eCommerce performance teams today are stretched thin. Data is stored in disparate systems, bandwidth is limited, and it’s rarely clear which actions will drive the best outcomes.
Rapid is purpose-built for that reality. It unifies the data, surfaces what matters, and gives you the controls to act without opening a ticket for every change or relying on expensive external resources.
Browser, edge, and origin metrics come together in one correlated model. Captures 100% of shopper sessions.
Anomaly detection, Conversion Zone analysis, and our YoBot AI assistant turn signal into prescriptive next steps. Native MCP access makes the same data available to your proprietary AI development workflows.
Application Sequencing and Context Intelligence let you change how the site loads — third-party load order, device-specific rules, geo-aware delivery — without code changes.
Traditional RUM misses 15–30% of your traffic, including ad blockers, privacy tools, failed beacons. Yottaa RUM combines client-side telemetry with edge network intelligence to capture every session, validate browser data with edge ground truth, and attribute latency to the layer where it actually lives so you don’t waste cycles trying to solve problems in the wrong place.
Browser-only RUM and synthetic monitoring only give you half the picture. When a regression hits, your team spends the morning guessing whether the problem is in the browser, at the edge, or at origin. Yottaa RUM puts all the pieces together to remove the guesswork and help you improve performance faster.
Google grades search ranking on Core Web Vitals — but their CrUX data is a 28-day moving average from Chrome users. Rapid captures CWV in real time from every browser, every device, every session, and shows you the exact resources pulling each score down.
Performance is relative. A 2.5-second LCP is great in furniture but middling in apparel. Rapid benchmarks your site against thousands of top commerce brands across 18 community categories and 6 major eComm platforms, so you know whether to invest in optimization or move on to the next priority.
Anomaly AI uses machine learning to track your site against a rolling two-week baseline and flag anything outside its usual pattern — page load spikes, error surges, third-party regressions, traffic shifts. Alerts come through dashboard, email, or Slack. The result? Most fires get put out before a shopper ever notices.
On Black Friday, an analytics provider’s beacon started causing 5-second timeouts across multiple Yottaa customers. Anomaly AI flagged the regression and our team deployed a sequencing rule to make the script non-blocking before most teams noticed.
There’s a specific load time range on every eCommerce site where shopper conversion rate spikes..and it’s all downhill from there. Yottaa Conversion Insights identifies that Conversion Zone for your site, quantifies how many shopper sessions currently fall inside it, and highlights your opportunities to improve.
YoBot, Rapid’s AI assistant, answers questions about your site’s performance in plain English, and is grounded in real shopper telemetry. Available in dashboard and Slack. Authenticated and scoped — it sees your data, never PII, and is always there to give you the quick, personalized answers you need to keep performance flowing.
Yottaa’s MCP server is the only one on the market that connects eCommerce web performance data directly to business impact. Ten purpose-built tools expose real-user metrics, anomalies, third-party impact, and Conversion Zone analytics to any MCP-compatible client: Claude Code, Cursor, Claude Desktop, VS Code, or custom agents.
Most storefronts run 25–40 third-party apps (or more). Application Sequencing scores each one, controls when and how it loads, and defers the ones that are costing you conversion.
Set your rules once, and start seeing the impact that faster load times can make on your conversion rates. No hassle, no time wasted. Just better outcomes for your digital store.
Assign a percentage of traffic to any sequencing rule, security rule, or third-party configuration. Feed results back into Adobe, Google Analytics, or any analytics platform. Decide whether vendor A or vendor B actually wins based on real-shopper performance data, not vendor claims.
A shopper on 5G in Manhattan and a shopper on 3G in rural Idaho don’t need the same payload. Context Intelligence analyzes each HTTP request — device, geo, browser, connection, custom properties — and adapts delivery to fit.
Reviews, chatbots, recommendations, ratings — every third party your team added to grow conversion is also a potential drag. Rapid tracks every script against five violation categories, in real time, so you know which vendors are pulling their weight (and which ones are slowing you down).
“After a short proof of concept with YOTTAA, we saw our average site speed improve by over 30%. We also saw a strong correlation between faster site speed and higher conversions.”
Jay Nigrelli, VP of eCommerce, Perry Ellis International
Perry Ellis
Performance issues diagnosed in minutes, not days. Problems caught proactively, before shoppers feel them. Less engineering time lost to firefighting. Smarter spend on agencies and external consultants.
A consistently faster, more reliable site that holds up under peak traffic.
A shared definition of “good performance” across technical and commercial teams.
Cross-industry benchmarks that tell teams where they actually stand.
Performance becomes a continuous, cross-functional program — not a siloed, one-off project.
RUM data streams through Kafka into Databricks for volume, advanced analytics, custom reporting, and AI model training.
Centralized performance data exposed through REST APIs and the MCP server for diagnostics, anomaly detection, and enterprise AI integration.
A single tag for client-side metrics with browser-specific polyfills. Fastly real-time log streaming for authoritative request data. Designed to onboard additional CDNs.
RUM data contains no PII. ISO 27001:2022 certified. GDPR and CCPA compliant. AES-256 encryption at rest.
Up to 13 months of historical data depending on package.