single Agentic AI - Human Dependency (~30–20%): Agents plan, use tools, and incorporate feedback with limited supervision. - Autonomy: Adaptive reasoning within a defined scope — memory + planning + tool use. - Scalability: Dynamic scaling for dedicated enterprise use-cases. - Use Case: Smart document retrieval, enterprise knowledge Q&A, semi-autonomous research. 5. Multi-Agentic AI - Human Dependency (~15–10%): Agents coordinate among themselves, requiring minimal human input. - Autonomy: Dynamic, multi-workflow execution with cross-agent collaboration. - Scalability: Designed for complex, large-scale enterprise automation. - Use Case: Interconnected coding agents, enterprise-wide orchestration, cross-department AI systems. 📌 The big takeaway: Those reports are right, which state that the problem is not models or workflows - it is people who are implementing them. In Week 1 of my latest cohort, I discussed why complex workflows are not always the solution and why choosing the right type of architecture is needed. This is because not every task needs a complex system, sometimes simpler approaches are more effective. Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents

SaaS startup founders have countless playbooks to guide them, but AI-enabled services founders are charting new territory. These pioneers combine AI with human expertise to deliver faster, better, and cheaper outcomes than legacy service providers. Many companies adopting this model have had compelling early traction. But here's the catch: founders are trying to force-fit traditional SaaS strategies onto these service-based businesses. There are some similarities but also major differences. We've identified 5 critical lessons for building an iconic AI-enabled service company. Consider this a contribution to the new playbook: 1) Bring on a domain expert early. They're even more critical than before In traditional SaaS, you're selling a product. In AI-enabled services, you're selling yourself. Domain authority isn't just important, it's existential. It also unlocks access to high-quality talent channels, which enables the rapid staffing that you may need while your AI is still maturing. 2) Beware Mirage PMF PMF is a different beast in AI-enabled services. Strong revenue growth and NDR can mask a lack of true AI enablement, i.e. "Mirage PMF." Real PMF in AI-enabled services requires proving you can scale non-linearly relative to your costs. To get there, your AI must quantifiably improve cost, quality, or speed—or ideally, all 3. 3) Develop partnerships early on — they can be a key growth accelerator. Incumbents offer immediate market credibility, established distribution, and access to proprietary datasets, which can be crucial early on while your data corpus is small. To take advantage, smart service startups are exploring partnership models that are well beyond the traditional SaaS revenue-share approach. 4) Leverage new pricing models — they can help unlock higher contract values. AI-enabled service contracts have two different models, each with unique benefits and risks: → Labor-Based: Priced by labor hours.  Guarantees early margins, but limits upside as automation scales. → Outcome-Based: Priced by delivered value. Value aligned and can unlock very high margins over time, but risks early profitability with nascent AI. We’ve found that it's typically best for AI-enabled service vendors to start with a labor-based approach while learning how to deliver their service. Just set clear timelines to transition to an outcome-based model. 5) It’s the demo, stupid! In this case, founders should borrow directly from the SaaS playbook and build a “wow” demo for their tech. Ditch the deck; a strong demo boosts customer confidence and accelerates sales conversations. If you’re exploring AI-enabled services, we’d love to learn alongside you. Share your thoughts—we’re all figuring out this new model together. P.S.- Thank you to Arjun Chopra, Medha Agarwal, Wayne Hu, James Currier, Zachary Bratun-Glennon, Wenz Xing, Nic Poulos, and Kent Goldman, for helping me put this together.

I was in a video meeting today with 15+ people and we invited ChatGPT to join. No, we didn't have one person hold up a phone ... it was a live meeting participant that everyone was able to interact and engage with in real-time. We pulled together this demo using Cloudflare Calls and OpenAI's new Realtime API support of WebRTC. (link to the demo in the comments). What this means in practice: applications built using Cloudflare Calls can now support multiple users simultaneously seeing and interacting with a voice or video AI. A few reasons this could be interesting ... In the not too distant future, every company will have a 'corporate AI' they can invite to their internal meetings that is secure, private and has access to their company data. Imagine being able to ask some of these questions in your meetings and have a live, audio response: "Hey ChatGPT, do we have any open jira tickets about this?" "Hey Company AI, who are the competitors in the space doing Y?" "AI, is XYZ a big customer? How much more did they spend with us vs last year?" That's now possible! Or if you build for consumers, you can now scale this to global, interactive livestreams and broadcasts. My colleague Ricky Robinett developed a fun Murder Mystery game for the demo ... imagine playing that with new friends all across the globe in real-time. Or a fully personalized HQ Trivia (iykyk) moderated by an AI about topics you are most interested in. I know everyone feels the pace of change is accelerating - I feel it too. But it's hard to not be excited about what is possible.

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

AI chatbots are a dime a dozen these days (or $15 / 1M output tokens if you’re using OpenAI). But building a valuable chatbot takes more than an OpenAI subscription.This story of how the data team at WHOOP used GenAI to democratize access to reliable insights is a masterclass in how to make a useful chatbot. According to Matt Luizzi, his team had “several hundred dashboards and all the typical sprawl you see in BI… Everyone’s creating things, nobody knows what’s being used or what’s correct… Depending on where you go, you may or may not get the right answer.” Matt’s team saw an AI chatbot as the perfect way to create a single source of truth that could be easily—and reliably—queried by his stakeholders. The first order of business? Getting their data quality in order. Here’s how they did it: Step 1. Re-architect their dbt project to improve documentation and accessibility. Step 2. Leverage lineage to deprecate dashboards that weren’t being used. Step 3. Define “golden questions” to audit the chatbot’s outputs. In the end, Matt and his team eliminated 80% of their existing dashboards, and implemented new data quality practices that improved not just the quality of reliability of their chatbot, but the reliability of their broader data platform as well. “Getting in the room and having conversations with the right stakeholders is half the battle,” says Matt. “For us, being able to showcase the fact that we’re able to not only create dashboards and run A/B tests but actually build tooling that’s serving the business — that’s gotten us a lot of value in the organization.” Check out the full story via link in the comments to get all the insights and find out what’s next for the data team at WHOOP.

What’s missing in conversational AI? The ability to plan responses across turns strategically to achieve goals. Most conversational AIs: • Focus on single responses • Lack strategic, long-term goals • Miss out on real human connection New UC Berkeley publications are contributing to the game: 𝗤-𝗦𝗙𝗧 (Q-Learning via Supervised Fine-Tuning) • Adapts Q-learning to train language models • Adds long-term planning directly into responses • Helps AIs respond with strategy, not just reaction 𝗛𝗶𝗻𝗱𝘀𝗶𝗴𝗵𝘁 𝗥𝗲𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 • Replays past conversations to find better responses • Learns from the past to improve future replies • Guide smarter conversational strategies Applications? • 𝗠𝗲𝗻𝘁𝗮𝗹 𝗛𝗲𝗮𝗹𝘁𝗵 𝗦𝘂𝗽𝗽𝗼𝗿𝘁: Builds trust, helping users feel heard. • 𝗘-𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲: Remembers past chats to close sales. • 𝗖𝗵𝗮𝗿𝗶𝘁𝘆: Guides conversations with empathy, boosting donations. Together, these methods will allow CAI to be goal-oriented, plan strategically, adapt, and connect with users.