Step 01
Map the data terrain
We read your CRM, warehouse, and current enrichment bills before writing a line of the waterfall. Which fields are missing, which providers you already pay, where the same record was purchased more than once. The gaps and the spend, charted into a map your team can inspect.
Output: the data terrain map, gaps and spend charted
Step 02
Design the waterfall
Per field, we set the provider order, verification rules, and cost ceilings: which API to try first for an email, when to fall through, what counts as resolved. The logic is code in your repo, readable in one sitting and changeable in a pull request.
Output: the waterfall spec, committed as code
Step 03
Call the providers direct
The waterfall calls data APIs straight from your infrastructure at raw rates, with no credit layer in between. Each answer is verified before it counts, failures fall through to the next provider, and every call is logged with the price it carried.
Output: fields resolved at raw api cost, logged
Step 04
Cache it in your warehouse
Every resolved record lands in your warehouse with source, timestamp, and price paid. Before any new run, the waterfall checks the cache first, so a record you bought in March costs nothing in June. Refresh windows keep stale fields from surviving too long.
Output: a cache that makes the second lookup free
Step 05
Read the quality ledger
Fill rate, verification pass rate, and cost per resolved field, reported weekly per provider. When a source degrades, the ledger shows it before your reps do, and the waterfall reorders around what still resolves cleanly for your segments.
Output: the weekly quality ledger, cost per field
Step 06
Feed every workflow
Enriched records flow onward: into the CRM your reps read, the scoring that ranks accounts, the outbound queues, the routing rules. One enrichment layer feeds them all from the warehouse, so no tool holds a private copy of the truth.
Output: one enrichment layer, every workflow fed