How it works

• Ask: ask questions in natural English. No prompt engineering. No pasted CSVs. No re-explaining your account structure.
• Retrieve: the agent pulls live data across campaigns, ad groups, targets, keywords, and other Amazon Ads entities.
• Analyze: a specialized analysis layer runs the work behind the answer: root-cause diagnostics, comparisons, regressions, scenario models, and performance decomposition.
• Recommend: the agent returns a clear answer, the evidence behind it, and recommendations mapped to real Amazon Ads levers.

The Xnurta Agent analyzes and recommends. It does not take action on your account without you. Action-taking is on the roadmap.

Built for the questions your team answers every week

Diagnose performance shifts.
Stop clicking through campaigns and bid histories to find the cause of a drop.
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Ask: why did ROAS drop on this account last week?
Example Answer: 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.

Model budget changes before you commit.
Scaling should not depend on a guess.

Ask: if I increase budget on this keyword, what happens to ROAS and incremental sales?
Example Answer: 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.

Debrief promotions while the context is fresh.
Prime Day, BFCM, and seasonal events should not take weeks to analyze.

Ask: compare this promotion window to the equivalent window last year. What worked, what did not, and what should we change next time?
Example Answer: 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.
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Prepare for client and executive calls.
‍Walk into the meeting with the three things that matter.

Ask: what are the top three performance changes this month for this client?
Example Answer: 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.
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Understand what the AI did.
‍Automation should not be a black box.‍

Ask: why did the AI pause this keyword?
Example Answer: 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 a 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 Xnurta Agent explains automated bid changes, pauses, negations, and other optimization decisions in plain English, with the underlying logic surfaced. This is one of Xnurta’s structural differentiators because the analytical agent and AI optimization layer live in the same platform.‍