Multichannel Customer Support Systems

Most voice AI systems ignore 90% of the world’s languages. Why? Because data is scarce. Meta’s new Omnilingual Speech Recognition suite breaks that cycle. Existing models are trained on internet-rich languages and that dominates the research loop. Omnilingual can transcribe speech in over 1,600 languages, including 500 that no speech AI has ever supported. This is a glimpse into the next wave of AI: models that don’t assume the internet is the world. Highlights: – Transcription accuracy under 10% error for 78% of supported languages – In-context learning: adapt to new languages with just a few audio clips – Fully open-source: models, data, and the 7B Omnilingual w2v 2.0 foundation This isn’t about just recognizing speech. It’s about who gets included. If we can build models that work across dialects, cultures, and scarce data, the future of voice AI in enterprise, customer service, and global markets changes fast. - Announcement blog: https://go.meta.me/ff13fa - Download Omnilingual ASR: https://lnkd.in/g3w4FqY3 - Try the Language Exploration Demo: https://lnkd.in/gVzrcdbd - Try the Transcription Tool: https://lnkd.in/gRdZuZqP - Read the Paper: https://lnkd.in/giKrvniC

60% of support tickets are repetitive. And, customers expect immediate responses. That creates pressure on teams and frustration for customers. This is why support is one of the most practical and now proven places to apply AI. AI can handle common, repeat questions instantly, in your tone, using your knowledge base and CRM data. That frees up humans to focus on situations that require judgment, empathy, and creativity. One of our customers, The Knowledge Society (TKS) Society, did exactly that. Every enrollment season, they saw a surge of messages across email, Facebook Messenger, and WhatsApp. The busiest time of year was also the most overwhelming for their team. They implemented the Customer agent to answer common enrollment questions around the clock. Today, close to 80% of inquiries are handled automatically. Their team now spends more time on complex conversations and less time copying and pasting the same answers. The (ISSA) International Sports Sciences Association also scaled with Customer Agent. They were managing multiple support channels across different tools. The experience was fragmented for their team and inconsistent for customers. By introducing an AI agent to handle repetitive questions across channels, they cut response times in half and created a more consistent experience. Over 8,000 companies are already using HubSpot’s Customer Agent, with resolution rates above 67%. This is the real opportunity with AI in support.

75% of internet users don't speak English. Yet most B2B sites are English-only. And then we wonder why international expansion feels complicated. It’s not complicated. It’s just operationally painful. For years, going global meant: Translator. Developer. SEO specialist. Weeks of coordination. So companies postponed it. But if you’re on WordPress, Shopify, Webflow, Squarespace or almost any CMS… You can make your site multilingual in minutes. That’s what I found interesting about Weglot. Install it once, and your site is instantly translated with AI. Behind the scenes, it also handles: • Language-specific URLs • Hreflang tags • Translated metadata • Automatic updates when content changes In other words, multilingual SEO without turning it into a dev project. You can refine key pages manually, control tone, and manage everything from one dashboard. Which changes the decision entirely. Instead of asking: “Are we ready to expand?” You ask: → “Which market should we test next?” If your website only speaks one language, you are not limiting ambition. You are limiting access. And access is leverage. Want to see how this works in practice? Try it here: https://lnkd.in/enXEbGFS If removing the technical friction made expansion easy… Which country would you test first?

You don’t need more money, staff or time, you need the right tools. Just because of AI knowledge & Execution is on next level. Good part is its level playing field for everyone, anybody can build & scale. Having co-founded and scaled VehicleCare , I've learned that the right tools can accelerate your journey. If I were starting a new, these are the AI tools I'd leverage: 1. Ideation – ChatGPT By OpenAI Quickly generate and refine product ideas, features, and user personas. 🔗 chatgpt.com 2. Design – Gamma Design presentations, social media posts, and websites effortlessly, without a dedicated designer. 🔗 gamma.app 3. Software Development – Lovable Transform plain English descriptions into full-stack applications swiftly. 🔗 lovable.dev 4. Customer Support – Chatbase Deploy an AI agent from day one to handle customer inquiries efficiently. 🔗 chatbase.co 5. Documentation & Content – Notion Organize specifications, SOPs, and your knowledge base effectively. 🔗 notion.com 6. Scheduling – Cal.com, Inc. Simplify meeting bookings with automatic calendar synchronization. 🔗 cal.com In today's landscape, building momentum is more accessible than ever. With the right AI tools, you can reduce dependencies, cut costs, and focus on delivering value to your customers.

Generative AI is transforming how people learn and work—but only if it speaks your language. Most AI features are still built English-first, and “just translate it” rarely delivers a great experience. Idioms, domain-specific terms, and cultural context often get lost when translation is treated as an afterthought. In a recent engineering blog, Udemy shares how they approached this challenge and built a framework for localizing generative AI features from the ground up. The team outlines three strategies along a spectrum of complexity. At one end is a Translation Management System (TMS): translate user input to English, run it through the LLM, then translate the output back. It’s fast to ship and offers broad coverage, but comes with tradeoffs in latency and nuance. At the other end is a Multilingual LLM System (MLS), where the model processes and generates directly in each language using multilingual prompting, cross-lingual embeddings, and optional fine-tuning. This delivers higher quality, but is more complex to build. In between sits a hybrid approach—routing simpler queries through TMS and high-stakes interactions through multilingual models—allowing teams to move fast while investing deeply where it matters most. What stands out is how they treat localization as a platform problem: core interfaces are designed to switch between TMS and MLS without major rewrites. Safety and compliance are validated per language, rather than assumed to generalize from English. And every new language follows a repeatable playbook: start with TMS, learn from real usage, then decide whether it’s worth upgrading to a fully multilingual system. The results are impressive: the team was able to go from concept to production for the Japanese market in under three months, and adding a new language now takes less than 25% of the original effort. The takeaway? Localization isn’t a tax on your AI roadmap—it’s a multiplier. Start broad, go deeper where the data justifies it, and design your system so scaling globally becomes the default. #DataScience #DecisionMaking #LLM #Translation #Platform #SnacksWeeklyonDataScience – – –  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/gFYvfB8V    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/ggZxBDYj

TymeBank (South Africa) and Moniepoint (Nigeria) have achieved unicorn status with valuations of $1.5 billion and over $1 billion, respectively, by blending digital banking with physical touchpoints. This hybrid model caters to Africa’s 90% cash-based economy and unbanked populations, overcoming barriers like unreliable internet and low trust in online-only systems. Together, these fintechs now serve over 25 million users, redefining what scaling financial inclusion looks like in emerging markets. SO WHAT TymeBank's partnership with supermarkets like Pick n Pay has enabled the deployment of over 1,000 kiosks and 15,000 retail points across South Africa, allowing it to grow to 15 million users. Moniepoint’s 200,000 agents, acting as human ATMs, bridge the gap in Nigeria, where only 16 ATMs per 100,000 adults exist, supporting over 10 million users. Both companies are expanding into Asia and broader African markets, leveraging $360 million in recent funding rounds to replicate their models. A digital-only strategy, like that pursued by Kuda (valued at $500 million), may be more scalable in regions with higher internet penetration and digital trust. However, it risks limiting market reach in areas where 43% or fewer have reliable connectivity. Think about it this way: the hybrid model embraces complexity to unlock growth in underserved regions. Could a hybrid approach redefine banking for other industries or regions, or is this model uniquely suited to Africa’s fintech challenges? What’s your take on scaling such a model sustainably? #fintech

What are the building blocks behind autonomous AI agents with #𝗔𝗜𝗔𝗴𝗲𝗻𝘁𝘀𝗟𝗮𝘆𝗲𝗿𝗲𝗱𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 and 𝗧𝗼𝗼𝗹𝘀 driving them? Understanding the building blocks behind #autonomousAIagents is essential for any professional working at the intersection of AI agents, and product development. This layered architecture provides a structured roadmap, from foundational models to governance — helping us build safer, more powerful, and context-aware #AIagents. Here’s a quick breakdown of each layer and the tools driving them. 🔹 𝗟𝗮𝘆𝗲𝗿 𝟭: 𝗟𝗟𝗠 (𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿) This is the reasoning and language core. Large Language Models like GPT-4, Claude, Mistral, and LLaMA form the foundation for text generation and understanding. 𝗧𝗼𝗼𝗹𝘀: OpenAI GPT-4, Claude, Cohere, Gemini, LLaMA, Mistral. 🔹 𝗟𝗮𝘆𝗲𝗿 𝟮: 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝘀𝗲 (𝗞𝗕) Provides external context (structured/unstructured) for better decisions. 𝗧𝗼𝗼𝗹𝘀: Chroma, Pinecone, Redis, PostgreSQL, Weaviate. 🔹 𝗟𝗮𝘆𝗲𝗿 𝟯: 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚) Retrieves relevant data before generation to improve factual accuracy. 𝗧𝗼𝗼𝗹𝘀: LangChain RAG, LlamaIndex, Haystack, Unstructured .io. 🔹 𝗟𝗮𝘆𝗲𝗿 𝟰: 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲 Where users and agents meet —via text, voice, or tools. 𝗧𝗼𝗼𝗹𝘀: OpenAI Assistant API, Streamlit, Gradio, LangChain Tools, Function Calling. 🔹 𝗟𝗮𝘆𝗲𝗿 𝟱: 𝗘𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻𝘀 Agents connect with CRMs, APIs, browsers, and other services to take action. 𝗧𝗼𝗼𝗹𝘀: Zapier, Make .com, Serper API, Browserless, LangChain Agents, n8n. 🔹 𝗟𝗮𝘆𝗲𝗿 𝟲: 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗟𝗼𝗴𝗶𝗰 & 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆 The brain of autonomous agents — task planning, decision-making, execution. 𝗧𝗼𝗼𝗹𝘀: AutoGen, CrewAI, MetaGPT, LangGraph, Autogen Studio. 🔹 𝗟𝗮𝘆𝗲𝗿 𝟳: 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 & 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 Ensures traceability, ethical alignment, and debugging. 𝗧𝗼𝗼𝗹𝘀: Helicone, LangSmith, PromptLayer, WandB, Trulens. 🔹 𝗟𝗮𝘆𝗲𝗿 𝟴: 𝗦𝗮𝗳𝗲𝘁𝘆 & 𝗘𝘁𝗵𝗶𝗰𝘀 Builds trust by preventing toxic, biased, or unsafe behavior. 𝗧𝗼𝗼𝗹𝘀: Azure Content Filter, OpenAI Moderation API, GuardrailsAI, Rebuff. This architecture is more than just a stack — it’s a blueprint for responsible AI innovation. Whether you're building internal copilots, autonomous agents, or customer-facing assistants, understanding these layers ensures reliability, compliance, and contextual intelligence.

Channel Management : In FMCG In the FMCG (Fast-Moving Consumer Goods) sector, channel management are especially crucial to ensuring that products reach consumers efficiently & distribution channels are optimized for speed, reach & profitability. I. Channel Leveling in FMCG 1. Segment Channels Based on Market Reach Primary Channels: Typically involve wholesalers, distributors, and large retailers who help cover broad market needs. These are often responsible for volume sales. Secondary Channels: This includes smaller retailers stores & direct-to-consumer online channels. Secondary channels help penetrate regional and local markets, catering to specific customer segments. 2. Define Roles & Responsibilities for Each Channel Distributor Responsibilities: Distributors often cover specific geographic areas and are responsible for handling stock, logistics & replenishment. Retailer Responsibilities: Retailers provide end-customer access, display & promote products, especially in high-traffic areas. 3. Minimize Channel Conflicts Price Consistency: Avoid price disparities across channels, which can lead to customer dissatisfaction and partner conflicts. Set standard pricing guidelines to maintain consistency. Geographic & Market Exclusivity: Consider giving distributors exclusivity in certain regions or channels to reduce intra-channel competition, which can improve focus & accountability. 4. Align Channel Incentives Volume Discounts for Wholesalers/Distributors: Provide volume-based discounts or rebates for bulk orders, which encourage large purchases and improve economies of scale. Retail Display and Marketing Support: Offer incentives or co-marketing funds to retailers who meet display & stocking guidelines, as well as promotional goals. II. Performance Management in FMCG Channels Major Elements 1. Set Key Performance Indicators (KPIs) Sales Volume: Measures the total product units sold through each channel. Distribution Reach and Market Penetration: Measures how widely products are available across regions, especially important for FMCG. Stock Turnover Rate: Tracks how quickly inventory moves through each channel to minimize holding costs & avoid stockouts. 2. Channel-Specific Performance Reviews Monthly or Quarterly Reviews: Review each channel’s performance regularly to evaluate sales volume, distribution reach & other KPIs. Scorecards for Key Partners: Develop scorecards with metrics for each channel partner, and share performance results to encourage transparency & improvement. 3. Incentives Based on Performance Performance-Based Rebates: Offer rebates or bonuses for hitting sales targets, increasing reach, or improving stock turnover. This is particularly effective with high-volume FMCG distributors. 4. Continuous Feedback Mechanism Partner Feedback: Collect regular feedback from distributors, retailers, and direct channels to understand challenges they face, and use this data to improve channel support and performance.

If you try to optimize your support org in the first 30 days, you will fail. Here’s what the best operators do instead Every company wants the same thing from a new Support Leader: “Lower response times.” “Cut cost per ticket.” “Fix escalations.” “Do it fast.” But here’s the part no one says out loud: You cannot fix a system you don’t understand. And the first 30 days are the only window you get to learn how things actually work — not how the dashboards pretend they work. Here’s what elite support leaders actually do in Month 1 👇 1️⃣ Diagnose the math before touching the machine (Week 1–2) No optimizations. No new processes. Just hard data. You map: - Ticket distribution by channel → where volume explodes vs. where agents sit idle - Handle time outliers → the workflows that drain hours without anyone noticing - Escalation patterns → complex issues vs. broken SOPs - Backlog anatomy → which categories stall and why - ...and so on This tells you exactly where the system leaks, instead of guessing based on vibes and dashboards. 2️⃣. Go straight to the source: Customers + Agents (Week 1–4) This is where most leaders fail. They rely on reports instead of reality. 🙋 Customers: Talk to: ▪️ 10 happiest ▪️ 10 most frustrated Ask: - Where did support exceed expectations? - Where did it break? - What did they expect that never happened? Then shadow live conversations. What people say and what they struggle with are never the same. 🧑💻 Agents: Sit with them during real tickets. Watch their workflow. You’ll spot: - 6–10 second delays repeated hundreds of times per day - Context scattered across 4 tools - Approvals that add 24 hours to simple resolutions - Escalations happening simply because agents lack access or clarity This is where 80% of your future wins come from. 3️⃣. Quick wins that actually move numbers (Inside 30 days) Not “culture boosts.” Not “team energy.” Real operational upgrades. - Fix routing logic → engineers stop receiving agent-resolvable tickets - Collapse redundant macros/forms → remove 30–60 seconds per ticket - Update the KB → match the 20 questions customers ask every day - Clarify escalation rules → fewer ping-pong cycles - Remove one blocker/week → access issues, approval delays, tool friction These improvements compound fast. 4️⃣ Reset expectations like a leader (Not a firefighter) If you accept the pressure to “fix support fast,” you inherit a problem you cannot win. The message you need to set with leadership is simple: “Give me 30 days to diagnose. Give me 60 days to fix workflows. Then I’ll give you the metrics you want.” Top operators don’t chase better numbers. They chase better systems. The numbers follow. TAKEAWAY Your first 30 days aren’t for speed, cost-cutting, or shiny improvements. They’re for: uncovering system truth, removing friction, and building a foundation you can scale. Everything else is noise.

Customer support becomes chaotic the moment a business starts receiving messages from multiple channels. Email WhatsApp Instagram Website chat Most business owners end up handling each channel separately. That’s where context starts breaking. The same customer might ask the same question in two places and get two different answers. Recently I built a unified support system for a client using n8n. Instead of treating every channel as a separate conversation, every incoming message now flows into one automation pipeline regardless of where it came from. Inside the workflow: • the message is normalised into a single format • duplicate requests are detected automatically • sensitive keywords are flagged before anything runs • the customer's profile, order history, and previous tickets are retrieved instantly • AI classifies the request (order, refund, product question, sales, general) • sentiment is analysed at the same time • the request is routed to the correct branch automatically • refund eligibility is checked when needed • product questions search a knowledge base • responses are sent back to the exact channel the message came from If anything fails, the system logs the issue and creates a ticket automatically. 46 nodes running inside a single workflow. The interesting part isn’t the AI. It’s the architecture. Most support tools simply respond. Well designed systems think before they respond.