Real-time Automated Assistance

When you're running voice AI agents at scale, waiting for post-call reports to spot issues is like driving while only looking in the rearview mirror. Real-time monitoring transforms how you manage voice AI performance, letting you catch and fix problems before they impact customer experience. Traditional call center metrics were built for human agents, not AI systems handling thousands of simultaneous conversations. When your AI agent starts struggling with semantic understanding at 2 PM, waiting until tomorrow's report means hundreds of frustrated customers. Real-time monitoring changes the game: → Spot issues instantly, not hours later → Prevent escalation storms before they overwhelm human agents → Optimize confidence thresholds on the fly → Maintain consistent quality regardless of call volume Metrics that actually matter: 1. Latency: Keep response times under 500ms - beyond 1 second, customers hang up. 2. Semantic Accuracy: Track confidence scores and clarification requests in real-time. 3. Live Sentiment: Catch frustration spikes before they become escalations. Your voice AI needs a nervous system, not just a brain. Real-time monitoring is that nervous system - giving you instant feedback to maintain the quality your customers expect. Precisely what we’re solving for at Leaping AI (YC W25).

When disaster strikes, every second counts. Traditional emergency response relies on human coordination, which can be overwhelmed in rapidly evolving situations. But what if we could empower responders with intelligence that predicts, adapts, and guides decisions in real-time? AI is no longer a futuristic concept; it's a critical tool enhancing emergency management today. From predicting wildfire spread in Australia's bushfire seasons to optimizing evacuation routes during floods in Pakistan, AI-powered solutions are transforming how we react to crises. How AI is revolutionizing emergency response: Predictive Analytics: AI models analyze vast datasets to forecast disaster trajectories, allowing for earlier warnings and more precise resource deployment. Real-time Decision Support: Algorithms can process live sensor data, social media feeds, and weather patterns to provide commanders with actionable insights, optimizing resource allocation and saving critical time. Automated Communication: AI can rapidly disseminate hyperlocal alerts, translate urgent messages, and even manage initial public inquiries, ensuring communities receive vital information swiftly. Optimized Logistics: AI can identify the fastest routes for emergency vehicles, manage supply chains for relief efforts, and prioritize aid distribution based on real-time needs. This integration of artificial intelligence empowers emergency managers to make smarter, faster, and more effective decisions, turning chaos into a controlled response. Is your emergency response strategy leveraging the power of AI? Explore how intelligent solutions can enhance your readiness.

While everyone's talking about AI replacing human agents, something more interesting is happening: technology and humans are forming a powerful partnership that's transforming customer experience. AI isn't stealing your agents' jobs – it's making them superheroes. At Callzilla - The Quality-First Contact Center, we've been implementing Agent Assist tools that give agents real-time support during customer interactions. The results speak for themselves: • Agent gets asked an impossible question? AI whispers the answer • Customer mentions an uncommon tech issue? Relevant articles appear automatically • Agent struggling to categorize the call? AI suggests the perfect reason code • About to make a mistake? AI catches it before it happens This creates a 'best of both worlds' scenario where technology handles routine tasks while agents focus on what humans do best: • empathy • genuine connection • creative problem-solving When to Automate vs. When to Humanize: • Let AI Handle: Repetitive tasks, basic info lookups, initial problem identification • Keep It Human: Complex problems, emotional situations, VIP customers who expect the red carpet treatment Pro tip: Give customers choice. Instead of forcing one path, ask: "We can have an agent available in 5 minutes, or you can chat with our AI assistant now who handles most issues. What works better for you?" Your tech should be: • Serving up answers faster than expected • Reducing agent cognitive load, not adding to it • Supporting natural conversation, not rigid scripts • Suggesting solutions, not just documenting problems AI doesn't replace your agents – it creates 'super agents' who resolve issues faster, with less effort, and greater accuracy. It's not about choosing between humans OR technology. It's about humans AND technology working together. The companies seeing the best results have figured out this perfect pairing – and their customers can't get enough. What's your experience with human-AI partnerships in CX?

We’re not 5 years away. We’re 5 minutes away—if we lean in. Here’s what AI could be doing in restoration right now: • DocuSketch to Estimate: You scan a property using DocuSketch, and within minutes, AI builds 75% of the estimate inside Xactimate or Cotality. No estimator needed—just a reviewer to validate and approve. • Real-Time T&M Logs: As your crew inputs labor, equipment, and materials, AI writes your T&M daily logs in real time—complete with time stamps, photos, and narratives ready for the carrier. • Auto-Scoped Job Photos: AI organizes all project photos by room and phase (demo, mitigation, build-back), links them to the corresponding estimate line items, and stores them with searchable tags. • Carrier-Tailored Scopes: Submitting to State Farm? Travelers? Citizens? AI formats the scope to match the carrier’s preferred structure, language, and pain points—reducing pushback and speeding up approvals. • Smart Estimating Adjustments: AI sees what line items consistently get approved or denied in your market and adjusts your estimate proactively—before the desk adjuster gets a crack at it. • AI-Powered Portals: Property owners get live dashboards with progress photos, bills, ES invoices, and ACH payment options. No more “Can I get an update?” calls. No more chasing checks. • Forecasting Complexity: Before you mobilize, AI flags if this will be a 3-day job or a 30-day fight. It looks at weather, location, policy type, and historical claims data to predict friction and guide your strategy. • Virtual QA: Before submitting, AI audits your job file against IICRC standards, carrier trends, and your own best practices. You catch mistakes and tighten your process—automatically. ⸻ This is the next frontier. If your AI strategy is “wait and see,” you’re going to get steamrolled by companies who are already building these tools in-house or aligning with the right partners. This isn’t about saving time. It’s about staying relevant.

Two drastically different ways enterprises are handling AI Agents for customer support — and only one actually works. THEIR WAY: - Train AI on product info and conversation history—no real-time data - Focus on routine support tasks: password resets, basic returns, store hours - Go fully autonomous, even when issues get complicated - Push self-service, often leading to dead-ends and hallucinations - Requires heavy technical expertise to customize OUR WAY: - Pull real-time customer context from CRMs, EHRs, EMRs, and more - Tackle complex use cases: returns, billing disputes, insurance claims - Offer flexibility: AI-based, logic-based, or hybrid automation, depending on risk - Cover the entire lifecycle—from self-service to agent-assist - Allow seamless human handoff—no forced autonomy where it doesn’t belong - Let business users design and modify AI Agents directly TAKEAWAY: AI Agent vendors tell you they can deflect your entire support volume. Sure—until you watch CSAT drop and revenue slip. Because they don’t capture and understand the customer context required to handle high-stakes issues. Your AI Agent can’t provide medical advice without understanding patient symptoms and medical history. It can’t approve or deny an insurance claim without policy details. If you implement AI Agents, make sure they have the context they need to make the right call. Context = Accurate automation #AI #CustomerSupport #Automation

The Future of Tech Support: Reimagining the Genius Bar Picture walking into an Apple Store, not to wait in line, but to be instantly greeted by an AI-powered system that already knows why you're there. This isn't science fiction—it's the potential future of tech support through an Autonomous Genius Bar system. Revolutionizing the Support Experience The Autonomous Genius Bar concept transforms traditional tech support into a seamless blend of AI-driven diagnostics and human expertise. Upon arrival, customers scan a QR code to initiate an intelligent support session that connects directly with their device. The system immediately begins gathering diagnostic information while guiding users through potential quick fixes. This innovative approach doesn't just streamline the support process—it fundamentally reimagines it. AI-powered diagnostics can identify common issues before a customer even describes their problem, while smart routing ensures that complex repairs reach the right technician with all necessary preparation completed. Key Features of the Autonomous System For simple issues, customers receive step-by-step guidance for self-resolution, from software updates to network resets. More complex problems trigger automated prep work—like disabling security features or running advanced diagnostics—before connecting customers with technicians who can begin repairs immediately. The physical space reflects this intelligent design, featuring: - Interactive diagnostic stations where customers can dock devices for immediate scanning - Smart kiosks that guide users through common troubleshooting steps - Flexible waiting areas with real-time repair status updates - Dedicated technician workstations for complex repairs The Business Case for Automation First, it dramatically reduces wait times by handling routine diagnostics and prep work before human intervention is needed. Second, it empowers customers who prefer self-service options while maintaining access to expert support. Third, it scales efficiently across locations by automating routine processes. Most importantly, it creates a personalized experience by leveraging AI to understand each customer's device history and preferences. The system can proactively suggest maintenance tips, recognize patterns in device usage, and even recommend upgrades or trade-ins when appropriate. The Future of Technical Support The Autonomous Genius Bar represents more than just an upgrade to Apple's support system—it's a blueprint for the future of technical support across industries. By combining AI efficiency with human expertise, it creates a support experience that's both more powerful and more personal than traditional models. As we move toward an increasingly connected future, this model of intelligent, automated support could become the new standard—not just for Apple, but for any company seeking to provide world-class technical support in the digital age.

🚀 AI Agents & Real-Time Automation with Rubric-Based Scoring 🚀 AI agents are driving automations at scale, but ensuring their responses remain accurate, relevant, and reliable in real-time is a challenge. That’s where I’m using rubric-based evaluation to keep AI-generated outputs in check in real time. How It Works (LLM as a Judge) ✅ Intent Clarity – Does the response directly address the query? ✅ Context Alignment – Is the answer factually consistent with the given data? ✅ Accuracy & Completeness – Does it follow structured rules, avoiding hallucinations? Why This Matters? 👉 Eliminates misinformation & hallucinations 👉 Builds trust in AI-driven automation 👉 Enables continuous AI improvement with feedback loops. Here’s one of the rubric scoring I am using for AI evaluation: """ ### Instruction Evaluate the generated response based on the rubrics below. Assign a score from 1 to 5 and explain your reasoning. ### Input **User Query:** {user_input} **Generated Response:** {response} **Context (if available):** {context} ### Rubrics - **Score 1** – The question is misleading, intent remains ambiguous. - **Score 2** – The response is irrelevant, vague, or evasive. - **Score 3** – The response contains information inconsistent with the provided context. - **Score 4** – The response is mostly correct but includes minor details not in context. - **Score 5** – The response is fully accurate, follows all rules, and derives entirely from context. ### Output Return a JSON object in the following format: ```json {   “score”: <Rubric score>,   “reason”: “<Explain why this score was assigned>” } """ With this, each response gets a real-time score (1-5) based on these parameters. If an answer is vague, inconsistent, or off-topic, it gets flagged for improvement—creating a self-correcting AI workflow. ➰ This approach is making my AI agents more accountable, accurate, and reliable in real time automation workflows. Curious about how it works?  Check out Ragas’ framework -> https://lnkd.in/gPCdbEYG How are you ensuring trustworthy AI responses in your systems? Let’s discuss! #GenAI #LLM #AIAgents #RubricEvaluation #LLMAsAJudge

Can AI Outpace Fraudsters in Real-Time? A payment platform detects and blocks fraudulent transactions before they happen, all in milliseconds. Here’s how one fintech did it: AI analyzed user behavior to spot anything unusual. Machine learning models evolved daily, adapting to new fraud tactics. Risk scores in real-time flagged suspicious payments instantly. The result? Fraud cut by 60% without slowing down legitimate users. In a world of instant payments, AI is the secret weapon to stay secure. How are you protecting your platform?

How to Speed Up Claims Processing and Reduce Cycle Times with VehicleCare Challenges for Traditional Claims Processing: For years, claims processing has been synonymous with paperwork, phone calls, and endless back-and-forth communication. This outdated approach leads to: 1. Delays: Manually gathering and verifying information takes time, leaving customers frustrated. 2. Errors: Human input increases the risk of mistakes, which can lead to disputes or rework. 3. High Costs: Inefficient processes drain resources, driving up costs In this digital age, these challenges are no longer acceptable. Customers demand fast, accurate, and transparent claims handling. Automation provides the solution. The Role of Automation in Claims Processing: Automation is transforming the insurance industry by streamlining repetitive tasks and enabling faster decision-making. Here’s how it helps: 1. Faster Data Collection: Automated systems, like Vehiclecare's DCX, can instantly gather data from various sources, such as telematics devices, dashcams, photos, and customer inputs. This eliminates the need for manual data entry and speeds up the initial stages of claims processing. 2. Accurate Assessments: Advanced analytics and machine learning tools analyze the data to assess the severity of a claim. By providing a clear picture of the incident, these tools help adjusters make informed decisions faster. 3. Seamless Communication: Automation streamlines communication between insurers, customers, and workshops. 4. Reduced Human Error: Automated processes minimize the risk of errors in data collection, analysis, and reporting, ensuring claims are processed accurately the first time. How Vehiclecare Speeds Up Claims Processing? Vehiclecare takes automation to the next level by combining powerful technology with user-friendly design. 1. Improved Reporting Efficiency: using the no-install web app users are able to notify handlers of incidents instantly using real-time data. 2. Visual Incident Reconstruction: The platform automatically integrates with telematics systems and generates a detailed visual reconstruction of accidents using on-board diagnostics (OBD) and dashcam data. This not only saves time but also enhances transparency and trust and protects against contentious claims. 3. Real-Time Collaboration: Vehiclecare’s collaborative tools allow all stakeholders—from adjusters to repair shops—to work together in real-time. Results That Speak for Themselves: Faster FNOL (First Notice of Loss): Automating the initial reporting process reduces delays, so claims get started right away. Shorter Cycle Times: With streamlined workflows and real-time collaboration, claims are resolved in days instead of weeks. Reduced Operational Expense: Due to increased efficiency and reduced time spent on claims. Reduced Vehicle Downtime: When claims are resolved faster, your vehicles can get back on the road sooner. https://lnkd.in/dtJ_eD2W.

Everyone says, "Stop talking about AI!" So this weekend... I built something real. I deployed a live AI-powered customer support chatbot — ✅ Built on Voiceflow ✅ Integrated with Supabase for live customer data ✅ Powered by real-time AI summarization and intent detection Fetches real customer information instantly Understands messy user queries intelligently Triggers real operational actions like system activation Recovers gracefully when users go off-track For this first phase, I focused entirely on validating the core workflows — Data fetch ➔ AI understanding ➔ Intelligent action ➔ Error recovery. Here's a quick video walkthrough 👇 Full blog + embedded demo here: https://lnkd.in/drGwQET8 Much more coming soon. Stay tuned. #AI #CustomerSupport #NoCode #AIEngineering