Spec-AI — Computer Vision for Packaging Authenticity Assessment

Type: Commercial AI product · Role: AI/ML engineering and system architecture · Source: Private

Problem

Product packaging can be difficult to assess consistently from photographs. Spec-AI explores how computer vision and structured reference information can help evaluate whether visible packaging resembles known authentic examples.

Scope: This is a visual packaging-consistency assessment, not a chemical test of the product or a guarantee of authenticity.

Engineering approach

The system combines image preprocessing, object/label segmentation, optical character recognition, deep visual representations, brand/SKU identification, and anomaly-based authenticity assessment. A Python/FastAPI API serves predictions; a Redis-backed Celery worker can execute inference. Model assets are versioned separately from application code using DVC.

My engineering contribution

Work spans ML pipeline design and integration, API development, model packaging, cloud deployment workflows, and operational debugging. The architecture required attention to compatibility between feature extractors and downstream model artifacts, repeatable deployment, and resource-intensive inference.

Architecture and deployment

At a high level, a client submits a bottle photograph; the API validates and routes it to image analysis; the pipeline extracts visual and text evidence and produces a structured result. The reviewed repository includes a GitHub Actions → container registry → DigitalOcean GPU host deployment configuration, with DVC-managed assets and an external Redis service. This describes inspected code/configuration, not independent confirmation of current production uptime or model accuracy.

Results and limitations

The code supports structured brand and packaging-consistency outputs, reasoning fields, and retake guidance. Independently validated accuracy, false-acceptance rates, and latency figures are not included in the reviewed documentation, so none are claimed here.

Further reading

Engineering deep dive: Building a Modular Computer Vision Inference Service

Commercial implementation and model artifacts are private. This page deliberately omits proprietary decision thresholds, detailed model configurations, and infrastructure identifiers.