Conviva research finds that consumer-facing agents spend an average of two minutes and 32 seconds establishing basic context before they can address a customer's actual request.
Download Conviva's State of Digital Experience 2026 to find out what consumers actually do rather than what funnel models assume they do.
New data shows that two-thirds of your most valuable customers take journeys your analytics were never built to see. The implications for AI agent design — and the analytics infrastructure behind them — are significant.
Context graphs are essential for consumer-facing AI agents. Learn how they work — and what it takes to build a good one
Download our report today to dig into the research and discover critical insights into how consumer-facing AI agents can make or break your relationship with customers.
Part 2 of 2. Part 1: We replaced mmap with io_uring in our Rust query engine. It got slower. Part 1 left us stuck: a compio-based io_uring rewrite running 60% slower than the mmap baseline it was supposed to replace. We’d added O_DIRECT and worked around Arrow’s copy-happy default buffer construction, landing at 16 secondsRead more »
In the beginning, there was mmap. It was convenient: it let us lazily read huge numbers of Arrow IPC files from disk without managing memory ourselves. It fit our file format perfectly — Arrow IPC’s layout is designed for zero-copy random access, and mmap gives you exactly that. Then we deployed to production, ran realRead more »
A shopper is using a brand’s AI agent to find wide-fit running shoes to train for a marathon. The agent recommends three pairs, answers every follow-up with confidence, and closes the conversation. Score that transcript on helpfulness, relevance, and clarifying-question quality and it comes back a fraction under perfect. None of the three pairs cameRead more »
Around 2010, one of the first major broadcasters to launch a streaming product had a problem nobody could name. The apps loaded. The streams played. Every dashboard was green. And viewers were disappearing before a single frame appeared on screen. The metric that explained it didn’t exist. We had to go find it and thenRead more »
Twenty million people don’t open a live sports app gradually. They open it ten minutes before kickoff, all at once. That’s exactly when one publisher’s Nexa data showed something breaking: pre-roll ad start failures spiking to 48 times their normal rate, right when everyone tuned in. Nobody on the ad ops side would have calledRead more »
An AI shopping agent can complete a purchase and still fail the customer. Why? Because outcome and experience are not the same metric. In this episode of Beyond The Cart, Keith Zubchevich, CEO of Conviva, discusses what e-commerce brands need to consider when deploying AI agents. Watch the full episode on YouTube and read theRead more »
Fresh off a World Cup where Conviva monitored tens of millions of concurrent streams across 20+ platforms, Conviva CEO Keith Zubchevich joined the Profluence Sports Podcast to talk about the future of live-streamed events. His main point: the Quality of Experience (QoE) discipline that built streaming can’t stop at the stream. It has to extendRead more »
SDK Integration MCP is Conviva's solution for allowing developers to guide, review and approve agents-driven Sensor SDK integrations.
Walk through the engineering arc behind the Conviva JavaScript Sensor SDK, used across our Digital Pattern Insights solutions.
