A set of applied fashion AI experiments exploring image retrieval, model behavior, OCR extraction, and the limits of visual inference.
Interface studyApplied experiments in similarity retrieval, CLIP benchmarking, and OCR extraction.
Overview
A useful system starts with a clear reason to exist.
01 / What was built
A set of practical experiments for retrieval, OCR, model comparison, and fashion data analysis.
02 / Who it was for
Teams evaluating what multimodal AI can reliably infer from imperfect retail data.
03 / Why it mattered
The experiments made model strengths, weaknesses, and data limits visible before a broader commitment.
Business challenge
The question was not what technology to use. It was what needed to work better.
Fashion data is visually rich but inconsistent. The work needed to test what current multimodal models can reliably infer across noisy, cross-store datasets.
Plumfind role / research, strategy, design, engineering, deployment, and support planning.
Images alone rarely tellthe full story.
Discovery & research
Reduce uncertainty before production.
01
Business context
Fashion data is visually rich but inconsistent. The work needed to test what current multimodal models can reliably infer across noisy, cross-store datasets.
02
Research
We defined experiments around visual similarity, color classification, label extraction, cross-store shifts, and second-hand product conditions.
03
Experience direction
The research interface presents comparisons, confidence signals, and source evidence so observers can understand where the model is strong and where it is uncertain.
04
Production plan
Demos were deployed through lightweight cloud infrastructure using EC2, Lambda, API Gateway, and a WordPress interface.
Research artefact
Clarity lives in the details.
We defined experiments around visual similarity, color classification, label extraction, cross-store shifts, and second-hand product conditions.
Embedding search
Similar visual attributesOCR: brand / size / materialModel comparison: variable color confidence
Solution
What Plumfind designed and built.
The research interface presents comparisons, confidence signals, and source evidence so observers can understand where the model is strong and where it is uncertain.
01
Similarity retrieval
CLIP embeddings were used to explore visual similarity as an alternative to brittle hand-authored classification.
02
Evidence-led comparisons
The research interface exposed comparisons, source evidence, and confidence differences across model behavior.
03
Structured attribute extraction
OCR experiments tested whether labels and product imagery could provide reliable structured signals.
Architecture
The system is a set of deliberate connections.
Each layer was selected to support the business workflow, not to make the technical picture more complicated.
01Cross-store inputs
02Multimodal methods
03Evaluation view
04Lightweight demos
01
Cross-store inputs
Product imagery, labels, and varied source conditions provide the experimental corpus.
02
Multimodal methods
Embedding, OCR, and model comparison workflows create different routes to usable evidence.
03
Evaluation view
Retrieval results and confidence signals make behavior inspectable rather than opaque.
04
Lightweight demos
AWS services and a WordPress interface make findings accessible for review and iteration.
Implementation
From considered direction to a production system.
01
Experience and system design
The research interface presents comparisons, confidence signals, and source evidence so observers can understand where the model is strong and where it is uncertain.
02
Engineering
We built CLIP embedding retrieval, model comparison workflows, OCR extraction, store-level analysis, and a small fine-tuning experiment on AWS EC2.
03
Deployment and ownership
Demos were deployed through lightweight cloud infrastructure using EC2, Lambda, API Gateway, and a WordPress interface.
Business impact
What changed because the system became clearer.
01
Built working fashion similarity retrieval
02
Compared vision-language model behavior
03
Validated OCR for structured attributes
04
Documented domain-shift limits across stores
Lessons learned
Data quality dominates model selection
Retrieval is often more robust than classification
OCR can be more trustworthy than visual inference for exact attributes
Technologies used
Selected for the work they needed to do.
Technology choices were made around fit, maintainability, and the operating reality of the project.
01
Visual retrieval
CLIP, SigLIP
Selected to test semantic and visual similarity across inconsistent product imagery.
02
Attribute evidence
Qwen OCR, DeepSeek OCR
Selected to test whether exact product details could be extracted more reliably from labels and imagery.
03
Research delivery
AWS EC2, Lambda, API Gateway
Selected to support lightweight, reviewable experiments without turning research into a premature product.
