Skip to main content
Prospect endpoints combine readiness data (from scans) with demand signals (public intelligence, synthetic tests, AI search mentions, telemetry) to produce an opportunity_score — the product of demand and readiness gap: 
opportunity_score = demand_score × (1 − readiness_score / 100)
High opportunity = the merchant is getting agent interest and would benefit most from an agentic-commerce integration.

​
GET /v1/scanner/prospects

Scope required: read   Cost: 0 Query parameters:
ParamNotes
categoryretail, saas, marketplace, …
regionlatam, north_america, …
min_opportunityMinimum opportunity_score (0–100)
prioritycritical, high, medium, low
limitDefault 50, max 200
curl -H "Authorization: Bearer $SCANNER_KEY" \
  "https://scanner.getsly.ai/v1/scanner/prospects?priority=critical&region=latam&limit=50"
Each prospect is a ProspectScore.

​
GET /v1/scanner/prospects/heat-map

Category × region matrix: aggregate readiness, demand, opportunity, and top priority per cell. Scope required: read   Cost: 0 
{
  "data": [
    {
      "category": "retail",
      "region": "latam",
      "merchant_count": 82,
      "avg_readiness": 41,
      "avg_demand": 68,
      "avg_opportunity": 58,
      "top_priority": "high"
    },
    ...
  ]
}
Sort order is descending by avg_opportunity.

​
GET /v1/scanner/prospects/export

CSV for CRM import. Same filters as /prospects. Scope required: read   Cost: 0 
curl -H "Authorization: Bearer $SCANNER_KEY" \
  "https://scanner.getsly.ai/v1/scanner/prospects/export?priority=high&limit=500" \
  -o prospects.csv
Columns: domain, merchant name, category, region, readiness score, demand score, opportunity score, sales priority, and the four demand-sub-score components.

​
Priority thresholds

Opportunity scorePriority
> 80critical
61 – 80high
41 – 60medium
≤ 40low

​
Demand score composition

demand_score = 0.30 × public_intelligence
             + 0.25 × synthetic_test
             + 0.25 × observatory
             + 0.20 × telemetry
  • public_intelligence — matches against the demand_intelligence data points for the merchant’s category/region.
  • synthetic_test — weighted on how often agents fail at the merchant’s checkout (more failures = more demand for an integration).
  • observatory — AI search engine mentions (ChatGPT, Perplexity, Claude) and MCP-registry listings for the domain.
  • telemetry — real agent checkout attempts observed in production traffic.