Introduction

How AI Automation Helped Us Scale from 10 to 1,000 Listings isn’t a theory piece—it’s a field note. We’ll unpack the lean stack, the exact prompts, the guardrails that prevented messy outputs, and the weekly rhythms that kept quality rising while the team stayed small.

Principles: accuracy over speed, policy-safe language, real photos, and human approvals on anything public-facing.

1) The backstory: from 10 to 1,000

We were posting manually with copy-paste fatigue. The first breakthrough was converting our scattered notes into a clean spreadsheet: one row per SKU with title seeds, specs, condition, and media paths. AI didn’t replace judgment—it removed the grunt work between “idea” and “ready to publish.”

2) Stack overview: data → AI transforms → QA → channels

3) Pipeline blueprint (CSV to published)

1. Ingest: upload CSV (new rows only) ➜ validate columns.
2. Transform: AI drafts titles/descriptions; extracts attributes; suggests category.
3. Media attach: link to image folder; auto-rename and compress.
4. Dedupe: fuzzy match on title+brand+image hash ➜ flag candidates.
5. QA: reviewer checks deltas vs source; approves or kicks back.
6. Export: assemble channel-specific CSVs or push via API.
7. Monitor: track time-to-live, approvals, error notes, and removals.

4) Prompt library: titles, descriptions, attributes

Title pattern (items)

{Category} — {Key Feature}, {Brand/Model/Size}, {Condition 8/10}, {Area}
Rules: ≤70 chars, nouns over adjectives, no fluff.

Description blueprint

Intro (≤120 words): what it is, standout feature, condition, availability.
Bullets (3): 
• Specs (model/year/size)
• Logistics (pickup window / shipping / fees)
• Value (receipts/accessories/warranty)
CTA (1): DM "TIMES" for pickup window.

Attribute extractor

From these fields {brand, model, dimensions, material, condition_text}, return:
brand, model, size, color, material, condition_score(1–10), defects_note(≤20 words).
Refuse to invent missing specs.

5) Images: rights, naming, quick edits

• Use real photos (licensed if not yours). Keep receipts/serials on file.
• Rename files to {sku}_{angle}.jpg; compress to web-safe sizes.
• Hero: bright, level, shows scale (square or 4:5). Close-ups for defects build trust.

6) Deduping & normalization

Normalize brand names, sizes, and units. We hashed images and used fuzzy matching (title + brand + size). Anything with a match score above a threshold hit the “Needs Review” queue.

7) QA gates: what we approve and why

8) Policy & risk checks

• Block disallowed items/phrases automatically.
• Use neutral, factual language; no health/legal promises.
• Store change logs for audits (who approved what, when).

9) Throughput math & batching strategy

We measured “listings per person-hour.” Batching similar SKUs let AI reuse context and cut editing time. Daily goal: 100 listings with two reviewers and one media lead.

Tip: lock your prompt and style guide for one week at a time—tune weekly, not hourly.

10) Team roles & SOPs

• Pipeline Owner: ensures sheet health, tracks KPIs, runs retros.
• Editor/QA: approves outputs, enforces policy and voice.
• Media Lead: manages photos, renaming, compression, and hero selection.

All steps live in one SOP with GIFs and one-line checklists per gate.

11) KPIs & dashboard

UTM idea for links: utm_source=marketplace&utm_medium=automation&utm_campaign=listing_scale_2025

12) 30–60–90 day rollout plan

Days 1–30 (Foundation)

1. Clean one category sheet; lock headers and validation.
2. Write the style guide; finalize title/description prompts.
3. Publish 25 listings/day; start the QA checklist.

Days 31–60 (Momentum)

1. Add dedupe & policy checks; introduce media SOP.
2. Hit 300–500 total listings; start basic analytics.
3. Hold weekly retros; prune steps that don’t add value.

Days 61–90 (Scale)

1. Expand categories; add API export or feed manager.
2. Target 1,000 listings with stable approval and low error rate.
3. Document everything and train backups.

13) Troubleshooting table