Production Data Feeds, Built to Your Schema
Create entirely new datasets or enhance existing ones with more up-to-date data. Quant-friendly delivery with historical backfill, broad ticker coverage, and your custom data dictionary.
The challenge
A systematic fund needed a unified retail promotions dataset covering thousands of SKUs across major brands. Existing vendors offered partial coverage with stale data, and the fund's internal data dictionary required fields that no off-the-shelf product provided.
How Kadoa solves it
Custom data dictionary designed to match their internal schema
Multi-source aggregation across brand websites, outlet channels, and marketplaces
AI enrichment to classify promotion types and normalize pricing formats
Historical backfill for 12+ months of backtesting-ready data
Daily delivery to Snowflake with ticker-level mapping
Time to production
Weeks not months
vs. in-house development
Delivery reliability
99.9% SLA
Guaranteed uptime
Maintenance burden
Zero
We handle all source changes
Kadoa
Scrape dataMonitor
Source
Brand Websites
Pricing, promotions, availability
Marketplaces
Third-party retail platforms
PDFs & Reports
Supplements, presentations
Press Releases
Newswire and newsrooms
Actions
Aggregate
Merge data from all sources
Enrich with AI
LLM classification and normalization
Validate
Cross-source consistency checks
Destinations
Snowflake
Scheduled table updates
API
REST endpoint for your stack
S3
Direct file delivery
Extracted data
| Ticker | Brand | Category | Avg Discount | SKUs Tracked | Last Updated |
|---|---|---|---|---|---|
| NKE | Nike | Footwear | 28% | 1,240 | Today |
| LULU | Lululemon | Apparel | 15% | 890 | Today |
| TGT | Target | Electronics | 22% | 3,100 | Today |
| HD | Home Depot | Power Tools | 8% | 2,400 | Today |
| COST | Costco | Seasonal | 12% | 680 | Yesterday |
"We tried building this in-house for months. Kadoa had a production feed running in two weeks with better coverage than we ever achieved ourselves."
— Head of Quant Research, Systematic Fund
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