Introduction
As commerce shifts from human-led browsing to AI-mediated decision-making, product catalogues are quietly becoming one of the most strategic assets in digital commerce. In an agentic commerce world — where AI systems discover, interpret, rank and recommend products on behalf of users — your catalogue is no longer just a merchandising artefact. It is an interface for machines.
This has given rise to a new class of tools promising to make catalogues more discoverable, interpretable and actionable by large language models and autonomous agents. Two such tools gaining attention are Ocula and ReFiBuy.
Both sit under the same broad promise — agentic commerce readiness — but they tackle the problem from very different angles. This article takes a deeper, more critical look at each: what they actually optimise, where they create value, and when merchants should think twice before adopting them.
Why Product Catalogues Matter in Agentic Commerce
Before reviewing the tools, it’s worth reframing the problem they are trying to solve.
In traditional e-commerce, product discovery is mediated by:
human search behaviour
keyword-driven SEO
visual merchandising and UX patterns
In agentic commerce, discovery is increasingly mediated by:
structured attributes
semantic completeness
consistency across systems
machine-interpretable signals, not persuasion
AI agents don’t “browse”. They filter, score and rank. If a product lacks attributes, clarity or context, it may never enter the decision set — regardless of how good it looks on a PDP.
This is the gap tools like Ocula and ReFiBuy are addressing.
Why Product Catalogues Matter in Agentic Commerce
Before reviewing the tools, it’s worth reframing the problem they are trying to solve.
In traditional e-commerce, product discovery is mediated by:
human search behaviour
keyword-driven SEO
visual merchandising and UX patterns
In agentic commerce, discovery is increasingly mediated by:
structured attributes
semantic completeness
consistency across systems
machine-interpretable signals, not persuasion
AI agents don’t “browse”. They filter, score and rank. If a product lacks attributes, clarity or context, it may never enter the decision set — regardless of how good it looks on a PDP.
This is the gap tools like Ocula and ReFiBuy are addressing.
ReFiBuy: Catalogue Intelligence for Agentic Decision Systems
What ReFiBuy Is Actually Solving
ReFiBuy operates at a deeper layer of the commerce stack. Rather than generating copy, it evaluates and optimises how AI agents interpret your catalogue as a system.
Think of ReFiBuy as a catalogue observability and optimisation layer for agentic commerce.
It focuses on:
- attribute completeness and consistency
- semantic alignment across SKUs
- machine-readable enrichment
- performance feedback from AI-mediated discovery
Where Ocula enhances expression, ReFiBuy enhances qualification.
Where ReFiBuy Excels
ReFiBuy is particularly valuable when:
- Your catalogue is structurally complex
Multi-variant products, configurable SKUs, regional inconsistencies — these are exactly where agents struggle and ReFiBuy adds value. - You care about AI-driven eligibility, not just visibility
Being discoverable is not enough. AI agents must trust and understand your data to recommend it. - You operate across multiple channels and feeds
ReFiBuy helps normalise and synchronise product intelligence across systems, reducing fragmentation. - You are preparing for autonomous purchasing agents
As agents move from recommendation to execution, catalogue reliability becomes a risk issue — not just a marketing one.
Where Merchants Should Think Twice
ReFiBuy is not a lightweight tool.
Considerations include:
- Higher organisational maturity required
ReFiBuy assumes teams understand data governance, taxonomy design and catalogue strategy. Without this, insights may go unused. - Less immediate ‘wow’ factor
Unlike copy changes, catalogue intelligence improvements are often invisible to humans — but critical to machines. - ROI depends on future-facing bets
If your customers are not yet using AI-mediated shopping journeys, value may feel indirect in the short term.
When ReFiBuy Makes Strategic Sense
ReFiBuy is best suited for:
- mid-to-large merchants with complex catalogues
- organisations treating data as a product
- teams actively preparing for agent-led discovery and purchasing
It is less suitable for early-stage stores or merchants still struggling with basic catalogue hygiene.
Ocula vs ReFiBuy: Different Layers, Different Jobs
A useful way to think about the difference:
Ocula optimises how products speak
ReFiBuy optimises how products are understood
In agentic commerce, both matter — but they solve different failure modes.
Ocula addresses:
thin descriptions
inconsistent messaging
outdated content
ReFiBuy addresses:
missing attributes
semantic ambiguity
agent misinterpretation
For many organisations, the optimal path is sequenced adoption, not tool replacement.
When Should Merchants Adopt Agentic Catalogue Tools?
Merchants should seriously consider tools like these when:
AI search and assistants are already driving measurable traffic
catalogue scale makes manual optimisation impractical
product differentiation depends on nuanced attributes
leadership recognises that “machine customers” are emerging
They should hold off when:
core product data is unreliable
the catalogue is small and stable
AI discovery is not yet a strategic priority
Agentic commerce tooling amplifies existing maturity — it does not replace it.
Conclusion
Agentic commerce is not about replacing e-commerce fundamentals — it’s about extending them into a world where machines act before humans.
Ocula and ReFiBuy represent two important but distinct responses to this shift:
Ocula makes product language more discoverable and scalable
ReFiBuy makes product data more intelligible and trustworthy to agents
For merchants serious about competing in AI-mediated commerce, the question is no longer if catalogues need to change — but which layer to fix first, and why.
The strongest agentic commerce strategies will treat catalogues not as static listings, but as living, machine-facing systems.
FAQs
1. What is agentic commerce in simple terms?
Agentic commerce refers to shopping experiences where AI systems — not humans — actively search, compare and recommend products based on intent, constraints and data.
2. Do these tools replace traditional SEO?
No. They extend it. SEO targets human queries; agentic optimisation targets machine reasoning and qualification.
3. Should small merchants care about agentic commerce yet?
Only if they are already seeing AI-driven discovery or plan to scale rapidly. For many SMEs, fundamentals still matter more.
4. Is it better to start with Ocula or ReFiBuy?
Start with Ocula if content quality is the bottleneck. Start with ReFiBuy if data integrity and structure are the limiting factors.
5. Will AI agents really make purchasing decisions?
Early versions already do in constrained domains. The trend is towards delegated decision-making — especially for repeat and utility purchases.
