Stop Digging Through Data. Meet The Xnurta Agent.

Reading time: 5 minutes

The Xnurta Agent answers your Amazon Ads performance questions in plain English; instantly, accurately, and on live data. No spreadsheets. No guessing. Just answers.

Your Analytical Copilot for Amazon Ads

Advertising analysts and operators spend hours running the same workflows to answer performance questions and generate insights and recommendations. The Xnurta Agent acts as an analytical copilot that allows your team to spend less time digging and more time scaling by replacing manual spreadsheet loops with instant, fact-checked answers on live data.

How It Works

1. Ask: Ask questions in natural English. No complex prompt engineering or explaining account structures.

2. Retrieve: The agent pulls live, entity-linked data directly across campaigns, targets, and keywords.

3. Analyze: A specialized layer runs true analysis; regressions, models, and root-cause diagnostics.

4. Recommend: Get reliable analytical outputs and clear recommendations tied directly to Amazon Ads levers.

Hero Use Cases

  • 60-Second Diagnostics: Stop clicking through endless bid histories. Get the specific cause of performance shifts instantly so you can maintain confidence and stop prematurely pulling back spend.
    Query Example: "Why did ROAS drop on [account] last week?"
  • Scenario Planning: Model the impact of budget increases before committing. The agent uses your historical data to build diminishing-return curves so you scale intelligently.
    Query Example: "If I increased budget on [keyword] 10x, what would my ROAS be?"
  • Post-Promotion Analysis: Get a same-day debrief for events like Prime Day. Compare complex, non-adjacent promo windows instantly to identify drivers and lessons for next time.
    Query Example: "Compare [promo dates] performance vs. equivalent window last year."
  • Last-Minute Meeting Prep: Walk into any client call prepared. Instantly synthesize account changes and optimizations into concise, accurate talking points.
    Query Example: "What are the top 3 performance changes this month for [client]?"
  • AI Autopilot Review: Turn the AI black box into a glass box. Get plain-English explanations for every automated decision, pause, or negation so you never lose control.
    Query Example: "Why did the AI pause keyword Summer-Pro on Jan 1?"

The Proof Layer: Published EVAL Methodology

We don't ask you to trust us blindly. We published a rigorous EVAL methodology testing 100+ real Amazon Ads questions across 5 dimensions.

  • Unmatched Data Accuracy: At the foundation level, the Xnurta Agent passes ~80% of the time. In the harshest version of the eval, alternative frontier models from Anthropic, Google, and OpenAI using the Amazon MCP collapsed completely, scoring just 35.3%, 27.6%, and 20% respectively.
  • Amazon Specialization: Understands managed-group ACOS shifts, branded vs. non-branded logic, and cross-surface effects natively.
  • Human-Verified Quality: Rated an industry-leading 4.04 out of 5 for reasoning and recommendation quality by expert reviewers.

Coverage

The agent connects deeply across the Amazon Ads ecosystem:

  • Sponsored Products (SP)
  • Sponsored Brands (SB)
  • Sponsored Display (SD)
  • Sponsored Video (SV)

Start Scaling Efficiently

The Xnurta Agent is free for customers (with a fair-use usage limit). No per-query API costs.

Contact your representative to evaluate it on your own data.

FAQs

What is the 'glass-box' standard for retail media AI?

It is an analytical approach requiring AI to show its work, providing full transparency into the data retrieved, the entities resolved, and the logical evidence chain behind every recommendation, ensuring every number is traceable.

Why is data accuracy more critical than fluency for Amazon Ads AI?

Generalist models often sound authoritative while using incorrect data. In retail media, a recommendation built on wrong numbers leads to wasted spend and faulty budget reallocation, making accuracy the only true performance metric for your stack.

How do I evaluate if an AI tool for retail media is truly trustworthy?

Evaluate tools using a benchmark framework that prioritizes retrieval accuracy, entity resolution, and visible reasoning rather than just writing quality. A trustworthy tool must allow you to verify the logic and data behind every specific recommendation it makes.

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