AI Readiness Assessment
AI is searching for your business right now. Most companies assume they are AI ready because they use AI tools. Having ChatGPT or Claude does not mean your commerce catalog can be found or understood by AI. These are two different things.
Catalog as read by machines
4 gaps found
Product title and description
Readable
Attributes in typed fields
Free text
Compatibility and fitment
Not structured
Relationships between products
Implicit
Compliance data, queryable
In PDFs
Can AI find you
Partial
Can AI understand you
Low
Recommendation Confidence
Low
Illustrative. Your assessment scores these same two questions against your real catalog.
The problem
AI systems are now part of how your products get found, evaluated, and recommended. But most product catalogs were built for human browsers, not for machines trying to understand them. If that data does not give AI enough context to understand what you sell, who it is for, and why it matters, you do not get recommended. You do not exist.
Browsing
Photography, layout, and copy that carry the pitch
Spec tables a person can scan and interpret
A spec sheet linked as a PDF they can open
Related products they can judge by eye
Built for keyword search and human judgement. This is what most catalogs optimize for today.
Querying
Your catalog was built for keyword search. AI requires something different.
Product data optimized for human readers, not for machine understanding
Compatibility, fitment, and relational data missing from structured catalog fields
AI channels are already influencing how customers find products and most businesses do not know they are invisible on them
AI readiness and SEO are two completely different strategies. Being great at one does not make you visible on the other.
No structured evidence means no confident recommendation.
What we do
AI readiness is not a technology purchase. It is a data and architecture problem. We identify exactly where AI systems cannot find or understand your business, then build the foundation that changes that.
01
We audit whether AI can find you and whether it can understand you. These are the two questions that determine whether your business exists in the AI channel at all.
Findability
Understanding
Gap report
02
We restructure your catalog so attributes are in typed fields, relationships are
explicit, and compliance data is queryable. The data becomes the evidence
machines need.
Typed fields
Relationships
Taxonomy
03
We validate that your AI commerce tools are reading the structured data correctly and configure the integration to maximize Recommendation
Confidence.
Validation
Configuration
Confidence
04
We build the ongoing data governance so your catalog stays machine-ready as it grows.
Ownership
Standards
Monitoring
Approach
Three stages, each answering a question you can check. Governance follows so the answer stays true as the catalog grows.
We determine whether AI can find you and
understand your products. These two questions determine whether you exist in the AI channel at
all.
We close the Machine Readiness Gap. Attributes into fields. Relationships structured. Compliance data queryable. Taxonomy normalized.
We confirm AI systems can evaluate and recommend your products with sufficient confidence. Then we build governance to maintain it.
60%
of Google searches end without a click. AI is answering first.
Crimson Agility
98%
of buyers abandoned purchases due to incomplete product data
Crimson Agility
15%
of purchasing decisions will be autonomous by 2028
Gartner
Why Crimson Agility
Commerce systems integrator since 2014
100+ implementations. 300% average revenue growth across our client portfolio.
7-year average client partnership.
2014
Building commerce systems since
Building commerce systems for brands navigating complex data and AI requirements.
100+
Implementations delivered
Platform-agnostic. We recommend and build what fits your business, not our partner economics.
7 yrs
Average client partnership
We stay engaged because architecture is a capability, not a project.
Platform-agnostic by design.
Our engagement starts beneath the platform, in the data layer, not on top of it.
Start here
Our assessment gives you a clear picture of where your catalog stands against AI
requirements and what to fix first.
No obligation beyond the conversation.
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