Driving Engagement Through Experience

Two months into implementing #Auditzy RUM for a leading #eCommerce brand, everything seemed steady, until it wasn’t 😅 . During a high-stakes campaign with a surge in traffic, conversions quietly started dipping. No one could explain why. There were no deployment issues. No downtime. No major visual bugs. But something was broken. That’s when Auditzy™ - Real Time Website Speed & Core Web Vitals Monitoring Tool’s stepped in, and surfaced the real problem within hours: A massive spike in Interaction to Next Paint (INP) across the site. Mobile INP shot up. Desktop wasn’t spared either. 😑 From product discovery to “Add to Cart” to payments — every interaction had a noticeable delay. What users experienced: 👉 Taps that didn’t respond instantly 👉 Navigation lag 👉 Checkout steps that felt frustratingly slow The campaign was live. Traffic was high. Every second mattered. Auditzy™ - Real Time Website Speed & Core Web Vitals Monitoring Tool didn’t just detect the issue, it visualized the problem 👉 Page-by-page, journey-by-journey 👉 Real user data, not synthetic guesses 👉Split Website Performance trends by OS, Device, Browser, and Network Speed The culprit? A third-party script injected as part of a plugin update — unnoticed by traditional monitoring. 🔥 Thanks to this visibility, the brand’s engineering team acted fast. ✅ INP stabilized. ✅ User journeys recovered. ✅ Revenue leakage was prevented mid-campaign — not after. This is what real-time, real-user performance monitoring looks like. This is why brands choose Auditzy — not just to monitor, but to protect experience when it matters most. P.S. If you are an online commerce brand, let's talk! I would love to share our learnings with your engineering team! #CoreWebVitals #INP #WebPerformance #RealUserMonitoring #EcommerceTech #PageSpeed

"When agents take actions, the nature of risk fundamentally changes. Unlike generative AI, these systems directly execute actions through digital tools and interfaces. While current agent prototypes handle tasks like scheduling meetings or booking flights, more ambitious proposals imagine agents that negotiate contracts, assist in healthcare decisions, or coordinate supply chains. Because these systems act directly in the environment, failures to meet user goals can result in financial loss, safety risks, or breakdowns in critical processes. While design choices and deployment context shape when and how failures occur, it is the agent’s real-time actions that directly cause incidents. These operational stages are therefore critical points for monitoring and intervention. To address these risks, we need real-time failure detection. Real-time failure detection is the use of automated monitoring systems that track agent behavior as it unfolds, flag anomalies, and either halt execution or escalate to human oversight. Ensuring robust detection is not without challenges: deploying agents is already resource-intensive, and monitoring systems can add comparable costs. If poorly calibrated, monitoring may overwhelm users and operators or miss critical failures. These challenges raise important questions about how well detection can scale, and whether market and policy incentives will support its adoption. The full report is structured around four central claims: Agents require new forms of failure detection due to their ability to effect change in the environment. The risk of agent failures — and the necessity of real-time detection — depends on the stakes of actions, their reversibility, and the agent’s architectural affordances. Safety-critical industries show failure detection can reduce harms and provide a foundation for safer agent design. Significant technical research and regulatory guidance must be prioritized to close gaps in designing and evaluating failure detection for AI agents." Madhulika Srikumar Kasia Chmielinski Jacob Pratt Carolyn Ashurst The Alan Turing Institute Chloé Bakalar William Bartholomew Rishi Bommasani Peter Cihon Rebecca Crootof Mia Hoffmann Ruchika Joshi Maarten Sap Caleb Withers Partnership on AI

Real-time monitoring isn’t about sensors or dashboards. It starts with people. Before wiring a single machine, sit down with operators, supervisors, and CI leaders. Ask: What information would actually help you hit your goals? Machine states, scrap problems, downtime details. Those answers shape the whole project. Here’s the 12-step framework to monitor your factory in real time: → Step 0: Interview people to define key info to track → Step 1: Map your process, lines, and machines → Step 2: Collect downtime, scrap, and capacity data → Step 3: Define fields from SKUs/work orders → Step 4: Set a heartbeat signal per machine → Step 5: Identify data sources (PLCs, SCADA, OPC…) → Step 6: Connect machines with wiring and networks → Step 7: Configure the system with your process info → Step 8: Train people and involve them in validation → Step 9: Validate data with regular shift/day/week reviews → Step 10: Build CI dashboards with structured agendas → Step 11: Track KPIs and actions tied to improvements → Step 12: Analyze trends to guide strategy High performers don’t start with tech. They start with people, then build the system that makes every meeting, every decision, and every improvement cycle run on facts. Pro tip: Step 0 saves months of wasted effort later. PS: If you had to pick one, what’s the most important data point to track in your plant? Save this framework and repost to help others start monitoring in real time.

⚡ Why Real-Time Monitoring of Link 16 and Link 22 Matters More Than Ever We all love a clean tactical picture until it starts drifting, lagging, or disappearing altogether. That’s usually not the fault of your equipment… it’s the network talking back. If you’re not actively monitoring your Link 16 and Link 22 networks in real time, you’re leaving mission assurance to chance. 🎯 Why It’s Critical Link 16 and Link 22 are a huge part of the nervous system of modern operations. They carry track data, mission orders, and weapon coordination across every domain. When they falter, everything downstream suffers from the Common Operating Picture to targeting and identification. You can’t fix what you can’t see, and you can’t trust what you don’t measure. Without real-time monitoring, you risk: •Undetected latency or drift between network participants •Track fragmentation across multiple nodes •Crypto or NPG mismatches that silently isolate participants •Link 22 burst errors or propagation loss degrading your picture •Forwarding loops or data floods that cause unnecessary congestion These issues don’t always trigger alarms, they hide inside the network until it’s too late. 🧠 What You Can Do Monitoring your networks isn’t just watching graphs, it’s about actively managing them. Here’s what a proactive approach looks like: 1. Run Live Health Dashboards: Use your Multi-Link Gateway’s diagnostics or TDL Monitoring Suite to visualize connectivity, latency, and throughput in real time. 2. Set Automated Alerts: Configure thresholds for jitter, message loss, or timeslot sync errors. Let automation catch the problem before the operator does. 3. Correlate Track Behavior to Network Events: Don’t just blame “data link.” Look at when tracks split or vanish and align it with link performance logs, that’s how you find root causes. 4. Validate Crypto and Time Sources Daily: Crypto or clock skew are the silent killers of network stability. A muli-second offset can wreck your synchronization chain. 5. Leverage Simulation & Replay Tools: Use systems like MLST3 or your platform’s link recorder to replay issues offline and fine-tune configuration settings. 6. Empower Operators: Teach your operators what “normal” looks like on the link, so they can recognize abnormal patterns instantly. A trained human eye, backed by good visualization, is your best early warning system. 🚀 The Payoff When you monitor in real time, you don’t just react, you prevent. You maintain synchronization, protect data integrity, and ensure the Common Tactical Picture reflects reality, not assumptions. It’s the difference between discovering a problem after a mission, and solving it before it matters. Real-time monitoring isn’t a luxury. It’s how we protect trust in the network and trust in the fight. ⚔️ 💬 How does your team monitor link performance today? dedicated dashboards, custom tools, or operator-driven checks?

ESP monitoring uses real-time downhole, electrical, and production data to track pump health and operating conditions. Continuous surveillance identifies early warning signs such as gas lock, low flow overheating, and abnormal power behavior. Early detection allows corrective action before failure, extending ESP run life and reducing downtime and deferred production Why Monitoring ESP wells •Analyze trends of intake/discharge pressure, amperage, frequency, and wellhead pressure vs time to see gas interference, scaling, or declining reservoir support. •Compare current operating point to pump curves and design envelopes to confirm the ESP runs within recommended flow/head and avoid up‑ or down‑thrust conditions. •Build event logic to correlate alarms/trips (overcurrent, underload, under‑voltage, thermal) with process changes so you can pinpoint true root causes instead of just symptoms. •Use failure/run‑life statistics (MTBF, failure rate, survival curves) from your ESP database to identify bad actors, dominant failure modes, and the impact of field practices on reliability •Apply advanced analytics and high‑resolution electrical and process data to detect anomalies and predict failures early, enabling proactive workover planning and optimized uptime.

Latency is often the first symptom of a problem, and the clearest signal of performance health. Latency metrics are critical for understanding and optimizing the performance, reliability, and user experience of a system. If you only track "average latency", you're missing the real picture. Here’s a breakdown of the most important latency metrics, what they mean, and why they’re worth tracking. ✅ 𝗔𝘃𝗲𝗿𝗮𝗴𝗲 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 The mean of all request latencies. Useful for quick insight, but sensitive to outliers. It can give a false sense of performance. ✅ 𝗠𝗲𝗱𝗶𝗮𝗻 (𝗣𝟱𝟬) 50% of requests are faster than this. Offers a more realistic view of the "typical" user experience. ✅ 𝗣𝟵𝟱 𝗮𝗻𝗱 𝗣𝟵𝟵 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 These capture the slowest 5% or 1% of requests. Critical for understanding how your system performs under pressure or at scale. If P99 latency is 1.2s while average is 200ms, most users are happy, but some are suffering. ✅ 𝗠𝗶𝗻 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 Fastest observed request. Not actionable by itself, but helpful for benchmarking best-case performance. ✅ 𝗠𝗮𝘅 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 Shows the worst case. Useful for spotting anomalies or system hiccups like DB locks or timeouts. ✅ 𝗥𝗼𝘂𝗻𝗱-𝗧𝗿𝗶𝗽 𝗧𝗶𝗺𝗲 (𝗥𝗧𝗧) Time for a message to go out and a response to return — used in networking. High RTT = slow communication. ✅ 𝗧𝗶𝗺𝗲 𝘁𝗼 𝗙𝗶𝗿𝘀𝘁 𝗕𝘆𝘁𝗲 (𝗧𝗧𝗙𝗕) When the first byte of data reaches the client. Combines server processing + network latency. Common in web performance tuning. ✅ 𝗝𝗶𝘁𝘁𝗲𝗿 Variability in latency over time. Especially problematic in real-time systems (streaming, gaming, VoIP). Why is measuring latency important? 𝗨𝘀𝗲𝗿 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲: Slow responses = frustrated users. Even a few hundred milliseconds can impact conversion rates, engagement, or app satisfaction. 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴: Metrics like P95/P99 help spot rare but severe issues that averages hide. They reveal if a few users are consistently having bad experiences. 𝗕𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Helps pinpoint if delays are due to code, infra, or external services. 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀: Spikes in latency under load show how well the system handles growth. Latency often increases before outright failure, serving as an early warning signal. 𝗦𝗟𝗔 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲: Accurate latency tracking ensures you meet contractual obligations. 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗢𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝗶𝗲𝘀: - Consistently high service time? Optimize the code. - High network time? Consider edge caching or CDN. - Queue delays? Check load balancers or autoscaling. Latency is the most human-facing performance metric. It’s how users feel your system. Track the right numbers. Watch the tails. Don’t trust averages. Measure what matters and act before users feel it. Because what happens to the slowest 1% of users affects 100% of your reputation. Follow Lahiru Liyanapathirana for more like this.

Real-world AI monitoring at scale. Not a white paper. Not a pilot. Here's an observation I'd like to share. When our mammography AI partner, ScreenPoint Medical, released a major Transpara update last year — significantly improving specificity — INCEPTO and its clients were able to measure, via incepto cockpit, what that change looked like in real clinical practice, across a large European installed base. Across three major European radiology groups, we observed drops in doubt classification from ~30% to ~15% — detectable within days of each site update, correlated with radiologist feedback on the ground. Not all sites updated simultaneously. Post-deployment monitoring isn't a feature. It's a precondition for responsible AI deployment at scale. The EU AI Act is beginning to formalize this obligation. But regulation alone won't build the culture of continuous validation this field needs. That requires institutions, vendors, and clinicians to treat post-deployment performance as a scientific obligation — not an afterthought. And here's what this example also shows: monitoring isn't only a safety net to catch degradation. It can document improvement — making algorithm updates visible, quantifiable, and trustworthy for the clinical teams relying on them.

📊 𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗘𝘃𝗲𝗻𝘁 & 𝗙𝗲𝘀𝘁𝗶𝘃𝗮𝗹 𝗠𝗲𝗮𝘀𝘂𝗿𝗲𝗺𝗲𝗻𝘁 𝗜𝘀 𝗔𝗹𝗿𝗲𝗮𝗱𝘆 𝗛𝗲𝗿𝗲 Traditional head counts are being revolutionised by technology that measures what matters: genuine engagement and emotional response. The transformation is happening through innovative measurement tech that captures real-time crowd dynamics and individual experiences. 𝗧𝗵𝗲 𝗡𝗲𝘄 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗧𝗵𝗮𝘁 𝗠𝗮𝘁𝘁𝗲𝗿: ✦ Biometric Engagement • Heart rate variation patterns reveal peak moments • Galvanic skin response tracks emotional intensity • Average engagement time: 47 minutes vs 18 minutes traditional ✦ Kinetic Surface Analytics • Smart floors measuring crowd flow patterns • Heat mapping of high-engagement zones • 34% more accurate space utilisation ✦ Crowd Flow Intelligence • Real-time density mapping • Predictive bottleneck prevention • 23% improved crowd distribution ✦ Experience Intensity Tracking • Sonic resonance measurement • Group synchronisation patterns • 3.2× more accurate than traditional satisfaction surveys 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗦𝗽𝗼𝘁𝗹𝗶𝗴𝗵𝘁: Tomorrowland's Tech Evolution Case Study: The world's leading electronic music festival transformed measurement across 400,000 attendees: • Bracelet-based crowd tracking reduced congestion by 52% • Smart-surface implementation across 7 stages improved flow efficiency by 93% • AI-powered heat mapping revealed 3.1× higher engagement in optimised zones → Result: Record-breaking NPS score of 92, up from 76 previous year 𝗧𝗵𝗲 𝗡𝗲𝘄 𝗠𝗲𝗮𝘀𝘂𝗿𝗲𝗺𝗲𝗻𝘁 𝗧𝗼𝗼𝗹𝗯𝗼𝘅: 1. Smart surfaces for movement patterns 2. Biometric wearables for engagement 3. AI-powered crowd flow optimisation When we move beyond simple headcount's to understanding the collective heartbeat of an event, we transform from counting attendance to creating resonance. 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻: Which emerging technology excites you most for measuring event success? Share your thoughts below 👇 #EventTech #FestivalInnovation #EventAnalytics #LiveEvents

Real-time video and audio monitoring powered by AI can improve OR efficiency and safety, but requires careful policy, legal, and ethical frameworks to gain clinician trust. 1️⃣ A large US health system implemented AI-driven video monitoring in over 50 ORs to track metrics like turnover time and case start delays. 2️⃣ Initial staff resistance focused on fears of surveillance, liability, and unclear data use, particularly during surgery. 3️⃣ A strict governance model was adopted: only three designated leaders could access intraoperative footage, and the surgeon was notified whenever it was reviewed. 4️⃣ Video is retained for 30 days and audio for 7; recordings are not part of the permanent medical record. 5️⃣ Use cases are limited to quality, operational efficiency, and safety. Any training or research use requires separate approval. 6️⃣ Consent forms were updated to cover digital monitoring, and visible signage informed OR staff about ongoing recording. 7️⃣ The program showed early signs of success, with growing clinician support and better perceived workflow and safety. 8️⃣ Policies remain flexible, with discussion underway about expanding the technology to other clinical settings like ICUs. 9️⃣ Broader adoption may benefit from centralized governance models, like those used in the UK, to ensure consistency and trust. 🔟 Future evaluations will focus on data accuracy, long-term clinical outcomes, and staff and patient perceptions of AI use. ✍🏻 Ngoc-Anh A. Nguyen, Brendan Holderread, Grace Lee, Divya Reddy, Roberta Schwartz. Integrating Image-Based Artificial Intelligence in the Operating Room: Enhancing Safety and Efficiency While Navigating Legal and Ethical Considerations. Telehealth and Medicine Today. 2025. DOI: 10.30953/thmt.v10.578

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).