Private beta · onboarding nonprofit teams by invite

Know who to ask, and how much, before you pick up the phone.

DonorScale turns a name and a company into a full prospect profile - giving capacity, wealth signals, and a recommended ask - built from public records and synthesized by AI your development team can actually trust.

Request beta access See how it works

Currently invite-only · we review applications on a rolling basis

300+

verified public & proprietary data providers

< 60s

to enrich a single prospect

100%

of AI claims source-attributed

profiles synthesized on thin evidence

Prospect research shouldn't take an afternoon per name.

Development teams lose hours manually cross-referencing FEC filings, nonprofit 990s, corporate registries, and news search just to figure out whether a prospect is worth a meeting - and the answer is often a guess.

DonorScale automates the cross-referencing and hands your team a structured profile: capacity score, recommended ask, affinity notes, and the exact sources behind every claim.

FEC filings

Political giving history

Corporate registries

Officer & ownership records

Nonprofit affiliations

Board seats & 990 filings

AI synthesis

Capacity & recommended ask

From a name to a profile in three steps

No spreadsheets, no manual cross-referencing.

Add a prospect

Enter a name, employer, and location. That's all DonorScale needs to start.

We enrich automatically

Identity resolution runs first, then we fan out across 300+ verified data providers - public and proprietary - in parallel.

AI synthesizes a profile

A capacity score, ask range, and affinity notes - every claim linked back to the source that supports it.

See the full pipeline

Confidence gate

DonorScale never fabricates a profile from thin evidence. If a prospect doesn't clear a minimum bar of matched, weighted signal, synthesis is refused - you get a coverage breakdown and the exact reason instead of a plausible-sounding guess.

Claim attribution

Every statement in an AI profile is tied to the specific source that supports it - so your team can verify, not just trust.

Built so your team can trust what it reads.

Most AI research tools confidently hallucinate when the underlying data is thin - especially for common names. DonorScale's confidence gate and identity resolution (via LinkedIn) exist specifically to catch that before it reaches your team.

Identity resolved against LinkedIn before other sources are trusted
Records that don't match the resolved identity are filtered out
Common surnames require stronger, multi-source signal before synthesis runs