E-commerce AI Training Data & Annotation

Retail

Our data labeling services cover the entire spectrum of retail LLM, from annotating product catalogs and customer interactions to training models that deliver accurate demand forecasting.

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LLM Data Types for Retail

Sentiment Annotation

Indicating the emotional tone (positive, negative, neutral) in the text. Analyzing customer reviews about products and services, monitoring complaints and identifying problem points in the customer experience.

Named-Entity Recognition (NER)

Isolating specific entities in the text: brand names, product names, categories, features, stores, locations. Creating structured data from product descriptions, reviews and customer requests; automatic catalog generation.

Intent Annotation

Determining the user's intent in the request. Teaching chatbots to understand whether the buyer wants to find a product, get advice, check the status of an order or return the product.

Question/Answer Annotation

Creating "question - correct answer" pairs based on retail data. Teaching LLMs to answer complex questions about product features, availability, model comparisons, delivery terms and return policies.

Product Attribute Annotation

Tagging and standardizing product attributes such as size, color, material, specifications, compatibility, and variants. Improving product search, filtering, comparison tools, and recommendation accuracy across retail platforms.

Relevance & Ranking Annotation

Evaluating how well products, answers, or recommendations match a user’s query or intent. Training LLMs and search systems to rank products more accurately, improve on-site search quality, and increase conversion rates.

LLM Data Services for Retail

Domain Data Collection and Cleaning

Generation, collection, and standardization of large amounts of specialized data for model training.

Specialized Data Annotation

Engaging experts to label data to transform input into structured training material.

Model Fine-Tuning

Adapting generic LLMs to client-specific data so that the model better understands industry terminology and context.

Accuracy and Hallucination Audit

Systematically checking the model’s generated responses for factual inaccuracy and fabricated information (hallucinations) to ensure reliability.

Prompt Engineering

Development and optimization of prompts to maximize the quality and predictability of the model’s output.

LLM Monitoring and Support

Continuous monitoring of model performance in a production environment, tracking data drift and using feedback for regular updates.

LLM Use Cases in the Retail Industry

AI-Powered Retail Analytics and Demand Forecasting

Harnessing the power of LLMs, retailers gain continuous insight into sales dynamics, seasonal trends, and customer behavior. With high-quality annotated datasets and data from embedded AI sensors on shelves and smart devices, LLMs correctly interpret historical records and sales patterns. As a result, LLMs can:

Forecast product demand across seasons and regions
Optimize inventory levels and replenishment cycles
Identify sales trends and promotion opportunities

Personalized Shopping and Recommendation Systems

LLMs enable highly individualized shopping experiences by analyzing customer purchase history, preferences, and behavior. Deeply annotated customer and product data, combined with insights from physical AI devices like in-store cameras and motion sensors, ensures recommendations are both accurate and actionable. The system can:

Generate personalized product recommendations
Tailor promotions to individual customers
Optimize assortments based on shopper preferences

Automated Retail Content Generation

Retailers can scale content creation across channels using LLMs trained on annotated product and marketing data. This significantly reduces manual effort and accelerates time to market. LLMs can:

Generate product descriptions and specifications
Create advertising copy and marketing materials
Produce newsletters, social media posts, and reviews

Intelligent Customer Engagement and Virtual Assistants

LLM-powered chatbots provide instant, contextual support to customers across digital touchpoints. By leveraging annotated conversational data, these assistants deliver reliable and coherent answers, improving customer satisfaction and reducing support costs. As a result, the system can:

Answer questions about product availability and pricing
Explain product features, promotions, and delivery options
Maintain context-aware, natural conversations

Sales and Merchandising Decision Support

LLMs assist retail teams by analyzing pricing strategies, product placement, and campaign performance. Structured annotations and input from embedded AI edge devices on shelves and POS systems ensure accurate pattern recognition. LLMs can:

Evaluate the effectiveness of pricing strategies
Optimize product placement and shelf layout
Recommend optimal assortments in real time

Customer Feedback and Satisfaction Monitoring

LLMs analyze large volumes of customer feedback, ratings, and complaints to uncover insights into product quality and service performance. Accurate sentiment and feedback labeling improves model reliability and helps retailers proactively address customer needs. This enables the system to:

Detect recurring product or service issues
Analyze customer sentiment and satisfaction trends
Predict customer needs and loyalty risks

FAQ

How to ensure accurate labeling of products and reviews?

Accuracy is achieved by involving retail experts and providing clear guidelines. Measuring inter-annotator agreement (IAA) helps reduce subjectivity when labeling categories, features, or customer intent.

Which types of retail data are hardest to annotate?

The most challenging are multimodal data, such as product images with text descriptions, and customer reviews with ambiguous or sarcastic language.

Why is annotation necessary if sales and inventory data are already available?

Sales data shows what happened, but annotation explains why. Annotated data is essential for training LLMs to understand product attributes, customer preferences, and contextual trends.

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"Delivering Quality and Excellence"

The upside of working with Keymakr is their strategy to annotations. You are given a sample of work to correct before they begin on the big batches. This saves all parties time and...

"Great service, fair price"

Ability to accommodate different and not consistent workflows.
Ability to scale up as well as scale down.
All the data was in the custom format that...

"Awesome Labeling for ML"

I have worked with Keymakr for about 2 years on several segmentation tasks.
They always provide excellent edge alignment, consistency, and speed...