Interactive Chatbot Implementations

I’ve been experimenting with ways to bring AI into the everyday work of telco — not as an abstract idea, but as something our teams and customers can use. On a recent build, I created a live chat agent I put together in about 30 minutes using n8n, the open-source workflow automation tool. No code, no complex dev cycle — just practical integration. The result is an agent that handles real-time queries, pulls live data, and remembers context across conversations. We’ve already embedded it into our support ecosystem, and it’s cut tickets by almost 30% in early trials. Here’s how I approached it: Step 1: Environment I used n8n Cloud for simplicity (self-hosting via Docker or npm is also an option). Make sure you have API keys handy for a chat model — OpenAI’s GPT-4o-mini, Google Gemini, or even Grok if you want xAI flair. Step 2: Workflow In n8n, I created a new workflow. Think of it as a flowchart — each “node” is a building block. Step 3: Chat Trigger Added the Chat Trigger node to listen for incoming messages. At first, I kept it local for testing, but you can later expose it via webhook to deploy publicly. Step 4: AI Agent Connected the trigger to an AI Agent node. Here you can customise prompts — for example: “You are a helpful support agent for ViewQwest, specialising in broadband queries – always reply professionally and empathetically.” Step 5: Model Integration Attached a Chat Model node, plugged in API credentials, and tuned settings like temperature and max tokens. This is where the “human-like” responses start to come alive. Step 6: Memory Added a Window Buffer Memory node to keep track of context across 5–10 messages. Enough to remember a customer’s earlier question about plan upgrades, without driving up costs. Step 7: Tools Integrated extras like SerpAPI for live web searches, a calculator for bill estimates, and even CRM access (e.g., Postgres). The AI Agent decides when to use them depending on the query. Step 8: Deploy Tested with the built-in chat window (“What’s the best fiber plan for gaming?”). Debugged in the logs, then activated and shared the public URL. From there, embedding in a website, Slack, or WhatsApp is just another node away. The result is a responsive, contextual AI chat agent that scales effortlessly — and it didn’t take a dev team to get there. Tools like n8n are lowering the barrier to AI adoption, making it accessible for anyone willing to experiment. If you’re building in this space—what’s your go-to AI tool right now?

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

Building a Smart Telegram AI Assistant/Agent! 🤖🎙️ I built Nova, an AI-powered companion bot (AI Agent) for Telegram. What can Nova do? ✅ Engage in text-based conversations ✅ Convert speech to text & text to speech ✅ Generate AI-powered images ✅ Support local & cloud-based AI models Nova is powered by OpenAI's GPT-4, DALL·E, and Kokoro (ONNX) for local TTS. The system is designed to be flexible—you can integrate vector databases like Qdrant or Milvus for memory and retrieval-augmented responses. Tech Stack & Implementation: 📌 Telegram Bot API + Webhooks 📌 OpenAI for LLM and Image Generation 📌 Kokoro (ONNX) for Local TTS 📌 Groq API for Speech-to-Text 📌 Expandable with a Vector DB for RAG This project showcases how AI-powered chatbots can be built and customized for different use cases. Whether for personal assistants, knowledge retrieval, or customer support, the possibilities are endless! You can check out the source code and watch the Youtube video that I explain how it works. Github: https://lnkd.in/eRz5Rpex Youtube: https://lnkd.in/eJvmQuVH

We went from zero to 10,000 chatbot conversations per month in 90 days. No consultants. No six-month roadmap. Here's the exact process. Step 1: Define the scope (2 days). Pick one use case. We chose lead qualification. Document 10-15 common questions. Create qualification criteria. Step 2: Choose the platform (3 days). Evaluated 5 platforms. Picked Intercom. Criteria: Easy to build, CRM integration, under $500/month. The platform matters less than shipping fast. Step 3: Build conversation flows (5 days). Map the decision tree. We built 3 paths: Product demo request. Pricing inquiry. Technical support. Each path ends with booking or contact collection. Step 4: Write the copy (3 days). Write like a human. Short sentences. One question at a time. Casual tone beat professional by 23%. Step 5: Set up integrations (7 days). Connected to: CRM (HubSpot). Calendar (Calendly). Slack notifications. Longest step due to API limits. Step 6: Build knowledge base (4 days). Documented 25 FAQ responses. Pricing, features, timelines, support. Short, scannable answers only. Step 7: Test internally (5 days). 8 team members tested every path. Found and fixed: Typo handling issues. Dead-end conversation path. Calendar integration bugs. Step 8: Soft launch (7 days). Enabled for 10% of traffic. Monitored every conversation. Week 1 results: 47 conversations. 34% completion rate. 8% booking rate. Step 9: Iterate based on data (ongoing). Analyzed drop-offs. 62% abandoned after third question. Fix: Shortened from 7 questions to 4. New results: 58% completion rate. 19% booking rate. Step 10: Scale to 100%. After two weeks, enabled for all traffic. Month 1: 1,200 conversations. Month 2: 4,800 conversations. Month 3: 10,000 conversations. 23% of conversations book demos without human involvement. Total timeline: 90 days from start to 10K conversations. What we learned. Speed beats perfection. Ship in 30 days, iterate weekly. One use case done well beats ten done poorly. Watch drop-off points, fix them fast. Where are you in this process? Found this helpful? Follow Arturo Ferreira and repost ♻️

We’ve (w/ Navdeep Jaitly) developed a framework, SAGE, that enhances emotional dialogue generation models by integrating “macro actions” into conversational agents, with a particular focus on building emotionally intelligent chatbots. At the core of SAGE is the State-Action Chain (SAC), which introduces latent variables to encapsulate emotional states and conversational strategies between dialogue turns. This allows for coarse-grained control over dialogue progression while preserving natural, engaging interaction patterns—crucial for emotionally resonant conversations. Recent advances in large language models excel in task-oriented applications, but emotional dialogue remains challenging. SAGE proposes to solve this problem by a novel fine-tuning strategy, where data is augmented with latent variables capturing emotional states (e.g., empathy) and conversational dynamics (e.g., trust building). For training these latent variables are infilled into the data corpus by a language model that assesses the entire conversation. During inference these variables are generated before each response and enable proactive, interactive multi-turn dialogues. For instance, an AI therapist balances empathy and prompting to encourage disclosure, while a fitness coach adapts tone based on energy levels, all while keeping interactions natural. We use a self-improvement pipeline that leverages dialogue tree search, LLM-based reward modeling, and targeted fine-tuning to optimize conversational trajectories. This approach enables models to navigate diverse conversational pathways and refine their performance based on the most effective strategies. Looking ahead, we hope to use Reinforcement Learning to steer multi-turn dialogues through these “macro actions.” The discrete nature of our latent variables facilitates search-based strategies and provides a foundation for future applications of reinforcement learning in dialogue systems, allowing learning to occur at the state level rather than the token level —perfect for the sparse rewards of emotional conversations. Check out our full paper for a deeper dive! Paper: https://lnkd.in/g6r83Gps Code/Model: https://lnkd.in/gQKK2_NK #MachineLearning #AI #DialogueSystems #EmotionalIntelligence #NLP

🚀 Building a Memory-Enabled Chatbot on Databricks with MemGPT-Inspired Architecture 🚀 Imagine a chatbot that remembers every conversation, picking up precisely where it left off each time. 📈 This level of personalization is now achievable by leveraging Databricks, Delta Lake, and a multi-tiered memory inspired by the visionary work of Charles Packer and Sarah Wooders et al in "MemGPT: Towards LLMs as Operating Systems." 💡 🔹 Persistent Memory with Delta Lake: Store conversations in Delta tables, creating a robust “long-term memory” for each user. 🔹 Real-Time Context with Main Memory: Maintain recent exchanges in a lightweight memory queue, providing seamless short-term recall. 🔹 Memory Recall on Demand: Retrieve user-specific context with keyword-based memory recall, giving the chatbot a remarkable ability to resume conversations effortlessly. 🔹 Databricks Model Serving: Deploy this memory-enabled chatbot as a scalable MLflow model, accessible via REST API for real-time user interactions. 🔥 This guide takes you through each step to bring your chatbot to life, from memory storage and recall functions to seamless deployment on Databricks. Transform the way you engage users! #AI #Chatbots #MemoryEnabled #DeltaLake #Databricks #MemGPT #ConversationalAI #CustomerExperience #MLflow #DataScience

Your AI chatbot forgets everything when a user switches between messaging platforms. Here's how to fix that. Most chatbots treat each channel as a separate world. A user shares a photo on WhatsApp, then asks about it on Instagram. The agent has no idea what they're talking about. I built a multichannel AI agent that maintains persistent memory across messaging platforms using Amazon Bedrock AgentCore. One deployment, shared identity, full context. The demo uses WhatsApp and Instagram, but the architecture extends to any messaging channel: Slack, Telegram, Discord, SMS. How it works: → Unified identity: deterministic user IDs per channel (wa-user-{phone}, ig-user-{sender_id}) mapped to a single actor in AgentCore Memory. Adding a new channel means adding one more ID pattern. → Two memory layers: short-term (conversation turns with TTL) and long-term (extracted facts, preferences, summaries that persist indefinitely) → Multimodal processing: text, images (Claude vision), voice (Amazon Transcribe), video (TwelveLabs), and documents → Smart buffering: DynamoDB Streams with 10-second tumbling windows batch rapid messages before invoking the agent The architecture uses three AWS CDK stacks: Stack 00 → AgentCore Runtime + memory layer Stack 01 → WhatsApp (AWS End User Messaging) or Stack 02 → Multi-channel API Gateway (WhatsApp + Instagram + any new channel) Users can even link their accounts across platforms through conversation. The agent merges identities in a unified DynamoDB table. The core memory and identity layers are channel-agnostic. WhatsApp and Instagram are the first two integrations, but the pattern is designed to grow. Full code and deployment guide are open source. Each stack deploys in about 15 minutes #AI #Chatbot #Agents #AWS #LLM

Just closed a major enterprise deal against a competitor who pitched at 1/3 of our price. Here's the inside scoop on how we transformed what started as a "chatbot search" into a complete GTM automation win: Here's what most vendors miss: Chatbots alone are just the tip of the iceberg. The real magic happens when you connect visitor intelligence to autonomous GTM actions. Reframing the Conversation : The prospect (a well-funded services company with 400+ employees) initially came to us looking for an AI chatbot and had done their homework. During our first demo, we showed them something dramatically different: Their current flow: Visitor chats with bot Lead gets logged Sales team manually follows up (maybe) Data sits in silos What we demonstrated live: AI chatbot engages visitor Platform instantly identifies the company AI agents then automatically: Create enriched company profiles Launch tailored outbound sequences Book meetings via voice/email Update their CRM in real-time Alert relevant teams in Slack/Teams The game changer? When we were able to demonstrate to their team the ways our AI agents were already acting autonomously based on chatbot interactions from other companies in their industry - booking meetings while competitors are still manually working over chat leads. Inbound Intelligence: Knows which companies are engaging (or not engaging) with the chatbot Analyze conversation patterns for intent signals Triggers targeted workflows by interaction type Routes high-value prospects to live sales teams Outbound Automation: AI agents autonomously prospect similar companies Creates targeted account lists by patterns of engagement with the chatbot Launches multichannel outreach (voice, email, LinkedIn) Syncs all activity back to their CRM The "Aha" Moment Instead of configuring a chatbot for them, we walked them through building a complete workflow in the demo: Key Takeaway: When you can show how a "simple chatbot" can become an autonomous revenue engine--price becomes irrelevant. The discussion shifts from "Do we really need another chat tool?" to "How soon can we put this complete GTM automation out there?"

From Enterprise AI to Open Source Agentic Chatbots! 🚀 Inspired by building an Agentic AI chatbot using Google Cloud tools (like Gemini & function calling) at the recent #GCloudCreate event in Chicago, I challenged myself: could I replicate similar capabilities for a small business (like a local bakery 🥐) using only open-source/free tools? Challenge accepted! Here's the stack I used: Database: PostgreSQL (deployed locally, managed with pgAdmin, populated with Python's Faker library) Backend: Flask API AI Model: 7B DeepSeek-r1 deployed locally via Ollama The Core Challenge: Local LLMs often lack built-in function calling. So, I implemented it from scratch to give the chatbot agentic capabilities! 💪 Frontend: Next.js + Tailwind CSS + Shadcn UI for a clean interface. Comms: Simple REST APIs for chat interactions. This project was a fantastic exercise in bridging the gap between powerful enterprise features and accessible open-source solutions. It shows that sophisticated AI agent logic can be built without relying solely on proprietary cloud services. Want to see how the custom function calling works or explore the code? It's all on GitHub! Feedback and collaboration are welcome. [https://lnkd.in/gE2kthzX] #Google #Gemini #GoogleCloud #AI #AgenticAI #LLM #OpenSource #DeepSeek #Ollama #Python #Flask #JavaScript #NextJS #TailwindCSS #SQL #PostgreSQL #FunctionCalling #SoftwareDevelopment #Chatbot #DIYAI

🚀 Want to build your own ChatGPT-like application that works with your private data? Here is an open source project from Microsoft. 💡 This repository showcases a complete implementation of a chat application powered by Azure OpenAI and Azure AI Search. It's perfect for both learning and as a starter deployment scenario. 🎯 Why this matters: • For Data Scientists: Dive into production-grade RAG implementation with proper vector search integration • For Developers: Get a full-stack Python application with React frontend that you can use as a reference • For IT Professionals: Deploy a secure, private chat solution for your organization's internal documentation 🛠️ Key Features: • Multi-turn chat & single-turn Q&A capabilities • Citation support with document references • Built-in UI for experimenting with different configurations • Support for multiple document formats • Optional GPT-4V integration for image analysis • Production-ready monitoring with Application Insights • Enterprise-ready with Microsoft Entra integration 🆓 Best part? It comes with sample data, so you can deploy and test immediately, even using Azure's free tier resources! 🔗 Link in comments. #AzureOpenAI #MachineLearning #AI #DataScience #SoftwareDevelopment #Microsoft #RAG #GenerativeAI #AzureCloud #OpenSource P.S. Star ⭐️ the repo if you find it useful! It already has 6.2k stars and growing!