Inconsistent client catalogs
Product attributes, naming and claims differ across systems, categories and suppliers.
For e-commerce agencies
Add AI-ready product data to your agency offer.
Veritiana gives agencies a structured method for auditing client catalogs, improving product-data quality and deploying deterministic commerce signals without building the infrastructure from zero.
Catalog AI-readiness audit
Reusable delivery framework
Continuous data revalidation
Agency delivery system
Client catalog → audit → pilot → scalable service
Client opportunity identified
Client catalog
HomeTech Store
Consumer electronics catalog across multiple suppliers and sales channels.
Products
4,820
Categories
12
Source systems
4
Markets
3
AI-readiness audit
Current catalog condition
Machine readability
54%
Attribute consistency
61%
Source completeness
72%
Missing required fields
17
Conflicting values
328
Unnormalised attributes
43
Recommended pilot
Wireless headphones category
High traffic, measurable product-data gaps and limited implementation scope.
Current readability
49%
Target readability
90%+
Pilot duration
4 weeks
Agency delivery path
Repeatable across clients
01
Audit
Identify catalog gaps and commercial priority
02
Pilot
Transform one category and measure improvement
03
Scale
Extend the signal model across catalog and channels
New agency service
AI-ready catalog transformation
A measurable service combining catalog audit, product-signal design, implementation and ongoing revalidation.
Clear project scope
Measurable before / after
Recurring validation work
Audit baseline created
Pilot category selected
Scale path defined
Reusable delivery framework
The agency opportunity
Clients need more than another AI-generated content package.
The durable problem is not writing more product text. It is building product data that search systems, assistants, recommendation engines and future commerce agents can interpret reliably.
Weak AI answers
Assistants cannot answer product questions reliably when the source catalog is incomplete or ambiguous.
One-off manual work
Teams repeatedly clean feeds and rewrite descriptions without creating reusable infrastructure.
Agency delivery framework
A controlled project from audit to catalog-scale rollout.
The engagement is structured so the agency can begin with a measurable pilot, prove value and then extend the product layer across the client catalog.
01
Audit
Review source systems, catalog structure and current product-data quality.
02
Model
Define the deterministic product profile and category-specific signal schema.
03
Pilot
Transform one category and validate outputs against real business requirements.
04
Integrate
Connect feeds, APIs, websites and downstream client commerce systems.
05
Scale
Extend the verified product layer across categories, markets and channels.
What the agency owns
Keep the client relationship. Add the infrastructure behind it.
Veritiana can operate as the technical product-data layer while the agency remains responsible for client strategy, communication, implementation and commercial delivery.
Agency
Client strategy and delivery
Own the relationship, project framing, creative implementation and ongoing account work.
Veritiana
Product-data infrastructure
Provide the audit model, signal extraction, deterministic profiles and validation pipeline.
Client
Approved business truth
Validate product facts, source priorities, category rules and publication requirements.
Agency offer components
A service package that can be sold in clear stages.
AI-readiness audit
Assess catalog completeness, consistency, source quality and current machine readability.
Readability scorecard
Give the client a measurable baseline and category-level improvement priorities.
Product signal model
Define deterministic product profiles adapted to the client’s catalog and categories.
Structured outputs
Deliver validated product profiles, JSON-LD and machine-facing semantic data.
Implementation support
Integrate outputs into product pages, feeds, search, assistants and client systems.
Ongoing revalidation
Recheck product outputs as catalogs, suppliers, prices and business rules change.
Best fit
Built for agencies working with complex product catalogs.
The model is most useful where product-data quality affects search, conversion, support, recommendation or AI-facing commerce channels.
Large catalogs
Thousands of products with inconsistent supplier data, attributes and naming.
Multi-market stores
Product data reused across languages, regions, channels and storefronts.
Technical products
Compatibility, variants and specifications directly influence purchasing decisions.
AI commerce projects
Clients preparing product search, recommendation, assistants or agent-commerce workflows.
Start with one client category
Turn one catalog problem into a repeatable agency service.
Select one client, one category and one measurable data problem. Validate the delivery model before offering it across the agency portfolio.
Discuss an agency partnership