Catalog systems built, verified, localized, and kept current

Every project starts the same way, and nobody prices it: a brand name, source files in different shapes, a target list. What the data covers and what is missing is unknown. Quote before that is answered and you are guessing.

A recon pipeline reads every source at once and executes automated data reconciliation against the target schema. It tests compatibility across databases, measuring coverage block by block. This automated reconciliation is the primary factor determining project complexity and timeline: straightforward when data aligns, engineered from scratch when divergent (why #ProjectBanana took 6 months of modeling vs 2 weeks for mapped feeds). It joins stock report, linesheets, master data and asset library on a verified key, measures coverage block by block, sorts the catalog into page types, writes the build spec, and lists every gap with its reason.

On the live assessment: 534 articles and 2,384 SKUs scored across 16 content blocks, 7 page types, 40 fields specified, 14 mockups from real data. Coverage came back honest: barcodes 100%, color 98%, composition 69%, descriptions 50%, highlight icons 8%. The client master file covered 56 of 534, wrong season.

No single pipeline reads every brand. Twelve watch brands, four sports brands and a jewelry catalog each publish a different data shape, a different variant model and different bot defenses.

Not a scraper. One shared core with an adapter per brand: collection agents read the source, processors match models and expand variants in parallel, price and stock are enriched, and a writer agent assembles an operations-ready workbook. Checkpoint and resume let each brand run in waves.

The pipelines collected the source behind everything on this page: 4,344 watch pages, 41,178 jewelry SKUs and over 90,000 sports SKUs. A real run finds 3,502 products, matches 575 of 575 models and writes 11,082 variant rows in 17.3 minutes.

Enterprise retail brands manage product media across closed, isolated DAM silos (Adobe Dynamic Media / Scene7, Bynder, and enterprise media hubs). Raw images, multi-angle variations (sole, toe, pair, details), and colorways lack unified export feeds, trapping merchants in days of manual downloading, mismatched variants, and broken links.

I built an autonomous extraction machine reverse-engineered for each server architecture. The engine deconstructs dynamic Scene7/AEM endpoint commands, probes and resolves angle parameters (DEFAULT, PAIR, TOE, SOLE, A), parses tokenized Bynder collections, applies perceptual hash deduplication (pHash), and structures master media into Shopify-ready schema.

Over 18,280 high-res master assets harvested across 3,620+ articles with 100% color-to-angle accuracy. Completely eliminates manual image handling, reducing catalog asset ingest from weeks to minutes with zero broken links.

A finished storefront of tens of thousands of pages hides its own defects: broken images, wrong or mismatched data, gaps, and rare edge cases that no sample check will ever surface.

I run the same parallel pattern over a live storefront: 12 to 24 agents sweep the store at once, each with its own brief, hunting broken images, data that does not belong, structural gaps and edge cases, and reporting findings as structured rows.

The needle gets found in the haystack. On the watch catalog one metafield error was quietly keeping 14 watches out of the catalog altogether, and a code bug had left wrong attribute and price values on a handful of random pages. Neither shows up in a sample check. The sweep finds them, in a fraction of the time a linear review would take and with about 80% less processing time.

Raw catalog data does not make a store shoppable. Manufacturer feature language is inconsistent, specs live in prose, and filters built on free text either return everything or nothing.

Feature language normalized into a fixed set and matched to icon sets, technical specs extracted or written per model, comparable rating scales derived for cushioning, flexibility and support, and filter attributes generated from real structured values.

The same feature reads the same way on every product page, and a shopper can compare models on a measure rather than a paragraph.

A catalog with no architecture is a pile. Categories, collections, landing structure and links have to be designed or nothing is findable, by shoppers or by search engines.

Category tree and collection logic designed alongside the data, landing structure defined, internal linking generated, and old-to-new page mapping produced on replatforms so rankings survive.

Every build leaves with a navigable, indexable structure instead of a flat product list.

A catalog at scale is only useful if someone can prove it is right, and no two catalogs are wrong in the same way. Watches fail on terminology and placeholder prices, apparel on images bound to the wrong color.

A gate between build and handover, rebuilt per pipeline: images probed live for size and reachability, translation checked against a locked glossary, prices range-checked, variants and cross-links resolved, every editorial decision recorded as a rule. On the watch catalog: a 10-check mapper QC, 14 client rules, 54 certification checks, 13 update-safety checks.

Every brand went out on a full pass rather than a promise: 54 of 54 checks green, image dimensions verified on 4,028 of 4,028 images. Every catalog leaves with its own audit trail.

Manual categorization of new products was slow, inconsistent, and did not scale across catalogs.

A fully automatic classifier that reads titles and descriptions, then maps products to categories, attributes, and taxonomy logic. Anything ambiguous or disputed is flagged rather than guessed, and routed to a second review pass.

98% of products classified with no human touch, the remaining 2% marked for a second review. A reusable pattern that carries to other catalogs.

A homeware brand was expanding from one WooCommerce catalog onto 4 UK Mirakl marketplaces, each with different templates, taxonomy, image rules, and SEO standards.

An automation workflow mapping one catalog through Mirakl into 4 retailer-ready templates: normalization, category mapping, compliance fields, image structuring, retailer attributes, and SEO content.

4 marketplace-ready templates delivered, 441 fields mapped, 2,414 SEO text items rewritten, average SEO score up from about 57 to about 90.

A practice and a body of work both need a public face that stays current, without a redesign or an agency every time something ships.

An automated publishing flow with no version limit: pages go live on demand, plus a password-gated submissions site with per-page encryption.

Roughly 95 live pages to date across a brand site, an artist portfolio, and gated client material. New work appears as it happens.

A live site had to stay editable by a non-technical owner without any risk of publishing a broken page.

WordPress to static, with 66 sections mapped so any edit can be located by description, and an edit agent that clones to draft, snapshots, and publishes only on an explicit phrase.

Fast, cheap to host, and safe to edit. Live and maintained.

Needed a lightweight pipeline-tracking tool, not a heavy Salesforce-style setup. Just status, notes, and next actions in one place.

A practical Mini CRM: lead status tracking, notes, next actions, and a simple follow-up structure.

A focused internal tool that keeps pipeline data organized, and a working demonstration of turning a personal ops problem into a usable application.