AI Training Data for Commerce

General web crawl datasets like Common Crawl contain raw HTML from billions of pages with no semantic structure, heavy duplication, and inconsistent formatting. Ecommerce training data is specifically curated from product listings with verified labels, consistent schemas, and structured attributes. Every record has been deduplicated, normalized, and quality-checked. This domain specificity means models trained on our datasets learn ecommerce language patterns, product taxonomy relationships, and pricing dynamics that general corpora cannot teach — resulting in significantly better performance on commerce-specific tasks.

We use a multi-stage labeling pipeline that combines automated extraction with human-in-the-loop verification. First, our AI extraction system pulls structured data directly from source pages with 99.4% field-level accuracy. Second, statistical validation checks flag records where extracted values fall outside expected distributions for their category. Third, a sampling-based human review process verifies label accuracy on randomly selected subsets from every batch. This pipeline consistently achieves 98.7% label accuracy across our full catalog, and every dataset ships with per-record confidence scores so you can set your own quality thresholds.

Yes. We offer both pre-built category datasets and custom curation for any product vertical or marketplace. Pre-built datasets cover 200+ retail categories across major platforms including Amazon, Walmart, Target, Best Buy, and hundreds of specialty retailers. For custom requests, specify your target categories, attribute requirements, volume needs, and delivery format — and we will build a dataset tailored to your model training requirements. Most custom datasets are ready within 5-10 business days depending on scope.

Standard datasets receive weekly refreshes that add new products, update prices, and incorporate recent reviews. For price history and competitive intelligence datasets, daily snapshots are available. We also offer continuous streaming feeds for clients who need real-time data for online learning systems. Every update is versioned, so you can track exactly what changed between releases and maintain reproducible training runs.

We deliver in all standard ML formats: Parquet (recommended for large datasets due to columnar compression), JSONL (for record-by-record processing), CSV (for compatibility with spreadsheet and legacy tools), and TFRecord (for direct TensorFlow ingestion). Image datasets can be delivered as URL lists with metadata or as pre-downloaded archives with matched metadata files. We also support custom schema mapping to match your existing pipeline format, so no transformation code is needed on your end.

All review datasets pass through an automated PII scrubbing pipeline that detects and redacts names, email addresses, phone numbers, physical addresses, order numbers, and other identifying information using NER models trained specifically for this task. The scrubbing pipeline achieves 99.6% PII detection accuracy. Redacted text preserves sentence structure and sentiment so the data remains useful for NLP training. We also remove reviews that contain excessive personal narrative with no product-relevant content.

Standard image classification datasets like ImageNet provide images with single category labels. Our image-text pairs provide product images matched with rich textual context: the full product title, a description excerpt, extracted visual attributes (color, pattern, material, shape), category labels at multiple hierarchy levels, and price context. This multi-signal pairing is specifically designed for training multimodal models that need to understand the relationship between visual product features and their textual descriptions — tasks like visual search, image-to-text generation, and cross-modal retrieval.

Yes, this is one of the most common use cases. Cold-start occurs when a recommendation engine encounters new products or users with no interaction history. Our dense product feature vectors — with 30+ attributes, category embeddings, image features, and price context per record — give content-based recommendation models the signal they need to make relevant suggestions for new products from day one. Clients who train on our datasets typically see a 40-60% improvement in cold-start recommendation relevance compared to sparse internal catalog data.

All prices in our datasets are stored in their original currency with ISO 4217 codes, and we include a USD-normalized column computed using daily exchange rates for cross-market comparison. Each price record includes the marketplace identifier, timestamp, promotional flags, stock status, and the number of competing sellers at the time of capture. For products sold across multiple marketplaces, records are linked by a unified product ID so you can study cross-marketplace pricing dynamics in a single query.

We deliver taxonomies in multiple graph-friendly formats: RDF/OWL for semantic web applications, JSON-LD for linked data pipelines, Neo4j-compatible CSV for graph database import, and custom edge-list formats for embedding frameworks like TransE, RotatE, and ComplEx. Each taxonomy includes node attributes, edge types (is-a, part-of, related-to, compatible-with), and confidence scores for inferred relationships. We also provide pre-computed alignment tables mapping our taxonomies to Google Product Category, Amazon Browse Tree, and UNSPSC standards.

Dataset sizes vary by type. A full product catalog dataset with 500M records in Parquet format is approximately 2TB compressed. Image datasets with pre-downloaded images range from 5-50TB depending on category and image count. Review corpora typically run 200-500GB. For processing, we recommend cloud object storage (S3 or GCS) for raw data and a distributed compute framework like Spark or Dask for preprocessing. We provide data loading utilities for PyTorch and TensorFlow that handle sharding, batching, and streaming automatically.

Both. Raw datasets give you full control over feature engineering and embedding generation. For teams that want to skip the embedding step, we offer pre-computed product embeddings using BERT-based text encoders and ResNet/CLIP-based image encoders. These embeddings are 768-dimensional vectors that capture semantic product similarity and can be used directly for nearest-neighbor search, clustering, and as input features for downstream models. Pre-computed embeddings are included at no additional cost with any dataset subscription.

Aspect-based sentiment analysis (ABSA) goes beyond simple positive/negative classification to identify sentiment toward specific product aspects — quality, value, shipping speed, fit, durability, and others. A review saying 'great sound but terrible battery life' is positive overall for sound quality but negative for battery. Our review datasets include aspect-level sentiment annotations for 15+ common aspects per product category, enabling you to train models that understand these nuances rather than just predicting an overall sentiment score.

We apply systematic bias auditing across every dataset. This includes verifying balanced representation across price tiers, product categories, seller types, geographic markets, and review demographics. For image datasets, we ensure diversity in product presentation styles, backgrounds, lighting conditions, and photography angles. Statistical tests check for over-representation of specific brands or sellers. Every dataset ships with a bias report documenting the distribution across key dimensions, and we offer custom resampling and stratification to match your target distribution requirements.