The Xnurta Agent is a specialized AI retail media analyst that retrieves live, entity-linked Amazon Ads data to deliver automated campaign diagnostics, predictive budget modeling, and instant performance readouts in plain English. Move from "what happened?" to "what should we do?" in minutes without manual spreadsheet loops or pasted exports.
Why did ROAS drop on this account last week?
General-purpose AI can write fluent advertising analysis. That is not the hard part. The hard part is getting the numbers right.
Amazon Ads teams work across campaigns, ad groups, targets, keywords, ASINs, branded and non-branded terms, promo windows, budget constraints, and automated optimizations. A model that treats that data like generic rows and columns will miss the relationships that matter.
The Xnurta Agent is built for Amazon Ads workflows from the ground up. It retrieves live, entity-linked data, runs real analysis, and returns recommendations tied to the levers your team actually controls.
Ask questions in natural English. No prompt engineering, no pasted CSVs, no re-explaining your account structure.
The agent pulls live data across campaigns, ad groups, targets, keywords, and other Amazon Ads entities.
A specialized analysis layer runs the work behind the answer: root-cause diagnostics, comparisons, regressions, scenario models, and performance decomposition.
The agent returns a clear answer, the evidence behind it, and recommendations mapped to real Amazon Ads levers.
Stop clicking through campaigns and bid histories to find the cause of a drop.
ROAS decreased by 12% due to a 14% increase in non-branded CPCs on [Keyword X] during the Prime Day warm-up window, coupled with a budget depletion cap at 2:00 PM EST.
The agent identifies the specific driver, shows the supporting data, and helps your team avoid pulling back spend before it knows what changed.
Scaling should not depend on a guess.
Increasing the daily budget on [Keyword: Top Competitor Term] by 35% ($500/day) is projected to drive an incremental 18% to 22% increase in unit sales over the next 14 days. However, due to diminishing returns on this specific target, account-level ROAS is modeled to contract by 4.1% as the bid engine captures lower-intent evening traffic. The confidence limit for this optimization stands at 91% based on your historical Q4 pacing data.
The agent uses your historical data to model likely outcomes, including diminishing returns and confidence limits.
Prime Day, BFCM, and seasonal events should not take weeks to analyze.
Compared to the identical 48-hour Prime Day window last year, Total Sales increased by 24%, driven by a 40% surge in Sponsored Products revenue. What worked: high-bid placement on your top 3 Hero ASINs successfully captured early-day conversion spikes. What did not: branded defense campaigns depleted their caps by 11:00 AM EST, allowing competitors to capture a 6% conquesting share in the afternoon. Next time: implement a 15% budget buffer on branded targets and shift long-tail inventory to a flat-bid constraint to preserve margin.
The agent compares non-adjacent windows, highlights drivers, and gives your team a same-day read.
Walk into the meeting with the three things that matter.
Here are the top three performance shifts for [Client Account] this month to reference on your call: 1. NTB (New-to-Brand) Acceleration: AMC data shows a 19% lift in NTB sales, directly correlated with the expanded Sponsored Brands video allocation launched on June 1st. 2. Wasted Spend Reduction: non-converting search terms were aggressively negated, reducing total wasted ad spend by 14% and shifting $3,200 back into high-margin exact match targets. 3. ACOS Optimization: overall ACOS compressed by 3.2 percentage points due to automated dayparting adjustments that lowered bids during historically low-conversion midnight windows.
The agent turns live account data into concise talking points your team can use before the call starts.
Automation should not be a black box.
The AI Autopilot paused [Keyword: exact_match_running_shoes] today at 2:15 PM EST. Over the last 7 days, this target generated 42 clicks but 0 conversions, causing its individual ACOS to spike to 114% against your account target of 30%. Additionally, search query logs revealed an intent-shift where 68% of the traffic was coming from low-converting variations (e.g., 'kids running shoes' instead of your adult SKU). The keyword was paused to prevent further budget bleeding, saving an estimated $140/week that has been redistributed to your top-converting targets.
The agent explains automated bid changes, pauses, and negations in plain English, with the underlying logic surfaced. This works because the analytical agent and the AI optimization layer live in the same platform, a structural Xnurta differentiator.
Every retail media platform now says it has an AI agent. Most claims sound the same. So we built the benchmark first.
The Xnurta Retail Media Insight Benchmark tests agents on real Amazon Ads workflows across five dimensions:
Did it answer the question asked?
The right data, windows, and baselines?
Are the numbers correct?
Does evidence support the conclusion?
Are recommendations specific, useful, and reversible? Data accuracy is not averaged away. It acts as a multiplier, so a fluent answer built on wrong numbers is still wrong.
The Xnurta Agent is built around the messy reality of Amazon Ads. That is why it can answer the questions Amazon power users actually ask.
We are explicit about the line: today, the agent analyzes and recommends. You decide what to do next.
A ROAS drop. A budget increase. A Prime Day readout. A client-call prep request. An AI optimization you want explained. We will run it on your account and show you the answer.