AI automation for retail — content, support, and returns ops
Product content at scale, support triage, returns automation for $10M to $100M retailers. $3,000/mo retainer.
Who this is for
Ops or digital director at a $10M to $100M retailer where content is slow, support is expensive, and returns processing is manual.
The pain today
- Product content for new SKUs is slow and inconsistent
- Support costs rise with customer count
- Returns processing is manual and slow
- Customer service cannot keep pace with holiday volume
- Platform AI does not cover brand-specific needs
The outcome you get
- AI automations for retail ops on $3,000/mo retainer
- Product content generation with brand voice at scale
- Support triage with on-brand response drafting
- Returns automation with policy-engine logic
- Integration with POS, ecommerce platform, and customer service
Retail-ops AI that actually pays back
Three areas deliver ROI. Product content — LLM drafts descriptions, bullet points, meta tags from structured product data. Human reviews and publishes. Cuts content time 60 to 80 percent. Support triage — LLM categorises and drafts responses for agent review. Cuts handle time 30 to 50 percent. Returns processing — LLM reads return requests, applies policy logic, drafts resolution (refund, replacement, store credit) for associate approval. Each removes repetitive typing while keeping human judgement on edge cases.
Product content and search enhancements
Product content at scale: catalog ingestion, LLM-drafted descriptions from structured specs, SEO metadata generation, variant differentiation. Human review before publish. For retailers with thousands of SKUs and seasonal rotation, this saves weeks of content work per season. On-site search enhancement: semantic search (understanding what customers mean, not just matching keywords). For retailers with large catalogs, semantic search can lift conversion 15 to 25 percent on existing traffic.
Returns and support triage
Returns: LLM reads request (reason, item, customer tier), applies policy engine, drafts resolution. Associate approves on one click for clear cases. Complex cases (fraud flag, heavy-returner, custom orders) route to supervisors with full context. Support: common issues (shipping status, return status, product questions) drafted first response. Agent reviews and sends. For retailers with holiday volume spikes, this is the difference between hiring seasonal staff and handling spike with core team.
Pricing and engagement model
$3,000/mo retainer. Covers AI integration, prompt engineering, ecommerce platform integration, monitoring, iteration. 14-day money-back guarantee. Cancel anytime. 100 percent code ownership under Work Made for Hire. LLM costs pass through — $300 to $2,000/month at mid-market retail. For retailers with very high content volume or seasonal spikes, cost optimisation matters significantly. Retailers often pair AI retainer with Applications subscription for deeper custom tooling.
Case: Imohub and Instill
Imohub: 120,000+ property portal with sub-500ms queries and 70 percent infra savings (Next.js, React, Laravel, MongoDB, Meilisearch, AWS, Docker). Performance and catalog discipline transfers to retail AI. Instill: self-initiated AI skills platform (Next.js 16, React 19, TypeScript, PostgreSQL, Vercel, MCP Protocol) with structured-prompt library. For retailers, these combine into retail AI that scales — fast catalog-driven AI content generation plus structured prompts for ops work.
When a retail-platform's built-in AI is enough
Shopify Magic, BigCommerce AI, Lightspeed AI — retail platforms ship basic AI features (product description generation, tag suggestions, smart segmentation). For retailers under $10M revenue with straightforward needs, platform AI may cover it at no extra cost. Custom retainer pays back when retailers need brand-specific voice, deeper integration, or ops automation beyond platform offerings. My target retailers are $10M to $100M where custom AI materially affects content velocity or support margin.
Recent proof
A comparable engagement, delivered and documented.
Rebuilt a real estate portal at a fraction of the cost
Rebuilt Imóveis SC's real estate portal as ImoHub — a faster, more scalable successor — handling 120k+ properties with sub-second search and drastically reduced AWS costs.
Frequently asked questions
The questions prospects ask before they book.
- How do you handle brand voice across many SKUs?
- Brand-voice prompts built from top-performing existing content plus brand guidelines. Product-type prompts specialise (apparel vs beauty vs home goods). AI generates, merchandiser or copywriter reviews before publish. For retailers with many brands under one company, per-brand prompt libraries. Voice stays consistent even across fast-growing catalog and staff turnover.
- Can AI handle returns processing?
- Yes, for policy-driven decisions. LLM reads return request, extracts reason and item, applies return policy. Drafts resolution — refund to original method, store credit, replacement, or escalation. Associate approves or overrides. For clear cases, one-click approval. For edge cases (fraud signals, damaged items, custom orders), human review always. Cuts returns processing time significantly while keeping fraud control human.
- How much do API costs run?
- Typical mid-market retail: $500 to $2,500/month in API costs. Product content at $0.02 to $0.10 per SKU. Support triage at $0.005 to $0.03 per ticket. Returns processing at $0.01 to $0.10 per request. For retailers with seasonal spikes, costs flex with volume. Cost optimisation important at scale — caching, routing, compression.
- Can you integrate with Shopify, BigCommerce, or Magento?
- Yes. Shopify (both classic and Plus), BigCommerce, Magento/Adobe Commerce all have APIs. AI outputs push to platform as product content, customer support responses, or order actions. For customer service platforms (Gorgias, Zendesk, Kustomer), separate integration for ticket handling. Integration scope depends on which surfaces AI touches.
- How do we handle evals for retail AI?
- Golden-set evals per content type (product descriptions, support responses, return resolutions). Production sampling with human review. For high-volume ops, conversion-rate impact of AI-generated content tracked vs human-written baseline. Evals catch regressions early; conversion tracking ensures AI is net-positive. For retailers scaling AI across multiple ops functions, eval infrastructure becomes a first-class asset.
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