AI Retrieval & Commerce Intelligence
Does AI Know Your Brand Well Enough to Recommend It?
RetrieveAI audits how AI systems understand, retrieve, and represent your brand and tells you exactly what to fix to show up when it matters.
RetrieveAI audits how AI systems understand, retrieve, and represent your brand and tells you exactly what to fix to show up when it matters.
Most brands are optimising for the wrong signal. Here's what that looks like in practice.
You rank #1 on Google for your main category keyword.
A user asks ChatGPT: "What's the best [your product] right now?"
Your brand isn't mentioned. Three competitors are.
You have no idea why or what to change.
Your brand scores 38 / 100 on AI Visibility. Three competitors are above 70.
The audit surfaces exactly which pages are missing structured data, unclear entity signals, and content gaps.
You get a ranked list of fixes ordered by impact on your AI Visibility Score.
Next audit, you see the score move. You know it's real because the system is deterministic.
Screens from RetrieveAI showing how retrieval scoring, scope management, and commerce intelligence surface across the audit experience.
Main dashboard showing AI Visibility Score, Entity Strength, and Retrieval Coverage as normalized 0–100 scores. Audit status and recent run history surfaced at a glance.
Score change tracking across audit runs over time. Surfaces which dimensions are stable and which shift enabling targeted, evidence-based content decisions.
URL discovery and scope selection interface. Maps site structure into auditable surfaces before scoring begins from single pages to full-site coverage.
Commerce readiness layer showing how well a site's product infrastructure is positioned for AI-driven discovery and agentic interaction.
Six dimensions that together give you a complete picture of how AI systems see your brand and where the gaps are.
Measures how prominently a brand surfaces when users ask AI systems relevant questions. Produces a normalized 0–100 score across the audited scope.
Evaluates how clearly and consistently a brand is represented as a named entity across structured data, content context, and AI-accessible signals.
Maps all auditable surfaces product pages, category clusters, FAQs and verifies each is correctly structured and accessible for AI retrieval.
Validates schema markup completeness and structured data quality ensuring pages communicate clearly to AI systems during indexing and retrieval.
Simulates the real queries users ask AI systems against your content identifying where coverage is strong, where gaps exist, and where the brand is missing entirely.
Audits whether a commerce site's infrastructure is ready for AI agent interaction without executing any transactions. Read-only, non-transactional assessment.
You choose the scope one page, a cluster of pages, a whole category, or the full site. RetrieveAI then runs a targeted audit at exactly that depth. No noise, no wasted cost, no overwhelming report.
Automatically discovers all the pages within your chosen scope before any scoring begins so nothing gets missed.
Related pages are grouped before scoring, so signals across a category or topic are understood together not in isolation.
Pick the scope that matches your question. Auditing a product launch? One page. Auditing a whole category? Full category mode. The depth is always your call.
Basic signals are checked first, deeper analysis runs after. Each stage only runs when the previous one has passed so results are always grounded in validated data.
A structured, multi-stage pipeline each stage building on validated output from the last. Enter a URL, get a complete picture of how AI systems see your brand.
Most audit tools read a page the same way a browser does fully rendered. But AI crawlers don't execute JavaScript. RetrieveAI runs a three-layer JS dependency audit that no existing tool does.
Compares the raw HTML a server sends with the fully rendered DOM. Surfaces content that only exists after JavaScript runs invisible to AI crawlers that don't execute JS.
Phase 3.5Simulates user clicks on accordions, tabs, dropdowns, and variant selectors. Detects product information, pricing, and content that's hidden until a user interacts content AI agents will never see.
Phase 3.6Scores how well an AI agent can navigate a site without JavaScript checking whether filters use real URLs, whether search forms work without JS, and whether pagination is in the HTML.
Phase 3.7Why this matters for commerce: If your variant prices, product descriptions, or filter URLs only load after a JS interaction, AI shopping agents can't read them regardless of how good your SEO is.
Each tool chosen for a specific reason reliability, AI compatibility, or capability that generic defaults cannot provide.
A few of the architectural choices that make the system reliable, accurate, and genuinely different from simpler approaches.
When someone asks ChatGPT, Gemini, or Perplexity to recommend a product, compare services, or find a solution the AI pulls from what it knows and what it can retrieve. Your search ranking doesn't matter inside that process.
Analytics tools measure what happens after someone clicks. SEO tools measure where you rank in search results. Neither one answers the more important question: does the AI even consider your brand when forming its answer?
RetrieveAI was built to answer that question with structured audits, controlled AI simulation, and scoring that tells you exactly where you stand and what to do about it.
More and more, people ask AI systems instead of typing into a search bar. If your brand isn't well-represented in how AI understands your category, you're invisible in that channel.
A brand can rank #1 in Google and still be missing from AI-generated answers. The signals that matter for AI retrieval are different from the ones that drive search rankings and most brands have no visibility into them.
Without controlled, reproducible scoring, changes between audit runs could reflect model randomness rather than real content changes. Determinism is what makes progress measurable.
Most AI retrieval problems are concentrated on a handful of pages or content areas. Auditing what matters not everything gives you faster, clearer answers and more actionable next steps.
The engine is built to be consistent audits don't fail silently, scores don't fluctuate randomly, and re-running the same audit produces the same results. Trustworthy data drives better decisions.
This is what shifts when you have actual data on your AI retrievability.
No idea whether AI systems mention your brand in relevant responses
Can't tell if content changes are helping or hurting AI visibility
Competitors show up in AI answers you don't know why
Publishing content without knowing if it's structured for AI retrieval
Product pages live behind JavaScript AI agents can't read the data
A score for every dimension — AI Visibility, Entity Strength, Retrieval Coverage, Commerce Readiness
Ranked recommendations — exactly what to fix, in what order, with estimated impact
Trend tracking — re-run audits and see scores move as content improves
Gap map — every question AI systems might ask about your brand, matched to your content coverage
Commerce audit — know exactly which product surfaces are and aren't AI-agent ready
Not every audit needs to crawl an entire website. RetrieveAI lets you target exactly what matters from a single product page to your whole site. Each scope level is tuned for a different use case, depth, and budget.
One page. Perfect for auditing a key landing page, product page, or hero content before a launch or campaign.
A group of related pages like a product category, feature set, or topic cluster. The most balanced option for most audits.
An entire section of your site including sub-pages, filters, and listing pages. Good for commerce categories or content hubs.
Your entire website. The most comprehensive view of how AI systems understand your brand across every surface.
| Scope | AI Visibility Score | Entity Strength | Cross-page Analysis | Commerce Readiness | Snapshot Tracking |
|---|---|---|---|---|---|
| single_page | ✓ | Partial | — | — | ✓ |
| context_cluster | ✓ | ✓ | ✓ | ✓ | ✓ |
| category | ✓ | ✓ | ✓ | ✓ | ✓ |
| full_site | ✓ | ✓ | ✓ | ✓ | ✓ |
Architecture and design decisions explained clearly.
No. SEO tools optimize for search engine rankings crawl coverage, backlink authority, keyword density. RetrieveAI audits how LLMs retrieve and represent a brand inside generative inference. These are architecturally distinct problems requiring different instrumentation and different remediation paths.
Ranking measures position in a results list. Retrieval measures whether a brand is included in an AI-generated response at all. A brand can rank highly in search and still be invisible to AI systems. RetrieveAI measures retrieval directly not as a proxy of search performance.
Full-site sweeps generate a lot of noise and cost. Most retrieval problems are concentrated on specific pages or intent areas. Scoped audits surface higher-quality signals faster, at lower cost, with clearer remediation paths.
Without reproducible scoring conditions, a change in score between two audit runs might reflect model randomness rather than a real content change. Determinism ensures that score changes mean something the content changed, not the measurement conditions.
No. The Agentic Commerce layer is strictly an infrastructure audit. It validates whether a commerce system is structurally ready for AI-agent interaction but does not perform checkout, payment execution, inventory locking, or financial transactions.
Anyone who wants to understand how AI systems represent their brand. That includes marketers who want to know if they're showing up in AI-generated recommendations, ecommerce teams checking if product pages are AI-readable, and agencies looking for a new kind of audit to offer clients.
A score of 80+ means your brand is well-represented AI systems can retrieve, understand, and cite your content reliably. A score below 50 means there are meaningful gaps: missing structured data, unclear entity signals, or content that AI systems struggle to interpret. Every score comes with specific recommendations for what to fix.
RetrieveAI runs a structured, multi-phase audit pipeline each phase building on the last to produce a complete picture of AI retrievability and commerce readiness.
Discovers and classifies all relevant URLs on the target site building the inventory that every subsequent phase operates on.
Groups related pages into coherent contexts ensuring cross-page signals are captured together and the audit scope matches the actual intent surface being measured.
Crawls and extracts content from each URL in scope structured data, headings, body text, and metadata preparing it for analysis and scoring.
Generates the set of real queries users might ask AI systems about the audited brand building the prompt universe that drives simulation and gap detection.
Simulates how AI systems respond to the prompt universe against the audited content identifying what's retrieved, what's missed, and where coverage is weak.
Combines all signals from prior phases into normalized 0–100 scores AI Visibility, Entity Strength, Retrieval Coverage with per-URL and per-cluster breakdowns.
Translates scoring gaps into ranked, actionable recommendations showing exactly what to improve and in what order to move the score.
Tracks score changes across audit runs over time surfacing regressions, confirming improvements, and attributing score shifts to specific content changes.
Audits whether a site's commerce infrastructure is structurally ready for AI agent interaction. Read-only, non-transactional assessment only, no actions taken.
RetrieveAI is a fully engineered platform 22-phase backend pipeline, multi-vendor crawl architecture, hybrid semantic scoring, and a complete Next.js frontend. Built independently to demonstrate what's possible at the intersection of AI infrastructure and marketing intelligence.
If you're working on retrieval infrastructure, AI visibility tooling, or post-search commerce systems this architecture may be relevant to your work.