Gratefully Review (2026): Donor Intelligence, Tested

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.

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 caveat being a short public track record.

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.

The four parts of Gratefully, and what each one is for

Gratefully is easier to evaluate once you stop treating it as one AI feature and look at the four things it actually ships. They build on each other, and the order matters.

Know is the foundation. It pulls your CRM, inbox, notes and documents into a single donor brain, so a conversation from eighteen months ago sits next to the gift history and the board member’s aside about a capital campaign. Every answer it later gives cites the source it came from, which is the difference between a tool you can defend in a meeting and one you cannot.

Ask is the query layer. Plain English questions, answered from your own data rather than from the open internet. This is the part that replaces the twenty minutes of digging before a call.

Action Center is where the product earns its keep. It works your portfolio overnight and hands you a ranked list at the start of the day, with the reason attached to each name, plus churn risk flags and stewardship reminders. Grace, the agent behind it, runs the ranking at 6:30 in the morning, so the work is finished before anyone opens a laptop.

Grow points the same machinery at revenue you have already earned but not collected: lapsed donors worth another approach, major-gift potential sitting in the mid-level file, and planned-giving signals that nobody has time to look for.

Underneath all four is the institutional memory piece, which is the least glamorous and possibly the most valuable. When somebody leaves, Gratefully generates a handover dossier from the relationship history rather than from whatever they remembered to write down in their last week. Anyone who has inherited a portfolio cold knows what that is worth.

Gratefully’s own figures from founding-partner pilots are eight hours a week reclaimed per gift officer, three times faster donor research before a call, and full retention of donor knowledge through a resignation. Those are the vendor’s numbers, not ours, and we have not audited them. We report them because they tell you what the product is optimising for, which is time and continuity rather than a bigger list.

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. Gratefully connects to Salesforce Nonprofit Cloud and NPSP, Bloomerang, Little Green Light, HubSpot, Mailchimp and Google Drive, plus direct file upload for CSV, PDF, DOCX, PPTX, XLSX and TXT. That covers most of the small and mid-market, and no CRM migration is required because the layer reads your system of record rather than replacing it. If your system of record sits outside that list, confirm the path in the trial rather than assuming parity with the flagship connectors.

Gratefully pricing in 2026: what to expect

Gratefully publishes its pricing, which is worth crediting in a category where most vendors make you sit through a demo to learn a number. There are two plans and they carry identical features. Annual is $400 a month, billed as $4,800 a year. Monthly is $500 a month, cancellable at any time. Choosing annual saves $1,200 over twelve months. Both include up to five team members.

Every plan covers unlimited document and CRM ingestion, natural language chat with cited answers, donor briefings, handover dossiers, daily intelligence and automated PII redaction, plus priority onboarding on the annual plan. There is a four-week free trial with no credit card, a seven-day grace period, and no setup or onboarding fee. If you leave, your knowledge graph exports in one click.

The honest way to read that number is per seat. Five seats at $4,800 a year works out at $80 per person per month, which sits well below a wealth-screening contract and above a basic CRM add-on. It is not an entry-level purchase for an all-volunteer organisation, and it is not the enterprise number the category has trained you to expect either.

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

The specifics are unusually clear for this category, and worth checking against any competitor you shortlist. Gratefully states that your data never trains public AI models, that each customer sits in an isolated tenant that is never shared, that every answer cites its source, that PII can be redacted in one click, and that there is a full audit trail. Your knowledge graph is exportable at any time, and on cancellation you can pull your data out with one click.

Two of those matter more than they look. Source citation is what lets a development director check an answer instead of trusting it, which is the difference between a tool a board will accept and one it will not. And one-click export matters because a knowledge graph built from years of your own notes is exactly the kind of asset a vendor could hold hostage at renewal.

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

• Gratefully
• DonorSearch
• Dataro
• Bloomerang
• Virtuous

See also our AI moves management guide and new CRM or better intelligence.