What Does Trust in AI Mean and How Does Product Agent Build Confidence?

Context is king in AI. And nowhere is this more true than in retail.

Brands and retailers don’t operate in a vacuum. They operate within verticals and subverticals, targeting specific audiences with defined expectations, compliance requirements, and merchandising strategies.

A product description for a luxury skincare brand shouldn’t read like one for industrial hardware. A children’s toy cannot be described the same way as a home appliance. Context defines relevance. And it’s why injecting AI into any workflow that produces an output for human consumption is risky. There’s always a chance that the output is SLOP.

In this blog, I’ll dive into what AI slop means for retail and how merchants can build confidence in AI tooling for critical workflows and outputs. I will outline how we’ve built trust factors into our Product Agent, but speak more broadly about the stakes for merchants, no matter where you’re considering AI in your workflows.

Let’s go!

What is AI slop?

AI slop is what happens when artificial intelligence produces low-context, high-volume, unrestrained, and unchecked outputs. We’ve all seen it to some degree using ChatGPT or Gemini. It’s content that may sound fluent, but lacks grounding, specificity, and accountability. It stems from broad, unstructured foundational data combined with a lack of guidance, guardrails, or clearly defined goals.

AI, left untrained or unguarded, doesn’t inherently understand nuance. Without domain grounding, it optimizes for fluency and probability, not accuracy or commercial impact. That’s where the “generic slop = reliability problem” begins.

Bad incentives produce bad content. Bad content poisons the information ecosystem. And poisoned ecosystems make models less trustworthy over time.

In retail, that degradation doesn’t just reduce quality, it erodes revenue, trust, and operational integrity.

How is it relevant in commerce?

There are so many ways that injecting AI into workflows can royally screw up outputs and have a lasting impact on brands today. I’ll explain my main three:

Bad product data is costly

In e-commerce, AI slop isn’t just annoying, it’s costly.

Agentic commerce craves content depth and quality to surface relevant products within the context of customer prompts. We’re really talking about the claims that frame an offer to the customer. Rich attribute coverage, accurate specs, relevant FAQs, and keyword density—these elements make products discoverable during early research, help shoppers narrow options during comparison, and ultimately influence conversion.

Every attribute is a claim.

Every claim shapes a purchase decision.

Sloppy data increases the likelihood that:

• The product doesn’t appear in filtered search results.
• The customer misunderstands the product.
• Expectations don’t match reality.
• The sale is lost.
• The item is returned.

There is real margin and brand reputation hanging in the balance for merchants. Inaccurate enrichment doesn’t just reduce visibility, it increases returns, hurts loyalty, and adds operational cost.

AI needs trust to scale

For AI to scale in e-commerce, it must be trusted.

If AI outputs are inconsistent, if attributes don’t match product categories, units fluctuate, or materials are described differently across similar SKUs, then teams will default to manually triple-checking everything. That defeats the purpose of automation. Worse, it leads to abandonment.

To scale AI in e-commerce, outputs must be domain-specific and retail-aware. AI must understand taxonomy structures, vertical-specific context, product use cases, customer personas, and geographic differences. It must operate within the logic of commerce, not outside of it.

Without consistency and reliability, AI becomes another tool to manage rather than a system that drives efficiency.

The industry is built on accuracy

AI slop is pervasive when asked to perform open-ended creative tasks. Retail product enrichment is the opposite.

Retail operates within constraints:

• Controlled vocabularies
• Fixed schemas
• Regulatory requirements
• Channel-specific formatting rules
• Strict category hierarchies

Without constraints, generative models can:

• Invent attributes
• Extrapolate beyond available evidence
• Infer from weak signals

Retail enrichment must be schema-bound and evidence-bound. It’s not about creative expression; it’s about structured accuracy.

Commerce is an industry built on precision. The systems that power search, logistics, pricing, and compliance assume the data is correct. If it’s not, the errors compound quickly.

How does fabric Product Agent avoid slop?

Avoiding slop isn’t about limiting AI. It’s about aligning it with the right incentives, constraints, and validation gateways. And we’ve built these into Product Agent and our fabric NEON commerce models to gain trust.

1. Built with the right incentives and goals

Every AI system needs to understand its goals.

Product Agent and the NEON commerce AI models are generated from millions of retail-specific reference data points and grounded in a robust commerce ontology. Every action our Product Agent is designed to take is rooted in retail optimization—whether that’s improving attribute coverage, enhancing discoverability, or aligning with channel requirements.

The goal isn’t to generate more content. The goal is to generate commercially accurate content.

2. Navigates with constraints

AI needs guardrails to stay focused on the right outputs.

We define constraints specific to commerce and, more importantly, to retail verticals and product categories. These constraints guide NEON’s models toward contextually accurate outputs, preventing schema drift, attribute invention, and hallucination.

The models are not operating in an open field. They are navigating within structured boundaries designed for retail.

3. Shows its work

Every suggested change or addition to content depth and quality includes a brief description of the agent’s reasoning. The explanation ties the enhancement back to extracted product data or contextual logic. Outputs aren’t presented as magic. They’re supported by evidence.

4. Seeks validation

Automation doesn’t eliminate expertise; it amplifies it. With human-in-the-loop workflows, Product Agent gives users the ability to validate and update changes to descriptions, attributes, FAQs, and image alt tags to ensure they’re brand-accurate, category-appropriate, and contextually relevant. Users can provide a reason for the change and apply updates at the product, category, or brand level.

This feedback loop allows NEON commerce models to learn and apply those refinements to future enrichments, continuously improving accuracy and alignment for your brand.

5. Scores its outputs

Perhaps most importantly, Product Agent displays a confidence score for AI-generated content where uncertainty exists.

If fabric has low confidence in a generated attribute or enhancement, it’s clearly surfaced in the UI. Users can quickly identify content that may require review, along with a brief explanation of why it was rated as low confidence.

Confidence is based on:

• The quality and completeness of extracted product data.
• The likelihood that the generated enhancement introduced hallucinated or weakly supported claims.

This allows teams to focus their attention where it matters most rather than reviewing every SKU equally. Let’s dig into this a bit more.

Built trust in Product Agent enrichment with confidence score

Our confidence scores evaluate two core components:

1. Extraction — Did we extract all relevant details from the source data accurately?
2. Enhancement — How confident are we in what we generated? Does it track cleanly to extracted data, or did the model infer beyond evidence?

These are two interconnected systems: extraction and enhancement.

Extraction

Extraction focuses on source data integrity.

We evaluate:

• Source agreement — If a product’s color is listed as red in both the title and description, do those signals align?
• Completeness — Are required attributes present? Is the source data sufficient?
• Input quality — If input data is inconsistent or incomplete, that uncertainty is reflected in the confidence assessment.

Extraction confidence ensures we understand what exists before attempting to enhance it.

Enhancement

Enhancement evaluates what the model adds.

We calculate a grounding score, which checks whether generated descriptions or attributes are directly supported by extracted data or if they were inferred. If something appears to be hallucinated or weakly grounded, confidence decreases.

We also assess brand alignment. If a brand has provided specific tone, messaging, or compliance instructions, we check for violations before surfacing enhancements.

Together, extraction confidence and enhancement confidence create a transparent reliability layer over every enrichment.

Product Agent brings confidence to agentic commerce

The race to unlock value from AI within enterprise organizations is on. We heard this firsthand from operators at NRF. Leadership teams are setting mandates: define the AI strategy, drive efficiency, move faster.

But there is also real concern. How can AI empower teams without risking reputation? How can automation increase efficiency without compromising accuracy?

The reality is that inertia is taking over. As we adopt AI in our daily lives, it is shifting how we operate at work. The question isn’t whether AI will be used, it’s how responsibly it will be deployed.

Establishing trust in AI requires incremental experimentation, validation, and measurement. It requires systems that acknowledge uncertainty instead of hiding it.

That’s why we’ve built trust factors into both the foundational models and the user experience of Product Agent. By embedding goals, constraints, evidence, human validation, and confidence scoring directly into Product Agent, we enable brands and retailers to grow their AI usage responsibly, and confidently, with each step.

See for yourself! Get a demo today.

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