AI Readiness

For Nonprofits & B Corps

A nonprofit or benefit corporation doesn't sell: it asks for trust. And trust, today, also depends on how AI systems describe your mission to those who don't yet know you: donors, funders, partners, institutions.

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Visibility & Grants Appearing correctly when networks, institutions, and funders search for organisations doing what you do
Trust & Transparency Mission, activities, and governance protected from inaccurate descriptions or hallucinations
Verifiable Entity Stable, disambiguated presence on Wikidata: an authoritative source that AI can cite with confidence

How your organisation is described by AI systems

When someone asks ChatGPT or Gemini who works on a certain issue in a given area, or what your charity does, the model doesn't open your website: it composes an answer from what it finds structured and verifiable. If your mission lives only inside PDF reports, brochures, and social posts, for the AI it's nearly invisible — or worse, filled in with guesswork.

1. GEO for impact

Ensuring your organisation appears in generative responses when people search for organisations addressing the same cause. Not to sell: to be found by those who can support, collaborate with, or join you.

2. Mission integrity and anti-hallucination

Anchoring mission, activities, and governance to stable sources (Schema.org, Wikidata) so that models don't confuse your organisation with others of similar names, nor invent activities you don't carry out.

3. Verifiable entity and transparency

Turning transparency into a machine-readable asset: activities, projects, and impact structured so they can be cited. For nonprofits, verifiability isn't marketing — it's consistency with your mandate.

What I do

AI Readiness for nonprofits and B Corps

Preparation of the informational presence of charities, foundations, and benefit corporations to be understood, cited, and accurately represented by generative engines and intelligent agents.

Areas of intervention
• Structuring mission, activities, and projects in AI-readable data
• Linking to verifiable Wikidata references and, where there is notability, a dedicated disambiguated entry
• Schema.org markup (Organization/NGO) on the official website
Who it's designed for

Nonprofits, charities, foundations, and voluntary organisations, as well as benefit corporations and B Corps that want their mission accurately represented by AI systems.

Why an impact organisation should care

For an organisation that lives on trust, the first impression is increasingly often delivered by an AI: to a donor searching for causes to support, to a programme officer evaluating partners for a grant, to a volunteer trying to understand what you do. If that description is vague or inaccurate, trust starts at a disadvantage.

This isn't marketing — it's consistency with the mandate. A transparent organisation should also be readable: mission, activities, and governance exposed in a way that anyone, person or machine, can verify.

Grants and donors: what changes in practice

Networks, foundations, and funding bodies increasingly use AI tools to map the nonprofit sector and identify active organisations on a particular cause or territory. Appearing correctly in that mapping means being considered for opportunities you would otherwise never hear about.

The point isn't to appear larger than you are, but to be found for what you genuinely do. A well-structured entity allows anyone searching for exactly your activity, in your area, to reach you.

Transparency as data, not declaration

Many organisations publish social reports and impact reports as PDFs that no machine can truly read. The transparency is there, but it remains inaccessible: for an AI system it's almost as if it didn't exist.

Structuring that data transforms it from a document into queryable information: activities, results, and impact become citable. Transparency stops being a formal act and becomes an asset working for you.

The AI Readiness journey for impact organisations

The value isn't in the final code, but in the decisions that precede it. You start from what to communicate and arrive at the syntax, never the reverse.

Phase 1: What you want to be known for

AI Exposure Audit & strategy

Before the code comes a decision. I map how AI systems describe your organisation today and define with you what those who don't know you (donors, funders, institutions) should know — and why. The narrative is chosen here, not chased afterwards.

Phase 2: The information basket

Source retrieval and verification

This is the real work. I retrieve and verify the sources that support every claim: grants received, impact reports, activities, projects, affiliations, dates. I decide with you what goes in, what stays out, what is genuinely demonstrable. A mission isn't declared — it's documented.

Phase 3: Weaving the entities

Entity linking & Wikidata

No organisation exists in isolation. I connect the entity to those already verified that it's linked to: networks, funders, project partners, territories, people. The meaning of what you do emerges from relationships, and that's what an AI system reads to place you.

Phase 4: Implementation and stewardship

JSON-LD, Schema.org, monitoring

Only at this point do I translate decisions into JSON-LD and structured data, and keep the infrastructure alive over time. The syntax is the last step, not the work: if a plugin could generate it, it would mean the upstream decisions hadn't been made.