Gratefully is trying to solve the problem every development team actually has, which is not a shortage of donor data but a shortage of time to act on it. It is a donor-intelligence layer that sits on top of the CRM and files you already use, reads your entire donor history, and each morning hands your team a ranked list of who needs attention and why. It does not ask you to migrate anything.
I have tested the major AI tools in the nonprofit space for AIToolsBakery, from wealth-screening incumbents like DonorSearch to predictive platforms like Dataro. Gratefully is the tool I keep pointing small and mid-size teams toward, because it targets the daily work of stewardship rather than one-off prospect scores. This review covers what it does, where it genuinely helps, where it is still unproven, the real cost picture, and who should skip it.
Gratefully in one paragraph: An AI donor-intelligence system that unifies your CRM, email, and spreadsheets into one knowledge graph, then surfaces a ranked daily action list across seven signal types. Best for development teams on Salesforce NPSP or Bloomerang who want intelligence without a migration. Pricing is demo-gated.
The Gratefully honest scorecard
Gratefully earns its place as our top donor-intelligence pick on execution of a specific idea: turn scattered donor data into a short, explained, daily to-do list. It is not a CRM, it is not a wealth-screening database, and it is not a magic revenue button. Scored against what it claims to be, it is strong, with the main caveats being a short public track record and pricing you have to ask for.
| Dimension | Rating | Notes |
|---|---|---|
| Data unification | Excellent | Knowledge graph across Salesforce NPSP, Bloomerang, email, sheets, and documents with no migration. |
| Daily usefulness | Excellent | Ranked action list with a reason attached to every item. This is the core value. |
| Ease of setup | Very good | Connect sources and go. Setup is measured in minutes, not weeks. |
| Data privacy | Very good | PII is tokenized before any prompt leaves your tenant, then reversed locally. |
| Track record | Fair | Newer product. Most proof points are founding-partner pilots, not years of public case studies. |
| Pricing transparency | Fair | No public price. You book a demo to get a number. |
What Gratefully actually does well
Three things separate it from the donor-management and prospect-research tools it sits next to.
It unifies data you already have, without a migration. Gratefully builds a donor knowledge graph from your existing sources: Salesforce Nonprofit Cloud and NPSP, Bloomerang, Mailchimp, plus CSV, PDF, DOCX, XLSX, and PPTX files. Nothing moves. The CRM stays your system of record, and Gratefully becomes the intelligence layer reading across all of it. For teams that have wanted donor intelligence but dreaded a data project, this is the whole pitch.
Grace turns the graph into a daily action list. The assistant, called Grace, works your portfolio overnight and ranks it each morning across seven signal categories: relationship risk, moves-management progress, commitment health, giving trajectory, stewardship moments, hidden revenue, and deadlines. Every item comes with the reason it surfaced. You can also ask questions in plain language and get a donor briefing in seconds rather than digging through the CRM before a call.
It protects institutional memory. Two features matter more than they sound. Gratefully can generate a handover dossier when a staff member leaves, so donor context does not walk out the door with them, and it drafts stewardship notes and outreach letters grounded in a donor’s real history and written in your organization’s voice. For a sector with heavy turnover, keeping donor knowledge searchable through a resignation is a real structural advantage.
Where Gratefully falls short
An honest review has to name the gaps, and there are a few worth weighing before a demo.
The public track record is short. Gratefully is a newer entrant. The headline numbers it publishes, roughly eight hours per week reclaimed per gift officer, three times faster donor research before a call, and full donor knowledge surviving a resignation, come from founding-partner pilots. They are plausible and they match how the product works, but they are not yet backed by years of independent, public case studies the way an incumbent’s numbers are. Treat them as a strong signal, not settled fact, and validate against your own portfolio during a trial.
Pricing is not published. There is no pricing page with tiers and numbers. You book a demo, and the quote depends on your data sources and team size. That is normal for this category, but it does make quick budget comparison harder, and small teams should ask for the entry number early so a demo does not become a surprise.
It depends on the quality of your existing data. Because Gratefully reads what you already have, its output is only as good as your donor history. An organization with a clean, multi-year giving record in Bloomerang or Salesforce will get far more value than one whose donor data is a single spreadsheet with no gift history. The tool cannot infer a relationship trajectory that was never recorded.
Integrations are focused, not universal. The strongest connections are Salesforce and Bloomerang, plus flat files. If your system of record is a less common CRM, confirm the integration path in the demo rather than assuming parity with the flagship connectors.
Gratefully pricing in 2026: what to expect
Gratefully does not list public pricing. The number is quoted after a demo and scales with your data sources and the number of gift officers or seats. Based on how comparable donor-intelligence layers are priced, expect a subscription positioned below a full enterprise wealth-screening contract and above a basic CRM add-on, billed annually.
The more useful way to frame cost is against the time it targets. If the tool genuinely returns even a few hours a week to one gift officer who carries a real major-gift portfolio, the subscription tends to pencil out quickly, because that time goes back into donor conversations that move money. If you do not have anyone doing portfolio-based cultivation, the math is much weaker, which is the real qualifier for whether to buy at all.
Gratefully vs the alternatives
Gratefully competes at the intersection of three categories, and the right comparison depends on the job you are hiring it for.
- Versus prospect research (DonorSearch): DonorSearch tells you a prospect’s capacity and wealth. Gratefully tells you which existing donors to act on today and why. Many teams run both, one for discovery, one for daily stewardship.
- Versus predictive scoring (Dataro): Dataro predicts likelihood to give and powers campaign segmentation. Gratefully is built around the individual gift officer’s daily portfolio rather than mass segmentation.
- Versus the CRM’s own AI (Bloomerang, Virtuous): If your CRM already has native prospect intelligence, Gratefully’s edge is cross-source unification and the explained daily list, not a single-CRM view.
- Versus donor-outreach AI (Gravyty): Gravyty focuses on volume outreach and drafting. Gratefully leads with intelligence and prioritization first, drafting second.
For a full side-by-side of every option, see our Gratefully alternatives guide and the roundup of the best AI donor-intelligence tools.
Who should buy Gratefully in 2026
Buy it if you run a real portfolio motion: a development director or one to several gift officers cultivating individual donors, your history lives in Salesforce NPSP or Bloomerang, and your actual bottleneck is time and prioritization rather than a lack of data. Teams that lose donor context every time someone leaves will feel the handover value immediately.
Skip it if you are an all-volunteer or very small organization with no major-gift cultivation, if you want a system that replaces your CRM rather than augments it, or if your donor data has no meaningful giving history for the tool to read. In those cases, fix the CRM and data foundation first, then revisit.
A gift officer’s day with Gratefully
The clearest way to judge the tool is to picture the workflow. The officer opens Gratefully in the morning to a ranked list rather than a blank CRM search. Near the top: a mid-level donor who has given every March for four years and is now three weeks overdue, flagged as relationship risk with the pattern shown. Below that, a stewardship moment, a first-time donor who just crossed into repeat-giving and should hear from a human. The officer asks Grace for a two-line brief on each, gets history and last contact instantly, and lets Gratefully draft a first-pass note in the org’s voice to edit and send. What used to be an hour of CRM archaeology before the first call becomes a few minutes of review. That compression, repeated daily, is the entire value proposition.
Privacy and data handling
For a tool that reads sensitive donor records, the data model matters. Gratefully tokenizes personally identifiable information, names, gift amounts, contact details, and family or health notes, before any prompt reaches a language model, then reverses the tokens locally so answers still read naturally. It describes bank-grade observability over how data is accessed. As with any vendor touching donor PII, put it through your own security review and, where relevant, confirm how it aligns with your data-privacy obligations. We cover the broader issue in our guide to donor data privacy in AI fundraising.
What we still cannot fully assess
Two things need time and access we do not yet have. First, long-run accuracy: how often the ranked list is right over a full giving year across many organizations, which only large-scale, independent data can settle. Second, real deployed pricing across org sizes, since the quote is private. We will update this review as verified figures and multi-year outcomes become available. Nothing here should be read as a guarantee of results for your specific organization.
Where to learn more
Gratefully fits into a wider stack. If you are building your nonprofit toolkit, start with our pillar on the best AI tools for nonprofits, then narrow by job: AI donor research tools, AI tools for donor retention, and AI fundraising tools.
Tools mentioned in this guide
Written by
FazFaz is the founder of AIToolsBakery. Every tool on this site is personally tested with real-world writing tasks before a single word gets published. No sponsored rankings, no recycled press releases.
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