Decision-Grade AI Audits for CPG Brands
AI is already shaping which products get considered.
Do you know why yours get chosen, skipped, or misread?

Xiphos delivers Decision-Grade Audits that test live AI surfaces, capture what the user actually sees, identify why your SKU was included or excluded, and turn that evidence into a prioritized action plan.

Book a Decision-Grade Audit Call Request a Sample Audit
Live interface testing
SKU-level evidence
Prioritized fixes
The Shift

The rules have already changed.

AI is becoming the filter between the shopper and the shelf.

  • That changes the job.
  • It is no longer enough to know whether your brand gets mentioned. You need to know whether your SKU is actually being considered, how it is being described, what signals are helping or hurting it, and why a competitor gets chosen instead.

This is not a visibility problem.

It is a decision problem.

Same shelf. Completely different treatment.

Old Playbook
  • Optimizes for: Clicks, rankings, and media efficiency
  • Measured by: Traffic and share of search
  • Useful for: Discovery, but incomplete for AI-mediated selection
Decision-Grade Playbook
  • Optimizes for: Inclusion, validation, and selection readiness
  • Measured by: Evidence, exclusions, support strength, and action path quality
  • Built to explain: Not just if you showed up, but why you won or lost
What the Audit Covers

Four pillars of a Decision-Grade Audit

A Decision-Grade Audit does more than count mentions. It tests live AI responses, captures what users actually see, reviews the source and support structure behind those outcomes, and turns the findings into a clear action plan.

01

Live Response Testing

We test the prompts, surfaces, and decision scenarios that matter most to your category so you can see where your SKU appears, disappears, or gets replaced.

  • Branded and unbranded queries
  • Comparison, use-case, and shopping scenarios
02

Decision-Grade Evidence

We capture the visible output, cards, citations, retailer pathways, and any signs of confusion, mismatch, or omission.

  • Screenshots tied to exact prompts
  • Inclusion, exclusion, and shopping-path evidence
03

Source and Support Review

We review the pages and signals most likely influencing the result, including brand pages, PDPs, citations, and competing surfaces.

  • Content clarity and consistency checks
  • Validation, specificity, and support-signal review
04

Action Plan and Recheck Path

You get a prioritized set of fixes, a practical rollout sequence, and a benchmark set for future rechecks.

  • Priority fix ladder by impact and effort
  • Recheck plan for tracking movement over time
How It Works

From questions to answers in weeks,
not months.

1
Scope

We define the exact SKU, category context, competitors, retailers, and AI surfaces in scope so the audit answers the business question that actually matters.

2
Audit

We run the live prompts, capture what the user actually sees, and log where your product is included, excluded, misread, or outperformed.

3
Diagnosis + Deliverables

We review the likely support structure behind the result, identify the decision signals helping or hurting you, and deliver the evidence pack, scorecard, and prioritized fix plan.

4
Recheck

We benchmark the core prompts and retest on a defined cadence so your team can see what changed, what improved, and what still needs work.

Deliverables

Not a dashboard.

Not a deck.

A precision instrument.

Xiphos is a Decision-Grade Audit built to show how AI platforms are actually treating your product in live decision moments. It combines response testing, captured evidence, support-structure review, and a prioritized action plan so your team can act with clarity.

What you get
Decision Scenario Map
The exact query types and decision moments used to test your SKU, including discovery, comparison, use-case, and shopping scenarios.
Evidence Pack
Prompt-linked screenshots and observed outputs showing where your SKU was included, excluded, misdescribed, or replaced.
Inclusion and Exclusion Analysis
A clear view of where you are making the shortlist, where you are being passed over, and which competitors are winning instead.
Source and Support Review
A review of the pages, citations, and product surfaces most likely influencing AI behavior, including clarity, consistency, and support signals.
Decision-Grade Scorecard
A summary layer that translates the evidence into a readable performance view across key categories, platforms, and risks.
Priority Fix Plan
A ranked list of what to change first, next, and later, tied directly to the issues the audit uncovered.
90-Day Recheck Plan
A benchmark set and follow-up cadence so your team can track movement after changes are made.
Optional Implementation Support

When needed, Xiphos can also provide implementation-ready copy updates tied directly to the audit findings.

Available
  • Title and variant-ready title options (retailer-safe)
  • FAQ additions focused on the decision questions AI is using
  • Description improvements that reinforce differentiators with verifiable detail
  • Bullet upgrades aligned to shopper language and AI summarization
  • Structured data and schema recommendations, scoped to what matters
  • Image alt-text recommendations where applicable

Optional execution support for teams that want faster follow-through.

Request a Sample Audit Uses a fictional CPG brand to demonstrate the full methodology.
AI Visibility & Selection Audit
Hero SKU | Quarterly Deep-Dive | Q1 2026
Evidence: 46 outputs | 18 sources | 12 queries
Sample
Verdict:
High mention rate, low selection. Mentioned 18% of the time but shortlisted just 3%.
3%
Shortlist Rate
Low
Recommended in final set
42%
Selection Loss
High
Compared, then skipped
38 /100
Confidence
Low
Verifiability strength
Mention vs Shortlist
BrandMentionedShortlistedGap
Competitor A22%14%
Competitor B17%11%
Competitor C14%9%
Your Brand18%3%15 pts
Mentions are not selections.
Why AI skipped your SKU
Top blockers driving selection loss:
  • Pack size conflict
  • Claims not verified
  • Availability unclear
Most common: Pack size conflict
Top fix: Normalize pack size + claims across PDPs and brand.com
Selection Journey (sample)
QuerySurfaceEvidenceOutcome
Best snacks for kids ChatGPT Not cited Excluded
Made with real fruit Perplexity Cited: PDP Not Selected
Bulk for families Walmart AI Cited: Brand Included
Each outcome captured with exact AI response text.
Retailer Coverage
Walmart Present
Amazon Missing
Target Wrong SKU
Kroger Not detected
Coverage issues reduce selection confidence.
Priority Fix Ladder
1
Pack size consistency
Titles, bullets, PDP specs
High Low
2
Add decision FAQs
Use-case and comparison queries
High Med
3
Verify key claims
Trusted sources and citations
High Med
Ranked by impact and effort.
Who This Is For

Built for CPG teams that need proof, not AI theater.

If AI is shaping which products get considered before the shopper ever reaches the retailer, this audit shows where your SKU stands, why it is winning or losing, and what to change first.

Most engagements start with one hero SKU, then scale across the portfolio.

Great fit when you are responsible for:
  • SKU-level performance across AI and retailer-linked pathways
  • Ecommerce content, PDP quality, and selection readiness
  • Competitive pressure at the point of recommendation
  • Fixing what is suppressing consideration before it costs share
Not built for:
  • Teams looking for vanity metrics without evidence
  • One-time curiosity with no plan to act
  • Brand storytelling that ignores product-level reality
Primary teams
Brand
You own positioning and trust.
You care about
  • How your products are described in AI answers
  • Whether your differentiators actually show up
  • Why competitors get framed as the better choice
You get
  • Proof of how AI is actually describing your product
  • Evidence of where your differentiators are not landing
  • Clear fixes to improve credibility and recommendation strength
Ecommerce
You own content and conversion.
You care about
  • Whether the right SKU appears as purchasable
  • Retailer coverage gaps and wrong-SKU matches
  • PDP completeness and consistency across channels
You get
  • A decision-grade view of retailer and PDP readiness
  • Evidence tied to wrong-SKU matches, missing coverage, and weak purchase pathways
  • A prioritized execution list your team can act on
Digital Shelf
You own visibility at the SKU level.
You care about
  • When you are mentioned but not shortlisted
  • Where selection losses are happening by query
  • Which signals AI seems to reward in your category
You get
  • A clear picture of mention versus selection
  • Query-level proof of where you are being passed over
  • A recheckable benchmark set for tracking movement over time
About

From the Founder

Cary Tobey, Founder of Xiphos
Cary Tobey
Xiphos Founder

The shelf has changed.

AI is increasingly shaping which products get considered, which get skipped, and how they are framed before a shopper ever reaches the retailer. Most teams still do not have a reliable way to see that process clearly.

I built Xiphos to solve that.

After decades helping CPG manufacturers strengthen how their products show up in buyer conversations, I now apply that same discipline to AI-mediated selection. Xiphos delivers Decision-Grade Audits that capture live outputs, review the signals behind them, and turn that evidence into a prioritized action plan.

I lead every engagement personally so the work stays specific to your category, your SKUs, your competitors, and the decisions your team actually needs to make.

If you need a clearer picture of how AI is treating your products, let's talk.

Cary

Let's Talk

Schedule a Decision-Grade Audit Call

If you are responsible for a CPG brand and need a clearer view of how AI is influencing product consideration, we can walk through your current situation, the surfaces that matter most, and whether a Decision-Grade Audit makes sense for your team.

For CPG, ecommerce, and digital shelf leaders who need evidence, not guesswork.

Common Questions

FAQs

Is this just SEO with an AI label on it?
No. SEO helps you compete for rankings and traffic. Xiphos is built to show how AI systems are actually interpreting, comparing, validating, and selecting your products in live decision contexts. Different problem. Different evidence standard. Different deliverables.
Do you just diagnose problems, or do you provide actual content?
The core offer is the audit. That includes the evidence pack, source and support review, scorecard, and prioritized fix plan. If needed, Xiphos can also provide implementation-ready copy support such as titles, bullets, descriptions, FAQs, and related updates tied directly to the audit findings.
How technical does my team need to be?
Not very. The audit is built so brand, ecommerce, and digital shelf teams can understand what happened, why it happened, and what to change. Technical items are documented clearly enough for internal or external partners to execute.
How quickly will we see results?
That depends on how quickly changes are made and how quickly AI systems refresh the sources they rely on. The point of the audit is to shorten the guesswork, focus the work, and create a benchmark so progress can be rechecked with evidence.
What if we only have a few SKUs?
We can start with a single hero SKU. The value comes from SKU-level precision, not from volume for its own sake. Most engagements start focused and expand based on what the first audit reveals.
What makes this "decision-grade"?
A Decision-Grade Audit is built around live testing, captured evidence, source review, and practical next steps. It is designed to explain not just whether your product appeared, but why it was included, excluded, or misread, and what your team should do next.
What's up with the name, "Xiphos"?
A xiphos (ZEE-fōs) is a double-edged ancient short sword built for close-range precision work. That's the idea. :-)