Cashierless Stores Are Booming — But Can AI Really Be Trusted?

Quick Overview

This blog explores e-commerce cataloging in the age of AI and how combining Human-in-the-Loop (HITL) expertise with AI automation leads to smarter, faster, and more reliable e-commerce product tagging. 

We cover how AI handles scale while Human-in-the-Loop (HITL) protects accuracy, so e-commerce teams ship cleaner attributes, faster SKU onboarding, and steadier marketplace compliance.

Key points include: 

1. Understand how weak tags reduce search performance, conversions, and SEO—and learn to measure their impact.
2. Leverage AI for first-pass enrichment, deduplication, and normalization, and assign humans to handle exceptions and edge cases.
3. Implement a practical HITL workflow for images, text, and specs, incorporating measurable QA and defined SLAs.
4. Ensure compliance with marketplace rules to minimize listing rejections.
5. Establish a cost-predictable operating model using dashboards, audits, and continuous improvement.

Introduction

One of the most underrated issues for Retail & Ecommerce Marketplaces in 2025:  Retail catalogs now outpace teams’ ability to keep product data accurate and compliant. New SKUs arrive daily, attributes change by season and channel, and marketplaces tighten requirements.
Unchecked gaps in product information lead to poor discovery, lower conversion rates, higher returns, and rejected listings.

E-commerce annotation tools alone don’t fix this. Purely manual ecommerce catalog tagging is slow and inconsistent; pure AI stumbles on context, edge cases, and new categories. 

The better answer is a managed e-commerce catalog management model that combines AI speed with human-in-the-loop judgment to build quality at the point of tagging, rather than chasing errors later.

That’s the operating stance of this article: catalog practice in the age of AI means treating tagging as an ongoing service, not a one-time tool decision. 

The main benefit: NextWealth’s catalog operations deliver consistently accurate and complete product data, driving clean attributes, higher acceptance rates, steadier SEO foundations, and fewer surprises at scale by combining AI enrichment with expert human review across images, text, and marketplace-specific rules.

In the sections that follow, we’ll unpack the cost of poor product tagging, why a hybrid e-commerce catalog management model outperforms manual or AI-only approaches, and how NextWealth implements this model end-to-end so that your product catalog becomes an engine for discovery and conversion, not a source of risk. 

Take the next step to future-proof your catalog: discover how a hybrid approach can transform your operations and drive better business outcomes.

Why E-Commerce Catalog Tagging Needs Reinvention

Modern e-commerce catalog management has outgrown ad-hoc spreadsheets and tool fixes. 

As SKUs and channels multiply, product tagging in catalog management in e-commerce must balance AI automation and Human-in-the-Loop (HITL) review, with experts validating AI results to ensure quality and keep attributes consistent, compliant, and searchable across Amazon, Flipkart, Shopify, and your store.

The result of this exercise is higher discovery, better conversion, lower returns, and steadier compliance. 

1. Poor tagging affects outcomes

Incomplete or inconsistent attributes break search and filters, reducing discovery and PDP views. Mis-tagged specifications create expectation gaps and more returns; thin or messy facets weaken e-commerce SEO.
These errors also cause listing rejections or silent de-ranking on marketplaces, directly impacting revenue and operations.

2. Marketplaces have raised the bar

Amazon, Flipkart, and Shopify expect normalized attributes, correct taxonomy, proper variant linking, and compliant identifiers (GTIN/HSN). They also enforce channel-specific image/text rules. These policies evolve and what worked well in the last quarter can fail now without proactive governance.

3. Software alone isn’t enough

AI for catalog tagging is excellent for first-pass enrichment like extraction, normalization, deduplication, and suggestion ranking.
But it struggles with context: regional size charts, fabric blends, bundles, seasonality, brand language, and new categories.

Reliable outcomes come from managing automation for scale, Human-in-the-Loop review for edge cases, expert escalation for compliance-sensitive categories, and auditable QA/SLAs to keep accuracy repeatable.

Result: smarter catalog tagging, faster SKU onboarding, steadier marketplace compliance, and fewer returns.

The Limits of Manual-Only or AI-Only Catalog Tagging (and the Sweet Spot)

Manual-only methods fail at scale. Even with playbooks, pure manual e-commerce catalog management tends to be slow and inconsistent. As SKUs, categories, and channels increase, turnarounds slow, costs rise, and inter-annotator agreement decreases—damaging catalog data quality, discovery, and compliance.

By combining manual and AI approaches, you achieve consistent, scalable catalog tagging that maintains quality and adaptability as SKUs grow and requirements evolve. Leveraging both methods not only delivers speed and coverage but also preserves necessary human judgment and contextual understanding.

The sweet spot: process, people, and AI. 

NextWealth runs a managed model that blends automation with Human-in-the-Loop (HITL) and Expert-in-the-Loop (EITL). AI suggests, humans validate, AI improves. 

This provides high-throughput tagging and QA flow to HITL; compliance-sensitive or complex categories go to EITL. 

Result: faster SKU onboarding, higher acceptance rates, and steadier marketplace compliance without sacrificing speed.

What does this change in practice?

1. Use AI for first-pass enrichment; route low-confidence cases to expert reviewers.
2. Define taxonomy/attributes upfront; monitor precision/recall and TAT as KPIs.
3. Treat catalog ops as a service, not a one-time tool choice.

NextWealth’s Approach to Smarter Catalog Tagging

We deliver e-commerce catalog management as a governed service. AI drives initial product data enrichment, Human-in-the-Loop ensures rapid review at scale, and Expert-in-the-Loop manages complex or compliance-sensitive categories. 

Outcome: uniform tagging, accelerated SKU onboarding, and robust compliance with verifiable QA.

Where AI helps

1. AI ingests product titles and specifications, standardizes attributes, and determines taxonomy and variants with precision and accuracy.
2. AI proactively detects gaps and duplicates, conducts competitive checks across platforms, and prioritizes data suggestions.

Where humans add leverage

1. Experts resolve ambiguity and edge cases, such as regional sizing, product bundles, and seasonal tags, with informed judgment.
2. Our team applies contextual brand understanding, reviewing and confirming critical fields, including GTIN/HSN, hazardous materials, and apparel.
3. We drive continual improvement through curated case studies and guideline updates, ensuring best practices are institutionalized.

Workflow (intake → AI → HITL → EITL → QA)

1. Confirm taxonomy and attribute spec 
2. AI candidate tags + confidence
3. HITL bulk verify/route low-confidence  
4. EITL for tricky categories and edge cases
5. Next, conduct QA sampling and audits. After QA, publish to PIM/MDM/marketplaces and monitor results.

What we measure

We track Precision/Recall, TAT/SLA adherence, exception rates, attribute-driven returns, and coverage of mandatory fields to ensure accountability.

Scale & governance

Supporting all these efforts, we manage operations with elastic teams across centers, versioned playbooks, attribute normalization, data privacy/access controls, and robust change management for evolving marketplace rules.

Human-in-the-Loop / Expert-in-the-Loop as a Service

What it is:

Expert-in-the-loop as a service is a managed service for your online product catalog, where AI adds product information automatically. Trained reviewers and subject experts then check for accuracy, context, and compliance.

What you get:

1. Throughput with control: AI makes suggestions; people review; experts handle special and regulated cases.
2. Controlled process: Step-by-step guides, quality checks, service goals, regular reviews, and problem resolution are all included.
3. Better data, faster: More details on products, quicker product uploads, and fewer mistakes on listings.

How it works:

1. AI extracts, normalizes, and proposes taxonomy/attributes
2. HITL reviews low-confidence items at scale
3. Experts handle complex product types, such as sizing, dangerous goods, or codes

Results: We track details matched correctly, mistakes made, work speed, and reasons for problems, reported weekly for clear accountability.

Implementing Best Practices with NextWealth Services

Govern e-commerce catalog management as a service that blends AI catalog tagging with Human-in-the-Loop review.

Deliver reliable product tagging, onboard SKUs faster, and maintain steady marketplace compliance. 

Follow these steps to implement best practices for catalog management in e-commerce.

1. Define taxonomy and attributes up front
   Lock the category hierarchy, variants, and canonical values. Publish attribute normalization rules for sizes, materials, colors, and units. Share maker–checker playbooks so every analyst and tool follows the same standards.
   
2. Use AI for first-pass product data enrichment
   Automatically extract titles and specifications, normalize units, and propose taxonomy placement plus attributes. Assign confidence scores to every suggestion so you can make objective, repeatable routing decisions.
   
3. Route risk, not volume
   Auto-approve high-confidence tags. Route low-confidence or policy-sensitive fields like GTIN, HSN, hazmat, and apparel sizing to reviewers. Escalate tricky categories to domain experts to protect accuracy and compliance.
   
4. Close the loop continuously.
   Run weekly calibrations, measure inter-annotator agreement, and refresh gold sets. Feed marketplace rejection logs and return drivers into guidelines and models to improve precision, recall, and TAT over time.

Retail Use Cases Delivered by NextWealth

NextWealth empowers retailers by applying advanced AI automation, coupled with Human-in-the-Loop, to solve everyday catalog challenges at scale while retaining essential context. 

You achieve cleaner product data, streamlined seller/SKU onboarding, and consistent marketplace compliance, enabling your business to thrive on Amazon, Flipkart, Shopify, and your proprietary storefront.

1. Seller & SKU onboarding at scale
   Drive higher acceptance rates, shorten time-to-live, and guarantee accurate identifiers (GTIN/HSN) with AI-first enrichment and rigorous HITL/EITL review. Elevate your onboarding process to outperform competitors.
   
2. Product matching & de-duplication
   Effortlessly match identical or similar SKUs across all suppliers and channels. Remove duplicates, unify listings, and achieve stable pricing and analytics for a distinct market advantage.
   
3. Catalog cleansing & attribute normalization
   Unlock new growth by filling missing attributes, standardizing canonical values (sizes, materials, colors, units), and fixing variant/parent-child links. Increase both product discovery and SEO with confidence.
   
4. Search relevance & navigation tuning
   Transform on-site findability by optimizing taxonomy placement, synonyms, and attribute weighting. See measurable improvements in filter usage and PDP views with every enhancement.
   
5. Image QA & channel compliance
   Guarantee marketplace image compliance—enforce rules for dimensions, backgrounds, and angles, while proactively flagging low-quality images. Dramatically reduce listing rejections for maximum seller success.
   
6. Seasonal drops & surge operations
   Utilize elastic teams and proven playbooks to excel in peak seasons, rapid assortment refreshes, and flash sales—without sacrificing accuracy. Stay ahead during every surge operation.
   
7. Multilingual/localized listings
   Win new customers with human-verified translations and region-specific attributes (size charts, regulations). Boost conversion and adoption as you confidently expand into new markets.
   
8. Returns reduction via attribute fixes
   Take control of your returns—trace major return drivers to weak attributes, tighten specs and sizing, and watch refund rates fall. Build trust and stop revenue leakage.

ROI & Outcomes of Partnering with NextWealth

Run e-commerce catalog management as a governed service, combining AI automation with Human-in-the-Loop (HITL)/Expert-in-the-Loop (EITL) and measure lift against a clear baseline (Precision/Recall, TAT/SLA, coverage, rejection rate, returns, discovery/PDP views). Here’s what customers typically see:

1. Faster time-to-market for new SKUs
   25–40% faster SKU onboarding via AI first-pass product tagging, confidence routing, and HITL queueing.
2. Lower catalog operations cost vs pure manual
   Realize 20–35% lower cost per SKU by auto-approving high-confidence tags, optimizing HITL effort, and eliminating rework.
3. Increase product discoverability and conversions.
   Expand attribute coverage by 10–20% to enable better filters/facets and search relevance, often resulting in a 3–8% conversion lift in priority categories.
4. Minimize rejection rates on marketplaces.
   Achieve 30–60% fewer listing rejections by executing channel checks (Amazon/Flipkart/Shopify), performing ROF (reasons-of-failure) analysis, and updating playbooks.
5. Compliance with evolving taxonomy/attribute standards
   Implement versioned taxonomy and attribute normalization rules, establish auditable QA, and manage changes to maintain long-term marketplace compliance.

Future of E-Commerce Catalog Tagging 

E-commerce catalog operations are shifting from tool-led projects to service-led programs. GenAI speeds first-pass enrichment, while Human-in-the-Loop and Expert-in-the-Loop maintain accuracy, compliance, and context. 

Multimodal tagging and governed workflows will define how retailers scale product data.

1. Evolving role of Generative AI in retail catalog

Looking ahead, GenAI will rapidly boost first-pass product data enrichment (like titles, bullets, and attribute suggestions) while uncovering new patterns across categories. The real win? When GenAI’s outputs are guided by confidence thresholds, enhanced by Human-in-the-Loop review, and secured by clear audit trails.

2. Growth of Expert-in-the-Loop for compliance, edge cases, and niche categories

As policies tighten and new categories emerge, such as hazmat, electronics, or regulated apparel, Expert-in-the-Loop (EITL) shifts from a rare exception to a core teammate. Specialists actively curate gold sets, resolve tricky edge cases, and transform rejection logs into fresh rules—raising precision exactly where AI struggles most.

3. Multimodal tagging moves mainstream
   
   Retail data is increasingly multimodal. Reliable catalog tagging will fuse visual cues (pattern, neckline, material), textual specs, and even short-form video to resolve ambiguity. Expect higher accuracy from workflows that align computer vision checks with attribute normalization and taxonomy placement.
   
4. Service-driven partnerships replace one-time platform buy

This shift extends to how solutions are delivered. Tool-only implementations stall on governance. Retailers will favor managed e-commerce catalog management with AI + HITL/EITL, SLAs, and continuous improvement, so taxonomy, attributes, and acceptance rates keep up with changing rules and seasonal velocity.

Conclusion

Smarter catalog tagging = AI speed plus human expertise. 

Automated enrichment scales e-commerce data operations and Human-in-the-Loop with Expert-in-the-Loop maintains context, accuracy, and marketplace compliance. 

Treat catalog practice as an ongoing service, not a one-time tool choice, and you protect discovery, conversion, and returns while accelerating SKU onboarding.

NextWealth is a services partner for e-commerce data operations for a number of e-commerce enterprises.
We operate a governed e-commerce catalog management end-to-end: AI-first pass, reviewer workflows, expert escalation, QA, SLAs, and continuous improvement tied to business outcome

Contact our team at NextWealth to explore a pilot and see how AI plus human expertise can upgrade your e-commerce catalog practice at scale.

FAQs