Portfolio audit across 12 client stores
Batch scan, branded reports, 4 remediation engagements
Context
A Shopify Plus agency managing 12 e-commerce clients wanted to proactively assess AI agent compatibility across their portfolio. None of the clients had asked about AI readiness yet, but the agency saw an opportunity to lead the conversation with data rather than wait for client requests.
Initial scan results
Portfolio average
Can AI agents find your products?
Can AI agents complete purchases?
Is your store safe for automated transactions?
How many AI platforms can interact with your store?
Do AI agents understand your product data?
Portfolio breakdown
| Store | Score |
|---|---|
| DTC apparel brand | 18/100 |
| Home goods retailer | 22/100 |
| Pet supplies store | 15/100 |
| Fitness equipment seller | 28/100 |
| Gourmet food shop | 31/100 |
| Baby products brand | 20/100 |
| Outdoor gear retailer | 42/100 |
| Skincare brand | 26/100 |
| Jewelry store | 19/100 |
| Electronics accessories | 35/100 |
| Candle and home fragrance | 17/100 |
| Sustainable fashion brand | 24/100 |
The workflow
Exported client store URLs from their project management system
The agency maintained a spreadsheet of all 12 client store domains. They formatted these as a simple CSV file with one URL per line.
Ran colter check --batch across all 12 stores
A single batch command scanned all stores in parallel. Each store received a 5-dimension readiness score: Discovery, Transaction, Security, Ecosystem, and Content Quality. Total scan time was under 3 minutes.
Reviewed the portfolio-level results
9 of 12 stores scored below 30/100. The remaining 3 scored between 31 and 42. No store reached 60/100 or above. The portfolio average was 24/100 -- below the industry average of 30/100.
Generated branded PDF reports for each client
Each report included the store's composite score, per-dimension breakdown, specific fix actions ranked by expected point improvement, and competitive context showing how the store compared to the 363-store industry benchmark.
Presented findings as part of quarterly business reviews
The agency framed AI readiness as an emerging channel risk. Reports were delivered alongside existing SEO and performance reviews. Four clients authorized remediation work immediately, with the remaining eight requesting follow-up in the next quarter.
Key findings
- --9 of 12 stores scored below 30/100
- --3 stores scored between 31 and 42
- --No store reached 60/100 or above
- --Portfolio average: 24/100, below the 363-store industry average of 30/100
- --Most common gap: missing JSON-LD product schema (10 of 12 stores)
- --Second most common: no sitemap.xml or robots.txt AI directives (8 of 12)
- --Security dimension was the highest-scoring across the portfolio (avg 48/100)
- --Content Quality was the weakest dimension (avg 12/100)
Outcomes
- +4 of 12 clients authorized remediation engagements immediately
- +Agency positioned AI readiness as a new recurring service line
- +Branded reports created a professional deliverable at minimal marginal cost
- +The 8 remaining clients requested follow-up in the next quarterly review
- +Total agency cost: one Agency plan ($199/month for 3 merchants + $29/merchant for 9 additional = $460/month)
This case study describes a representative workflow based on real data patterns from Colter's batch scan of 363 e-commerce stores. No company names, individuals, or direct quotes are used. Score breakdowns reflect realistic distributions observed in the scan data. This is a pre-launch example, not a customer testimonial.
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