Scaling AI catalog quality across 1.69M+ data points

For a leading e-commerce enterprise — AI-driven catalog validation across attributes, images, taxonomy, and multi-format content.

97%

Accuracy Achieved

1.69M+

Data Points Processed

Specialised Workflows

About The Client

A leading Indian e-commerce enterprise operating one of the country's largest product catalogs — spanning apparel, accessories, electronics, and non-apparel categories across millions of active listings. The client had invested significantly in AI-driven catalog automation — deploying models across attribute prediction, image evaluation, taxonomy classification, and generative AI-led content enrichment. With outputs spanning both structured data (attributes, metadata) and unstructured data (images, videos, enriched content) across 30+ distinct workflows, maintaining quality and consistency at scale had become a critical operational challenge.

Challenge/Problem

Attribute variability
AI-generated values for colour, pattern, sleeve type, and brand were inconsistent, amplified in bundled and multi-category listings where strict grading was difficult to enforce.

Image quality & compliance
Product images carried blur, distortion, compression artefacts, embedded promotional overlays, improper cropping, and partial visibility, all degrading model input quality.

Taxonomy & brand misalignment
Misclassified products and discrepancies between visual cues and structured metadata reduced catalog reliability and created downstream search and discovery failures.

Generative AI output validation
AI-enriched content required multi-dimensional checks across product fidelity, visual realism, anatomical correctness, and text accuracy, well beyond what traditional rule-based validation could handle.

Dynamic content at scale: video & live commerce
Expansion into video and live commerce introduced frame-level inconsistencies and real-time moderation needs that static catalog workflows had no mechanism to address.

Approach

Attribute intelligence validation
AI-generated attributes were systematically verified using product images and metadata. A structured grading framework classified outputs by confidence level, enabling consistent decisions even in complex bundled or multi-category scenarios and producing high-quality datasets for model retraining.

Image quality & compliance layer
Every image was assessed for clarity, distortion, resolution, and structural framing, and checked for non-compliant elements including promotional overlays, embedded size charts, and warranty visuals. This gave the client's AI systems clean, high-fidelity visual inputs.

Classification & brand alignment
Visual cues were cross-verified against structured metadata to identify and correct category misassignments. Brand information was validated across both image and attribute layers to prevent misuse and maintain catalog integrity.

Generative AI output evaluation
AI-generated and enriched content was evaluated across product fidelity, visual realism, text accuracy, anatomical correctness, and scale consistency. Corrected outputs and detailed feedback were fed back into the retraining pipeline continuously.

Video & live commerce validation
Frame-level validation was introduced for video formats, detecting inconsistencies across frames and assessing overall visual quality. Real-time tagging and moderation capabilities helped maintain compliance in live commerce environments.

Closed-loop improvement
Together, these layers formed a closed-loop system where every validated and corrected output fed back into the AI pipeline, creating a self-improving mechanism for model performance at scale.

Results

Scaled AI validation
AI validation was scaled across millions of catalog entries, with consistent quality enforced across structured attributes, images, videos, and enriched content.

Reduced AI output variability
The solution improved reliability across attribute prediction, image analysis, taxonomy classification, and brand alignment.

Ground truth data creation
High-quality ground truth data was created at scale, directly strengthening model training datasets and evaluation pipelines for ongoing AI improvement.

Extended quality control
Quality control expanded beyond static catalog content into video and live commerce, helping the client maintain standards as their AI use cases moved into newer content formats.

Continuous feedback loop
Validated outputs and correction data were fed back into the AI pipeline, enabling continuous improvement in model accuracy over time.