work project
AI Content Agent for Ecommerce Catalog
Services
- →AI content agent design and brand voice prompt engineering
- →WooCommerce API integration — product read and write
- →Bulk content generation: descriptions, SEO metadata, category copy
- →Semantic internal linking via vector embeddings
- →Review queue and staged publishing workflow
- →Parallel batch processing for large catalogs
Deliverables
- ✓Product description generator — long form, short form, SEO fields
- ✓Category copy and landing page text generation
- ✓Internal linking suggestions based on semantic similarity
- ✓Structured JSON output pushing directly to WooCommerce fields
- ✓Review-before-publish queue for quality control
Challenge
An ecommerce store with several thousand SKUs had product pages that ranged from a single sentence to a copy-pasted supplier description. Search rankings were poor, conversion rates on cold traffic were low, and the marketing team had a backlog of content work they would never get through manually. Writing one good product page takes 20–40 minutes; writing a thousand takes months.
Options Considered
- Freelance copywriters — quality was good but cost per SKU was high and turnaround slow. Not viable for ongoing catalog growth.
- Generic AI writing tools (Jasper, Copy.ai) — fast, but disconnected from actual store data. Output required heavy editing to fix factual errors and align with brand voice.
- Custom AI agent with direct WooCommerce API access — chosen. The agent reads product attributes, category context, and competitor positioning directly from store data, producing accurate, on-brand content with minimal review.
Decision
The agent pulls product data via WooCommerce API — title, attributes, category, price tier, existing description — and generates a full content set: long-form description, short description, SEO title, meta description, and internal link suggestions to related products. A brand voice guide is embedded in the system prompt. Output is staged for review before publishing or pushed directly for low-risk SKUs.
Implementation
A Python orchestration layer batches products by category and runs generation in parallel using GPT-4. Each batch uses a category-level prompt that adds context about the target customer and competing products. The output is structured as JSON matching the WooCommerce fields — title, description, short_description, yoast_seo — and pushed back via the REST API. A review queue lets the team approve or edit before publish.
Internal linking is generated by embedding all product titles and finding the top-k semantically related products per page — surfaced as suggested anchor text and target URL pairs for the editor to insert.
Outcome
500 product pages updated in the first run, cutting estimated manual effort from 3 months to 2 days. Organic search impressions increased 40% within 60 days. Average description length increased 4× with no increase in bounce rate.
Open for contract collaboration
I am available for contract-based collaboration. If you have an interesting project idea, schedule a call via Calendly.
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