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Syntalic Promo Intelligence
x402shopping
Syntalic Promo Intelligence is a paid API for AI agents from api.syntalic.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-09-14).
Analyzes promotional activity within a product category — promo frequency, average discount depth, and max discount — aggregated by brand or retailer over a date range.
Price / call$0.01
Success—
StatusUnknown
How this service works
Analyze promotional activity within a category - promo frequency, average and max discount depth - over a date range. Pivot the breakdown with `aggregate_by`: default `brand` ranks brands within the category; `retailer` ranks retailers (use together with `brand=<name>` to answer 'which retailers run the deepest promos on Brand X in Category Y'). The response key mirrors the dimension: `brands: [...]` or `retailers: [...]`.
Output
Returns a list of brands or retailers ranked by promotional activity within the requested category, including promo frequency (how often items go on sale), average discount percentage, and maximum discount depth observed over the specified date range. The response key mirrors the aggregation dimension: `brands: [...]` for brand aggregation or `retailers: [...]` for retailer aggregation.
Example use cases
Example prompt
“Which retailers have been running the deepest promotions on Apple products in the electronics category over the last 30 days in the US? Rank them by average discount depth.”
Technical details
Endpoint
GEThttps://api.syntalic.com/v1/marketing/promo-intelligenceWhen to prefer
Use this endpoint when you need to understand the promotional landscape within a product category — who discounts most aggressively, how deep discounts go, and how frequently promotions occur. It is the right choice over Deal Finder (which returns live discounted products) or Brand Tracker (which tracks a single brand's metrics over time) when the goal is cross-brand or cross-retailer comparison of promotional behavior. Especially useful for competitive benchmarking, retailer partnership analysis, or identifying which retailers most aggressively promote a specific brand.
Side effects
- Unrecognized or ambiguous category name fails to resolve through fuzzy matching — returns an error or empty results
- Date range too narrow returns sparse or zero promo activity
- Invalid country code (outside 'us'/'ca') returns a validation error
- Brand name not found in the specified category returns an empty result set
- Missing required `category` parameter returns a 400 validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| to | string | End date (ISO 8601, defaults to now) |
| from | string | Start date (ISO 8601, defaults to 30 days ago) |
| brand | string | Optional brand filter — limit aggregation to products of this brand (case-insensitive) |
| country | string | Country (us or ca) |
| categoryrequired | string | Product category (e.g., electronics, grocery, beauty). Fuzzy human-readable input — the resolver maps it through tier 1-4 (id → exact name → trigram → embedding kNN) to a canonical category_id. Pair with `category_id` if you have a deterministic id from a prior call. |
| aggregate_by | string | Group-by dimension. `brand` (default) ranks brands within the category. `retailer` ranks retailers — pair with `brand` to answer 'which retailers run the deepest promos on Brand X'. |
Output
Schema · Response
{
"type": "object"
}Tags
x402shopping
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