Retail AI Consulting
Fix the foundation.
Put AI to work
across every product experience.
Okkular has shaped its practical retail AI services through customer conversations and delivery work. Those learnings give us a whole-workflow view: source data, product and brand rules, AI output, human review, and the systems that put it to use.
- Built from real retail workflow learning
- Work with your existing retail stack
- Measure value before scaling
- Keep people in control of change
How we work
A practical path from retail problem to measurable AI outcome.
Start with one workflow, prove the improvement, and expand only when the data supports it. No open-ended AI programme and no forced platform replacement.
Diagnose
Find the product-content, discovery, or channel-readiness workflow that is losing time, quality, or revenue.
Fix the foundation
Define the category, attribute, value, brand, and approval rules the workflow needs to succeed.
Apply AI
Use the right enrichment, content, discovery, or automation capability against the approved definition.
Measure and scale
Compare results with the current process, improve the operating model, and extend only where value is proven.
What we have learned
We built the services by working through real retail problems.
Across customer conversations and the work of building our product suite, we have learned that better tags, content, or discovery are rarely just AI problems. They are connected workflow problems across data, rules, people, systems, and the shopper experience.
The request is not always the root cause
A request for more tags or better descriptions can point to a deeper issue in supplier data, product ownership, approval timing, or channel rules. We look across the workflow before deciding where AI will help.
Rules make AI output usable
AI needs clear category, attribute, brand, and channel rules. We make those rules usable by the AI, the people reviewing its output, and the systems that need to activate it.
Review and activation matter from day one
A result only has value when the right people can review it and it can move into the retail systems they already use. We include exceptions, ownership, and integration in the design from the start.
The full workflow shows the practical next step
Because we have built across these connected parts, we can help teams focus on the change that will make the biggest difference now, then build from that evidence rather than chase a broad AI idea.
Applied retail AI
What this looks like in a real retail operation.
These are the day-to-day product-data problems that slow teams down, create exceptions, or leave shopper experiences incomplete. Okkular applies AI where it can improve the work, not just generate an answer.
Supplier data does not line up
Product details arrive under different names, in different formats, or only in images.
AI applies: Read product images, feeds, and specifications, then extract and normalise the attributes the workflow needs.
One product must serve several channels
Teams rewrite or reformat the same product for ecommerce, marketplaces, feeds, and campaigns.
AI applies: Create channel-ready content from an approved product record, applying the relevant brand and channel rules.
Different retail groups use different structures
Brands, categories, markets, and business units often use different taxonomies and approved language.
AI applies: Use the right category and attribute definition for each workflow, while retaining shared rules where they matter.
Visual discovery can leave shoppers at a dead end
When a shopper cannot find a close alternative or a complementary product, relevant items in the catalogue remain unseen.
AI applies: Visual Search surfaces visually similar products and in-stock alternatives from product imagery. Okkular is developing Shop the Look to extend this into complementary-product and complete-the-look journeys, tested for relevance, availability, and engagement before scaling.
What improves
AI becomes commercially useful when the product experience improves with it.
The engagement starts with the retail result that matters. Okkular then identifies the data, rule, and workflow constraints preventing AI from delivering it consistently.
Complete product data
Generate and validate product tags and attributes that are useful across categories, systems, and retail teams.
Content ready for every channel
Turn an approved product record into descriptions that follow the rules for ecommerce, marketplaces, feeds, and campaigns.
Discovery that keeps shoppers exploring
Improve search, filters, visual similarity, related-product, and complete-the-look journeys with richer product data and visual intelligence.
Fewer recurring exceptions
Fix the rules and approvals that cause repeated correction work, instead of paying to solve the same data problem in every batch.
The difference
Do not just enrich the catalogue. Fix what keeps it inconsistent.
An enrichment request often exposes a deeper problem: unclear category attributes, uncontrolled values, implicit brand rules, or a release process that never reaches every system. Okkular makes that product-definition foundation explicit and governed.
That is how AI output becomes accurate, reviewable, reusable, and ready for the workflows that matter to your retail operation.
Retail groups rarely operate with one product taxonomy. Brands, categories, markets, and channels can need different structures, attribute sets, and approved language. The aim is not to force them into one generic model, but to make the rules explicit, decide what should be shared, and apply the right definition to each workflow.
See how the engagement worksMake the rules explicit
Define the category, attributes, accepted values, brand rules, and owners that shape useful output.
Give AI an approved foundation
Use that shared definition to guide generation, validation, review, and exception handling.
Activate with confidence
Deliver approved output into the PIM, ecommerce, search, marketplace, or data workflow that creates value.
Your existing investment
Make the systems you already own work harder.
Okkular does not begin with a replacement programme. We work with the systems your retail operation depends on, improving an agreed workflow through approved integration boundaries.
The aim is not to add another disconnected AI tool. It is to make your existing product data, content, and commerce investment more useful in production.
- PIM and MDMProduct records, attributes, and workflow
- Ecommerce and CMSProduct pages, channels, and publishing
- Search and discoveryFacets, relevance, recommendations, and journeys
- ERP, PLM, and suppliersSource product, range, and specification data
- Keep the strategic systems. Improve the product-definition and AI workflow between them.
Bring us the retail workflow that is not delivering.
We will identify what is limiting it, establish the right product-definition foundation, and define a bounded path to measurable improvement with AI.