A prospect goes through the same pipeline every time: resolve identity, gather evidence from every source in parallel, decide whether there's enough signal to trust, then synthesize.
DonorScale searches for the prospect's public LinkedIn profile and scores the match on employer, location, and name specificity. This resolved identity becomes the anchor every other source is checked against - critical for common names, where a single record can belong to several unrelated people.
Seven sources are queried at once: FEC political contributions, ProPublica nonprofit filings, corporate officer registries, insider trading records, Wikidata, an email finder, and targeted web search. Records that don't match the resolved identity are filtered out before anything moves downstream.
Before any AI writing happens, DonorScale checks whether the matched evidence clears a minimum bar - enough independent sources, enough weighted signal, and no unexplained contradictions between high-confidence sources. Common surnames with thin signal require a higher bar. If the gate isn't cleared, synthesis is refused and you get a plain-language reason instead of a guess.
Once the gate passes, the AI model receives only the filtered, matched evidence and produces a structured profile - summary, wealth indicators, capacity score, recommended ask, and affinity notes. Every claim is required to cite the specific source it came from; unsupported claims are rejected.
Prospect research touches sensitive information. Here's how we handle it.
Prospect and donor records you enrich are never used to train or fine-tune any model - yours or anyone else's. Your data is used to answer your query, then it's done.
Synthesis runs on models we host and control ourselves - not a third-party AI API that logs or retains your prospects' information.
Every organization's workspace and enrichment history is walled off - nothing is pooled, shared, or visible across accounts.
Enrich prospects without leaving your CRM, and push finished profiles straight back into it.
Don't see yours? Tell us what you use - we're adding integrations continuously.
The same categories of signal a professional prospect researcher would dig up - assembled automatically.
A 1–10 rating of giving potential, backed by the evidence behind it.
A specific suggested amount, not just a wide capacity range.
The concrete evidence - FEC totals, corporate positions, and more.
Assessed values and ownership records from public property databases.
Prior gifts to other nonprofits, surfaced from public 990 grant disclosures.
Trustee and officer roles on private and family foundations.
Reported pay and equity positions from public filings, where available.
Form 4 insider transactions tied to the prospect's resolved identity.
Shared board seats and affiliations that reveal warm-introduction paths.
Causes and organizations the prospect has shown interest in.
Every statement linked to the source record that supports it.
Which sources matched, which were empty, and why - even when the gate refuses synthesis.
DonorScale is in private beta - apply for access to enrich your first prospects.
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