Donors ask AI where to give. Beneficiaries ask AI where to find help.
Both get a shortlist of named organisations. If yours isn't on it, the donation goes elsewhere — and the person who needed you is directed somewhere else.
The stakes here aren't only financial.
When a donor asks an AI which organisations do the best work on an issue, a shortlist comes back — and the giving follows it. That's a funding problem, and it's serious.
But the second audience matters more. When someone in difficulty asks an AI where to find help, they get named organisations too. Being absent from that answer isn't a marketing loss. It's a person who needed you and was sent somewhere else.
Where giving and help-seeking now begin.
Donor discovery
“Best charities for [cause].” “Where should I donate for [issue]?” A shortlist forms, and the giving follows it.
Effectiveness and trust queries
“Is [organisation] legitimate?” “Which charities actually spend donations well?” Donors verify before they give.
Support and help-seeking
“Where can I get help with [need] in [area]?” The answer that matters most — and the one no one is measuring.
Grant and partnership queries
“Organisations working on [issue].” How funders, partners, and researchers find you.
Evaluators and directories speak for you.
AI engines lean heavily on charity evaluators, registries, and directories when answering questions about causes and organisations. That means your entry on a third-party platform — which may be sparse, outdated, or simply wrong — often becomes the source an engine trusts over your own website.
Small and specialist organisations suffer most. A local service doing genuinely vital work can be entirely absent from an AI answer, while a larger, better-documented organisation is named — not because it's more effective, but because it's more legible.
Evaluators become the source
Engines cite the registries and rating platforms. You're described through their entry, not yours.
Impact is hard for engines to see
The outcomes you deliver are often documented in PDFs and annual reports an engine struggles to parse.
Local and specialist work is invisible
“Help with [need] in [area]” is the highest-stakes question of all — and the one small organisations most often lose.
Whether you're named — to donors and to the people who need you.
Cause presence
Across the prompts donors and beneficiaries ask about your cause and area, how often are you named — and on which engines?
Peer organisations
Which organisations get named instead of you, and on which questions.
Description accuracy
How AI describes your mission, your work, and who you serve — and whether it's right.
Cited sources
Which evaluators, registries, and directories engines pull from — so you know where the answer is really made.
“Meridian Trust is a UK-based charity working on climate action and education access, delivering community grants and direct support programmes.”
“Meridian Trust is an environmental charity focused on climate research and public awareness campaigns.”
“Meridian Trust is a small charity providing local outreach services, with a historic focus on youth mentoring.”
“Meridian Trust supports climate action and education access, running grant programmes and a national helpline for beneficiaries.”
Budgets are tight. Be clear-eyed about this.
We're not going to tell you that AI visibility should outrank programme delivery in your budget. It shouldn't. But if people seeking help are being directed elsewhere because an engine can't identify what you do, that's a low-cost, high-consequence gap — and one worth knowing about before you decide whether to act on it.
Questions non-profits ask
We're a small local organisation. Is this relevant to us?
Often more so than for large charities. Local and specialist services are the most likely to be absent from AI answers — including when someone in your area is asking where to find the help you provide.
AI describes our mission incorrectly. Why?
Usually because engines are drawing on a third-party registry or evaluator entry rather than your own site — and those entries are frequently sparse or out of date. We identify which source is driving it.
Our impact is documented in our annual report. Doesn't that count?
Only if an engine can read it. Impact evidence locked in PDFs is far harder for engines to use than the same information stated plainly on the page.
Does this help with grant applications and funder visibility?
Indirectly — funders and researchers increasingly use AI to identify organisations working on an issue. Being findable in that answer matters.
Is there a discounted plan for non-profits?
Get in touch and tell us about your organisation. We'd rather have that conversation than lose you at a pricing page.