DonorSearch AI

What is DonorSearch AI?

DonorSearch AI is a fundraising prospect research platform that scores donors by their likelihood to give and their capacity to give at scale. When you load a database, the predictive models read each record against giving history, wealth markers, and philanthropic signals, then return a score that ranks the prospect. Gift officers use those scores to sort a long list into an order they can work, so the warmest and highest-capacity names rise to the top rather than sitting buried in the file.

DonorSearch makes the platform. The company built its product around predictive scoring paired with wealth and philanthropic screening, so the models rank prospects on their own rather than waiting for a researcher to profile each name by hand. That design suits teams whose record count would swamp a research desk that leans on manual work for every prospect.

The audience is nonprofit and fundraising teams that hold more prospects than they can research. DonorSearch targets development offices, universities, hospitals, and advocacy groups that carry large donor databases, where the best prospects are hard to spot and where research time is the bottleneck. These teams want to find the supporters ready to give, gauge how much each can give, and point gift officers at the names most likely to return a yes.

Key features

DonorSearch AI centers on a set of capabilities that work together to rank and profile every prospect in the database:

• ▸Predictive giving models: the models weigh giving history, wealth, and philanthropic signals to predict how likely a prospect is to give and how much, which turns a raw list into a ranked one.
• ▸Prospect scoring: each record gets a score, so gift officers sort the database by fit and work the strongest names first.
• ▸Wealth screening: the platform pulls capacity markers such as property, business ties, and holdings into one profile, so the score comes with the wealth behind it.
• ▸Philanthropic and giving history: past gifts to other causes and prior giving surface in the profile, which shows a prospect's habit of giving, not capacity alone.
• ▸Batch database screening: the platform scores a full file at once, so a team can screen thousands of records and surface hidden supporters in one pass.
• ▸CRM and platform integrations: scores and profiles push into common donor CRMs and fundraising platforms, so gift officers see the data where they work.

The predictive models matter most for focus. Because the platform scores every record on its own, a team sees a ranked list rather than a flat file, so gift officers spend their hours on the prospects most likely to give. The wealth screening adds the capacity read, so a high score comes with a sense of the ask a prospect can support. The batch screening drives reach: a single pass scores the whole database and surfaces supporters a manual review would miss, and the integrations carry those scores into the CRM so the research reaches the people who make the calls.

How well does it work?

DonorSearch AI performs well on the research gap that costs fundraising teams gifts. For databases where gift officers cannot profile every record, the models score the whole file and rank prospects, which surfaces supporters that would sit unworked. The ranking is the standout: the platform sorts a long list into an order a team can act on, and the wealth screening pairs each score with a capacity read, so gift officers know both who to call and how large an ask to prepare. The batch screening means a team can score a full database in one pass rather than one name at a time.

The limits track the data and the model. Scores point a team at likely supporters, but a large ask still needs a human read on the prospect, the relationship, and the timing, so the platform guides outreach rather than deciding it. Data coverage varies by donor, so some profiles carry thinner signals than others, and a score built on sparse data deserves a closer look before a gift officer acts on it. The output is a starting point for a conversation, not a substitute for the cultivation work that turns a score into a gift.

A practical way to lift results is to treat scores as a first pass, then confirm the top-ranked prospects with a quick manual review before a gift officer reaches out. The scoring narrows the field, and the human read confirms the ask.

DonorSearch AI pricing

DonorSearch AI uses custom pricing. There are no public rates. The platform scopes a quote to your organization based on the number of records you screen, the products you turn on, such as prospect scoring, wealth screening, and integrations, and the size of your team. To get a number, you talk to the DonorSearch sales team.

Here is how the main cost drivers fit together so you can prepare for that conversation:

The math favors teams with a large database that goes under-researched, since each gift the scoring helps surface offsets the research time a manual review would take on the same file. Because rates are custom, model your record count, the products you need, and the seats your team requires before the sales call, then compare the quote against the giving you leave on the table when prospects go unworked.

Who should use DonorSearch AI?

DonorSearch AI fits nonprofit and fundraising teams that hold more prospects than they can research by hand. It suits these groups in particular:

• ▸Development offices with large donor databases, who want the whole file scored so the best prospects rise to the top.
• ▸Universities and hospitals running major-gift programs, who want a capacity read on each prospect before a gift officer prepares an ask.
• ▸Nonprofits with lapsed or low-signal records, who want to re-screen names they already hold to find supporters they overlooked.
• ▸Annual-giving and campaign teams, who want to rank a segment before an appeal so outreach reaches the names most likely to give.

DonorSearch AI is a weaker match for small nonprofits with a short donor list, for teams that already research every prospect by hand with time to spare, or for groups that want a free or fixed-rate tool with no sales call. In those cases the scale of the platform and the custom pricing bring more than a light workload needs.

Alternatives and how it compares

DonorSearch AI competes with a field of prospect research and wealth screening tools. The right comparison depends on your stack and how you want scores to reach gift officers.

• ▸iWave: a prospect research and wealth screening platform with scoring, a fit for teams that want profiles and screening across a broad data set.
• ▸WealthEngine: a wealth and capacity screening tool, a fit for teams that lead with wealth data and want capacity ratings across a large file.
• ▸Windfall: a data platform focused on net worth signals, a fit for teams that want a modern data feed pushed into their CRM.

DonorSearch's edge is predictive giving models paired with wealth and philanthropic screening in one profile, so a score comes with both the likelihood to give and the capacity behind it. If your problem is that the best prospects hide in a database no one has time to research, DonorSearch is a strong candidate. If your priority is wealth data alone or a light feed into your CRM, a screening specialist or a data platform may fit with less overlap, so weigh the job you want done alongside the feature set.

Limitations and getting started

Be honest about the trade-offs before you commit. The custom-only pricing means no public rates and a sales call before you can budget. Scores guide outreach but a large ask still needs a human read on the prospect and the relationship, so the platform informs the work rather than replacing it. Data coverage varies by donor, so some profiles carry thinner signals than others, and strong outcomes assume gift officers use the scores as a starting point and confirm the top names before they reach out.

Getting started follows a clear path:

1. 1Connect your donor CRM and load the database so the platform can screen your records and return scores where your team works.
2. 2Run a batch screen on the full file, then review the top-ranked prospects to confirm the scores match what you know.
3. 3Set the segments and thresholds that matter to your program, such as major-gift capacity or annual-giving fit, so scores map to your outreach.
4. 4Route scored prospects to gift officers, then track outcomes and refine which signals and thresholds you lean on as gifts come in.

A staged rollout keeps risk low: start with one segment or campaign, confirm the scores hold up against known donors, then widen screening as the results earn trust. Because the platform scores records on its own, the early weeks are about learning which signals map to your donors so gift officers act on the ranking with confidence.