If three regional networks agree to share one analytical platform, the obvious efficiency is a shared taxonomy. One set of categories, one definition per category, comparable numbers across all three regions.
That is not what happened, and the reason is worth understanding by anyone building measurement infrastructure across African markets.
What the platform does
Our partners at Impact Intelligence set out the case in Stanford Social Innovation Review in April 2026, in a piece called Supercharging Network Intelligence. The system they describe, Social Investment in Action, runs across three venture philanthropy networks: Latimpacto in Latin America, the African Venture Philanthropy Alliance, and AVPN in Asia. Between them the networks represent more than a thousand foundations, impact investors, corporations and international NGOs.
The problem it addresses is mundane and expensive. A membership network holds a great deal of information about its members, scattered across websites, reports, news coverage and informal updates. Answering a simple question, such as which members are currently deploying catalytic capital in a given country, means weeks of manual research producing an answer that is out of date by the time it is circulated.
Over four years, the platform surfaced more than 54,000 documented member activities from public sources and mapped more than US$4 trillion in social investments. The African instance, Social Investment in Action - Africa, tracks capital mobilised towards impact involving AVPA members.
What each network asked for
Here is where the design decision shows up.
AVPA wanted greater visibility into catalytic capital and financial instruments in Africa, reflecting an active regional argument about which financing mechanisms actually work.
Latimpacto wanted to distinguish rural from urban community development, and wanted more precise categories of marginalised community: economic vulnerability, identity-based exclusion, and racial marginalisation as separate things rather than one bucket.
AVPN wanted to track faith-based giving, which carries cultural and strategic weight across much of Asia and almost none in the categories a European or North American framework would supply.
Three networks, three sets of questions, none of them reducible to the others. A shared taxonomy would have had to pick one, or invent a superset broad enough to be useless.
The principle
The article states it directly: separate a common analytical backbone from locally defined categories and priorities.
The networks aligned on baseline categories such as social causes, beneficiary groups and types of support, financial and non-financial, so that activity remains comparable at that level. Above the baseline, each network defined its own categories according to what it needed to know.
This is not a compromise between standardisation and local relevance. It is a recognition that they operate at different layers. The backbone answers what kind of thing happened. The local categories answer whether it mattered here.
Anyone who has tried to run one reporting framework across Kenya, Tanzania and Nigeria has met the problem the other way round: a taxonomy designed centrally, applied uniformly, and quietly ignored by everyone who found their actual work did not fit any of the boxes. The data comes back complete and means nothing.
What this method cannot see
The evidence base is public sources. News articles, organisational updates, announcements, websites.
That has a structural consequence, and the article’s authors name it themselves when they ask where the blind spots are in the information a network already collects: smaller organisations, rural initiatives, and groups working in less visible issue areas are underrepresented, because they lack the capacity to produce regular reports or respond to surveys.
Scanning public sources at scale does not fix that. It industrialises it. An organisation that publishes nothing is invisible to a system reading what has been published, and it is invisible faster and more comprehensively than before.
This is the honest limitation of every automated mapping exercise, including the ones we build, and it is the reason the third capability in the article matters more than the first two.
Voice, as the answer to the blind spot
Alongside text analytics and research agents, Impact Intelligence describes voice-based interview agents: conversational, multilingual, on-demand interviews conducted over the web or a mobile phone. In 2025 they used the approach to assess a government-backed sustainability award, interviewing winning organisations across the world, many with little public information available.
That last clause is the whole argument. The organisations with the least published about them are the ones a public-source scan cannot reach, and they are reachable by asking. Participants respond in their own words, in their own language, at their own pace, without scheduling a call across five time zones or completing a written form.
Which is the same problem we are working on from the other end. Kipimo’s Swahili speech recognition work exists because a business owner who has never filled in a written application should still be assessable on what they said.
The question is not whether AI can map an ecosystem. Fifty-four thousand documented activities and US$4 trillion in mapped capital settle that. It is whether the map records the organisations that write about themselves, or the ones doing the work, and what it takes to close the distance between those two sets.
