Most nonprofits choose grant management software the same way: someone is drowning in spreadsheets and inbox threads, a demo gets booked, and a year later the team is still exporting to Excel to do the part that actually matters. The tool solved intake. It never solved selection.
That is the first thing to be clear about before comparing vendors. Grant management software covers three different jobs, and most products are strong at one of them. Intake is collecting applications and files reliably. Selection is reviewing, scoring and deciding. Administration is reporting, payments and post-award tracking. Write down which of the three is costing your team the most hours right now, because that is the job you are buying for.
Once you know the job, the feature list stops being a checklist and becomes a filter. Here is what to test, in the order it matters.
1. Form building without a developer. You should be able to publish a working public application form in an afternoon, with sections, conditional questions, file uploads and a shareable link. If building a form requires a support ticket or a services engagement, every future change will too. Ask to build one live in the demo rather than watching a pre-built example.
2. Rubric-based scoring, not free-text notes. Fair selection needs criteria fixed before review begins and applied identically to everyone. Look for named criteria with weights, per-criterion scores, and a total that is computed rather than typed. Systems that only offer a comment box push judgment back into private opinion, which is exactly where inconsistency lives.
3. AI that surfaces evidence instead of deciding. This is where products differ most in 2026. A useful implementation reads the application, drafts a score against your rubric, and shows the exact passages it relied on so a reviewer can agree or overrule in seconds. That saves the mechanical work of parsing while leaving the decision with the person accountable for it. Be skeptical of anything that produces a ranking with no citation, and of anything marketed as automated decisioning. Ask two direct questions: can I see why it scored that, and can I override it without breaking the record.
4. Automated rubrics that stay yours. Some platforms can draft a rubric from a description of your program, which is a real time saver for a first cohort. What matters is whether you can then edit every criterion, weight and scale, and whether the version used to score a given cohort is preserved. A rubric you cannot inspect is a rubric you cannot defend.
5. Applicant communication in the same place. Decision emails, waitlist notes and requests for missing documents belong next to the application, not in a separate mail merge. Check that emails send from your own domain, that they are reviewable before sending, and that the send history is attached to the applicant record.
6. Data isolation and export. Ask where files are stored, whether access is scoped per program, whether links to uploaded documents expire, and whether your data is used to train models. Then ask how you get everything out. A clean export is what makes switching possible later, which is the only real protection against a tool that stops fitting.
7. Total cost against real volume. Pricing per program, per seat and per applicant behave very differently once a second cohort opens. Model your actual numbers for the next twelve months, including the cohort you are not sure will happen. Watch for charges attached to reviewers, since a per-seat model quietly discourages inviting the external panel that would improve the decision.
A short way to run the evaluation: take twenty real applications from your last round, load them into each shortlisted tool, and score them. Then compare the results against what your team actually decided. The tool that gets closest, with reasoning you can point to, is the one that will hold up in your third cohort rather than just your first.
This is the standard Softly is built to meet. Describe the program in plain language, get a public form and a rubric you can edit, let evidence-backed AI scoring do the parsing, then review every suggestion and send the decision from your own domain. The AI surfaces, the human decides, and the record shows both.