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In September, the AI company Anthropic published a blog post that claimed its models were approaching “superhuman capabilities for geolocating outdoor photos”. Anthropic made this claim by comparing its models’ performance to that of players of the popular online game GeoGuessr, which challenges users to geolocate Google Street View images. 

Bellingcat has been running its own benchmark tests on AI models since June 2025, right around the time when Large Language Models (LLMs) began to challenge reverse image searches for assisting with geolocation tasks. In our first round, GPT 4o-mini from OpenAI appeared to be the best tool for geolocating, a title which was taken by Google AI mode when we performed our second round of tests a few months later in August 2025. 

This time, we significantly upped the ante for the models, providing increasingly challenging photos and asking for only one set of coordinates. We tasked not only closed-weight models from Claude, Grok, Gemini, ChatGPT, and Mistral, but also open-weight models from Moonshot AI’s Kimi, commercial provider GeoSeer and the non-generative geolocation model GeoClip. In total, we fed each model 52 photos that had not been published anywhere on the internet before.

The results showed that the current generation of models have markedly improved geolocation capabilities. Anthropic’s Opus and Fable, as well as Google’s recent Gemini 4 Argon model, performed the best on geolocation tasks, narrowing down locations from images faster than a human could under specific conditions. Preliminary tests of a set of agentic skills and tools being developed internally by Bellingcat also showed promising results in further improving the geolocation performance of generic-use AI agents.

But crucially, the tests also showed why humans remain necessary for accurate and trustworthy geolocation work, as no models were foolproof. This is particularly important given the increased use of AI in military contexts. The Anthropic blog post focused specifically on geolocating photos for military and intelligence purposes such as targeting, a historically labour-intensive process requiring significant human expertise. 

While our tests showed some models performed extremely well at narrowing down results to the correct general area, none were close to being perfect at finding the exact spot a picture was taken. That accuracy is extremely important in geolocation, especially in military contexts where the cost of incorrect information can be life or death. It is also important for organisations like Bellingcat whose reputation relies on pinpoint factual accuracy.

Our Methods

To compare model results, we used two metrics built on the distance in kilometres between a prediction and the real location. GeoGuessr Score was used to assess whether models could systematically identify the general area, while the Mean Log Score (explained below) measured which models most accurately pinpointed the correct location in the final few kilometres. 

The GeoGuessr Score metric is based on the online game of the same name, whose scoring system Stanford University academics reverse-engineered in a 2024 study. This system rewards answers close to the general area an image was taken, but does not strongly distinguish between guesses close to the precise location. For example, an answer 200km from the correct location with more points than one 2,000km away. However, an answer 500m from where a picture was taken and another 5km away achieve a similar score. For real-world geolocations, that distance matters a great deal.

For more precision, we introduced a custom metric that grades models higher if they narrow down the search in the last few kilometres. This metric, the Mean Log Score, assigns a value from 0 (completely inaccurate) to 100 (perfect model) and works on a logarithmic scale instead of a linear one like GeoGuessr score.

Therefore, a 10-fold improvement in distance means the same whether it is from 5km to 500m or from 500km to 50km. You can compare both metrics here.

Below are the model results sorted by either metric, showing how many photos each model geolocated within distance bands from the true location.

Several models placed at least 10 out of the 52 photos correctly within one kilometre, and the top-performing models placed over 50 percent of the photos within 25 kilometres.

NOTE: Geoseer-fast refused to answer on four of the 52 photos, claiming “This image is not suitable for geolocation”, while Mistral-large-4 refused once with “insufficient information in image to identify location”. Kimi-k2.6 also failed to provide a set of coordinates on one photo, and Gemini 4 Argon timed out repeatedly on one photo.

Our Test

To independently assess the models, we sought to give them very specific instructions and some images that were harder to geolocate than those provided in our past tests. The prompt asked for a single set of coordinates reflecting the photographer’s location. The purpose of restricting the response in this way was to prevent hedging, such as multiple guesses or confidence scores, and make the grading more uniform across different models. Models were allowed to use their providers’ built-in tools: search, code execution and page fetch. We capped their token output at 64,000 and ran each model under its default effort setting with a maximum time of 15 minutes per photo. If a model did not return coordinates on the first prompt, we also allowed a single rescue prompt asking it to do so using its previous reasoning and without using any tools.

The images provided were unique, from around the world, not published on the internet before, scrubbed of metadata and provided without any context. They did not contain significant landmarks. We also included images in snowy, foggy and nighttime conditions.

The results show that the LLMs tested could, in many circumstances, successfully narrow down a location much faster than a human can geolocate even without any context. This is not limited to Google Street View-style images. Take, for example, this image of vegetation and a very small part of a rooftop.

For an average person, identifying the country this photo was taken in without any context could take a significant amount of time. However, multiple models quickly narrowed it down to Kenya. You can navigate their answers in the map below. Notice how several correctly inferred enough vegetation details to narrow down the exact part of the world and the park itself, but also that for many, the final response was a best guess only. It is important to note that even though some models were correct, others were significantly off.

The full model results for all 52 photos can be seen in the table below.

Of the models tested, Gemini 4 Argon scored highest on the Mean Log Score, and Claude’s models (Opus and Fable) were at the top of the table regardless of the score metric. Even Claude Sonnet, the weakest of its lineup, consistently came close to the real location while being the fastest to answer across all models.

This was followed by GPT-6 Astra and GPT-6.1 Sol, as well as other Claude and Gemini models, and then the geolocation-specific model, GeoSeer Fast, which provided good results but also timed out frequently. We tested GeoSeer’s more competent Agent model, but it refused to answer on eight photos and regularly timed out.

For secondary factors like time and cost, Claude also performed well on both metrics, and Gemini Argon, while much slower, came at a similar cost to Opus. Based on API rates, Argon and Opus models offered the best balance of accuracy and cost, especially compared to Fable and Grok. Grok was the most expensive option and had an underwhelming bottom-half performance. Meanwhile, GeoClip was freely available but only outperformed Claude Haiku.

Model Limitations

While we sought to analyse the most prominent models, there was one that we could not include as it has yet to be fully released.

In its blog post, Anthropic claimed its Mythos model beats “even the strongest human baseline on median distance error, scoring 37.0km and 47.2km across 6,000 photos (placing 23.7 percent and 23.1 percent within 1km).“ But Nuno Moniz, Associate Research Professor at the Lucy Family Institute for Data & Society at the University of Notre Dame, told Bellingcat that “the median represents a strong simplification of what would be acceptable to compare AI-based system performance with regards to humans”. 

Anthropic compared its models to competitive GeoGuessr players and their success in identifying the location of Google Street View images. The entire set of test images was online and there was no guarantee the models were processing the imagery for the first time. 

While Anthropic has been relatively open about these methods, not all developers have been as forthcoming. It is therefore difficult to assess whether the same limitations will impact all other models, or whether there will be further caveats that we are simply not aware of. 

Moniz said that “the increasing availability of benchmarking tools and datasets has made it somewhat difficult to measure reliability in test results such as these”. Specifically, due to the ”uncertainty as to whether that data was included in its training”. This is why we ran our Bellingcat tests with non-published photos sourced from Bellingcat staff and the Bellingcat Volunteer Community.

A further limitation of any models comparing performance to GeoGuessr scores is that the game uses images from Google Street View, which is limited to a select number of countries, and within these, a limited number of roads. Street View is also intentionally skewed to feature clear, high-quality photos taken during daytime that are useful for navigation, and users can pan around their view. There are also some well-known tricks that allow GeoGuessr players to bypass actual geolocation to correctly guess the location of photos: for example, Google is known to use a black car in Jordan and a white car in the UAE to capture imagery. While this wouldn’t necessarily impact model performance, it is a drawback of measuring against GeoGuessr score as the game has a set of workarounds that can bypass geolocation skill.

Anthropic’s data consisted of thousands of social media photos of various scenes. Once again, though, these photos were online and may have featured in the model’s training data. Anthropic’s blog post contained four images as an example of this dataset.

The examples Anthropic shared are relatively easy to geolocate as they feature recognisable buildings and signs. But footage that requires geolocation often comes from regions where access is limited and which do not feature on social media or Street View.

Specifically, in a military and intelligence context, footage often involves low-resolution material and intentionally obscured details recorded in remote or inaccessible areas. Context that could be found online can be uncertain or plain wrong, especially during a developing situation, such as the image below that Bellingcat investigators geolocated when verifying a US strike on a school in the city of Minab in Iran that killed at least 120 children earlier this year.

Anthropic researchers acknowledge this caveat, writing that it “does not as clearly emulate the task of precisely pinning down a location in near-real-time in a less populated battlefield setting; that is a task for future research.”

As noted, it is also possible that similar caveats need to be applied to other models when seeking to understand and interpret any geolocation-related benchmarking.

Supercharged Models

In parallel to the above models, we also tested a set of agentic skills and tools being developed internally by Bellingcat. These are researcher-informed instructions and code that can equip generic-use AI agents to augment their geolocation ability. For instance, we designed skills such as allowing it to download Environmental Systems Research Institute (ESRI) satellite imagery tiles. We also designed another skill that enabled direct access to data from Bellingcat’s OpenStreetMaps search tool, which aims to assist geolocation efforts by comparing items identified in imagery (like the position of a pylon compared to a nearby railway line) to OpenStreetMaps data. GeoSleuth is a similar project that has already been published, which we used for comparison. We picked Claude’s Opus 5.5 model to run both skills customisations because of its strong performance and reasonable pricing, and ran all the photos through the supercharged models under the same conditions – except that they had no timeout enforced as the supercharged models required more time to utilise their extra skills. 

The different timeout conditions and expanded toolset do not allow for a direct comparison of the customised models to the previous off-the-shelf models’ results, even if they both outperformed the barebones Opus model they were based on, and all other models on GeoGuessr Score, with only Argon ranking above Bellingcat’s agent in Mean Log Score.

Still, this test allows us to showcase why skill customisations can bring value. Take the following photo.

This is not an exceptionally hard geolocation, and 11 of 17 models answered within 5km of the actual capture location in Venice, Italy. GPT-Sol answered with the correct monastery, but only Bellingcat’s agent and GeoSleuth answered with the exact camera position coordinates. This was possible because both model customisations can download and compare satellite imagery tiles, which allows them to validate not just which monastery the photo was taken at but also the correct courtyard position and orientation from visual cues. You can click the buttons in the list below to audit each model’s final answer.

While in this instance the supercharged models got the exact spot, there were other instances where they did not, as the scores in the results table above show. This again emphasises that even with extra skill sets added, these models were still far from infallible.

Tests Conclusions

Despite increased model improvement, our analysis shows that important caveats remain and it is important to keep a human in the loop when seeking to guarantee a geolocation is precise and correct.

Our tests show that the coordinates models provide as answers are rarely an exact match, even with supercharged models. Invariably, even the best models, with the best tools, can confidently claim a wrong position. Anthropic says its Mythos model was able to pinpoint less than a quarter of its social media photos dataset within a 1km radius. But narrowing down a location is rarely the trickiest part of geolocation work. A large part of important geolocation work is in the final stretch: finding specific coordinates from a general area. This level of specificity is needed because a general area is not a match. Matching features and perspective are necessary for an accurate geolocation, and in many cases that is done on very low-quality images or even by cross-referencing multiple sources. 

This level of precision is essential for investigations where it matters what building a person could be found at, what lines of sight individuals might have had, or what impact an air strike might have led to. 

Since LLMs can still confidently get things wrong, as our analysis shows, all results still have to be verified independently. Strong human geolocation skills therefore remain crucial, as the more one understands about geolocations, the better one can verify or take to completion an automated result. 

Though it can speed up geolocation work significantly, especially in narrowing down an area of interest, AI models remain a useful assistant rather than a tool we can blindly delegate geolocations to.

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Bellingcat is a non-profit and the ability to carry out our work is dependent on the kind support of individual donors. If you would like to support our work, you can do so here. You can also subscribe to our Newsletter and follow us on Bluesky here, Instagram here, Reddit here and YouTube here.

The post Can LLMs Supercharge Geolocations? Our Tests Show Big Improvements But Humans Remain Key appeared first on bellingcat.

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Satellite imagery obtained by Bellingcat lays bare the extent of tree-felling at a Washington DC golf course that President Trump is reportedly seeking to transform into a championship-level course, despite moves to protect the site by conservationists. 

The extent and circumstances surrounding the rapid changes prompted a judge to call an emergency hearing earlier this week, banning any further changes until later this month.

Reports on Wednesday detailed that 430 trees were cut down at the East Potomac Golf Links  which is owned by the Federal government and managed by the National Park Service (NPS). This is despite a judge reportedly stating earlier this year that no more than 10 trees at the course could be cut down without first notifying the court. 

NPR reported that officials notified the court just before 5pm on Friday Oct. 9 of plans to remove up to 529 trees in phases starting  the next day on Saturday Oct. 10. That prompted US District Court Judge Ana Reyes to call an emergency hearing on Oct. 12. By that time, 430 trees had reportedly been cut down.

Reyes is reported to have said she was “furious” with the Justice Department for the lack of time afforded the court and plaintiffs before the tree removal was carried out.

She was also reported to have said that the government’s actions had cost it “all of its credibility with me in this case.”

Images taken at the course earlier this week show trunks and branches strewn out across fairways.

Just total devastation wrought on East Potomac Park this past weekend. 450+ trees. For an admin that professes to support “beautification” of DC, that’s sure not what we or anybody else who has seen what they did on Saturday would describe it as. East Potomac is (was?) a thriving, beautiful gem in the heart of Washington DC. A beautiful, public, accessible place that hundreds of thousands use annually. The assault on this place should infuriate every American. The impacts of what took place Saturday will take years to fix. We cannot sit idly by while this admin treats our public lands like they are for the taking.

— Save East Po (@SaveEastPo) October 13, 2026

But satellite imagery affords a broader look at exactly what sections of the course have been impacted.

The Image below from Planet Labs taken on October 8 shows the golf course before the tree-felling was carried out.

Planet Labs imagery captured on Oct. 13 shows large barren sections towards the south and west of the course, where trees were removed.

A before and after slider allows for easy comparison of the Oct. 8 and Oct.13 images.

Reuters reported that an organisation called The ​DC Preservation League said in a February ​2026 lawsuit that President Donald Trump was flouting ⁠environmental laws to “irreparably destroy a priceless piece of American history” so he could “convert a public recreation space into an exclusive, high-end golf course.”

Kevin Griess, an NPS official, reportedly stated at Monday’s hearing that tree removal was part of a 2019 plan that predates Trump’s aims for the course and sought to restore links-style vegetation.

Bellingcat contacted NPS as well as the Department of Justice but did not receive a response before publication.

The changes to the golf course are not the only ones President Trump has sought in Washington DC and the surrounding area since the beginning of his second term. In October last year  demolition began on the East Wing of the White House with the aim of building a new ballroom claiming it was “the safest building ever built.”  

Trump has also proposed a 250-foot-tall “Triumphal Arch” to be built just outside the Arlington National Cemetery in Virginia.

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Correction: This article was updated on October 15 to reflect that a judge told the US government not to cut down more than 10 trees before providing notice. The article previously said that no more than 10 trees could be felled without the court’s approval. The headline has also been changed to reflect this.

Bellingcat is a non-profit and the ability to carry out our work is dependent on the kind support of individual donors. If you would like to support our work, you can do so here. You can also subscribe to our Newsletter and follow us on Bluesky here, Instagram here, Reddit here and YouTube here.

The post Satellite Images Show Extent of Trump Admin’s Tree-Felling at DC Golf Course appeared first on bellingcat.

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US non-profits whose founding leaders or organisations have been associated with India’s controversial Rashtriya Swayamsevak Sangh (RSS) – or initiatives the Hindu nationalist group has endorsed – received millions of dollars over recent years through grants that allow donors to remain anonymous, a Bellingcat analysis has found. 

The right-wing and Hindu nationalist volunteer organisation is widely considered the ideological parent of India’s ruling Bharatiya Janata Party (BJP). Prime Minister Narendra Modi, a former RSS worker, is one of its most famous supporters. 

But the RSS’ vision of India as a Hindu nation is controversial both at home and abroad: it was previously banned in India three times, and a bipartisan US commission on religious freedom recently called for the US government to sanction the group “for their responsibility and tolerance of severe violations of religious freedom”. Almost a hundred US politicians, both Democratic and Republican, recently pledged not to take funding from RSS-linked entities. 

While there have been efforts to map out RSS-related entities, it can be challenging to establish concrete links between the organisations because the RSS remains an unregistered entity in India and has no formal membership, maintaining that it is a “body of individuals” with shared ideological views.

“The RSS functions without independent oversight of its resources, finances, influence, and activities,” said Dr Angana P Chatterji, an anthropologist and scholar of South Asia studies at the University of California, Berkeley. “This is not proportional to the vast power, reach, and influence it possesses. Its leadership and cadre are protected from accountability and scrutiny,” she added.

The RSS had not responded to multiple requests for comment, as of publication.

Bellingcat identified two US-registered non-profits that appeared to have past and present ties to the RSS, or organisations associated with them, based on the non-profits’ or RSS websites and publications, as well as statements by the non-profits’ founders and spokespeople. The non-profits were the Ekal Vidyalaya Foundation of USA Inc (Ekal USA) and Sewa International Inc (referred to as Sewa USA). 

We looked at all Form 990s – tax returns that US non-profit organisations are required to file annually – listing Ekal USA and Sewa USA as grant recipients, as well as the Form 990s of the charities themselves, from 2020 to 2024. As of September, the latest available Form 990s for Sewa USA were for the fiscal year ending December 2024, while Ekal USA also filed a form for the fiscal year ending December 2025, which was excluded for consistency, although the same pattern was observed during this period.

This analysis showed that the charities received at least US$24.4 million from 34 organisations that manage donor-advised funds (DAFs), or 20.7 percent of the total $117.8 million they reported receiving in contributions and grants during this period. 

The actual amount from DAFs may be higher, as we excluded filings that covered partial years outside of this period – for example, June 2019 to June 2020. The filings from the two charities we identified were based on the calendar year.

The Form 990s also revealed that the vast majority of the money spent by these two charities on grants to other organisations or individuals was in India or the South Asia region rather than in the US: Ekal USA donated $33.7 million in foreign grants – all of which went towards the Ekal Vidyalaya project in India according to its filings –  and none in domestic grants in the five years from 2020 to 2024, while Sewa USA reported $29.7 million in foreign grants and $622,613 in domestic grants during the same period. 

DAFs are common vehicles for people in the US who want to make donations to charitable causes while receiving tax relief or benefits. Donors do not control the money once they donate it but can advise on which charities the money eventually ends up at.

These funds are legally maintained and operated by non-profit sponsoring organisations. As public charities, DAF sponsor organisations detail the amounts and recipients of grants that they make on behalf of their donors in their Form 990s. However, they do not disclose who their donors are, and donors can choose to remain anonymous. (Note: Bellingcat US receives grants from donor-advised funds, and some grants do not disclose the name of the grant recommender. However, many of our donors are listed on our funding page.)

There are many more organisations reportedly linked to the RSS that are not included in this analysis, but we focused on these two because of the large amounts of grants they received through DAFs, with the original sources of the funds untraceable from public information, as well as evidence pointing to both historical and current connections between them and the RSS or organisations associated with the RSS. 

It is worth noting that some of the evidence we found pointing towards a connection between the two charities and the RSS or organisations associated with the RSS was from before or after the period we analysed (2020 to 2024). There is no evidence that DAF grants funded the RSS, only that they went to organisations that appear to have connections with the RSS. Experts told us that DAF grants undergo less regulatory scrutiny than other charitable donations and that DAF sponsor organisations have discretion over whether to restrict or permit donations to certain recipients.

Ekal USA

Houston, Texas-based Ekal USA is a non-profit that describes its mission in its Form 990s as “to provide free elementary education and free primary healthcare to children in remote and rural India”. The USA branch of Ekal Vidyalaya, according to its website, was founded in 2001 as part of efforts to “reach out to the global diaspora” in order to achieve a goal to “expand the reach of Ekal to 100,000 villages”. 

RSS chief Mohan Bhagwat has appeared at multiple Ekal Vidyalaya events in India promoting the initiative over the years, as far back as 2014 and most recently in May.

Ekal USA was started in the late 1990s as a programme of the Vishwa Hindu Parishad of America (VHPA), or World Hindu Council of America, VHPA spokesperson Shyam Tiwari told Bellingcat in an email response. He added that it was set up as an independent organisation in early 2000. According to VHPA’s website, Ekal USA was established in 2002.

VHPA is based in the US, while the similarly named Vishwa Hindu Parishad (VHP) is in India. VHP lists VHPA in the “Hindus Abroad” section of its website. A 2017 article from VHPA’s magazine states that late founder Mahesh Mehta was trained by the RSS since he was 10 years old, and that the VHPA was born after Mehta was advised by the former supreme leader of the RSS to “build VHP in USA”. The national spokesperson of VHP in India, Vinod Bansal, said that it is an independent organisation that “sometimes shares ideology” with VHPA.

RSS’ website states that VHP was launched to organise Hindus and “reconvert all those who had been knowingly or unknowingly proselytised to alien faiths and are now desirous of coming back to the Hindu fold”. Bansal referred to VHP as an “independent part of the RSS” and an “ideological brother”. 

Ekal USA Chairman-Advisor Subhash Gupta wrote in an Ekal USA publication this year that in the ‘80s and ‘90s, he was “president of the VHP’s Houston chapter” and “often received many high-profile guests from RSS and VHP”.

Gupta did not respond to requests for comment as of publication.

From 2020 to 2024, Ekal USA reported receiving more than $48.4 million in contributions and grants. Of this amount, at least $8.3 million was from DAF grants, based on Form 990s that listed Ekal USA as a grantee. 

According to Ekal USA’s Form 990s, it transferred $33.7 million in foreign grants during this period and none in domestic grants for organisations or individuals. The forms also show that all the foreign grant money went towards building Ekal Vidyalaya schools in India. The rest of its expenses involved operational costs such as staff salaries.

The Ekal Vidyalaya movement has been criticised over reports that allege the schools are used to indoctrinate millions of India’s poorest and most vulnerable children and women with Hindu nationalist ideology, including that Muslims and Christians are “enemies of Hinduism”. Ekal Vidyalaya in India did not respond to multiple requests for comment.

Ekal USA did not respond to Bellingcat’s questions about these reports or any relationship it may have with the RSS. However, on its FAQ page, the group states that it does not teach religion in its schools and “does not discriminate based on caste, religious affiliation or linguistic affiliations”.

Sewa USA

Sewa USA, also based in Houston, describes itself as a “Hindu faith-based, humanitarian, nonprofit service organisation” and is listed under the heading of “Sewa Movement” on the website of India-based charity group Sewa International. 

Sewa USA Vice President of Public Affairs Rakhi Israni told Bellingcat over email that the organisation “does not operate as a branch or affiliate of the RSS” or participate in political activity. 

But multiple pages on the organisation’s website appear to indicate a connection between the non-profit and the RSS. In 2022, Sewa USA reposted an article on its website referring to Sewa International as “the US-based charitable non-profit organisation with links to the Rashtriya Swayamsevak Sangh (RSS)”. In a press release about the death of its founding member Ved Prakash Nanda in 2023, it noted that Nanda was “immersed in the ideals of the [RSS]”. And two different blog posts from 2021 and 2024 on Sewa USA’s website had volunteers citing RSS-led activities as the starting point for their involvement.

In addition, Sewa USA has also posted multiple times on social media and its website about an India-based organisation – Sewa Bharati – that appears to have the support of the RSS. 

Sewa USA referred to Sewa Bharati as a “sister organisation” in an X post in 2014, and its regional chapter in Purvanchal as a “partner organisation” in a Facebook post in 2022.

On its website, Sewa USA said in a post that volunteers from its “partner organisations in India” had participated in rescue operations related to the 2023 train accident in Odisha, and listed several initiatives by Sewa Bharati in India. It also launched a fundraiser for families affected by floods in Maharashtra in September 2025, which mentioned that over 100 Sewa Bharati volunteers were already on the ground clearing debris and assisting communities. 

“Foreign grants awarded to partner agencies in India, including service-oriented NGOs like Sewa Bharati, are strictly project-based, fully audited, and evaluated based on measurable humanitarian impact in education, health, and disaster relief,” Israni told Bellingcat.

RSS’ website links directly to the main website of Sewa Bharati through a tab on its main navigation bar labelled “Sewa”, and Sewa Bharati is mentioned in RSS’ 2016 annual report under the heading “Seva Vibhag”, a Sanskrit term for “service department”. 

A blog post on Sewa Bharati’s website from December 2024 about an event the group organised described Indian Defence Minister Rajnath Singh as highlighting the role of RSS leaders in establishing Sewa Bharati in his address and noting that “RSS not only builds the nation but also the individual”. RSS Joint General Secretary Alok Kumar also spoke at the Sewa Bharati event, according to the blog post. 

In response to Bellingcat’s queries, Israni said that Sewa USA had “no role in Sewa Bharati’s governance or operations”. Sewa Bharati did not respond to multiple requests for comment as of publication.

Sewa USA received almost $69.4 million in contributions and grants from 2020 to 2024, according to its Form 990s. Filings listing Sewa USA as a grantee show that at least $16 million was from DAFs. In its expenditure statements, Sewa USA reported giving $29.7 million in foreign grants to groups in South Asia during this period and about $622,613 in grants domestically in the US, while the rest of its expenditures included operational costs such as salaries and travel. It is not specified in the forms how much funding, if any, Sewa USA transferred to Sewa Bharati during this period, and Sewa USA did not respond to questions about any amounts transferred as of publication. 

‘Cause-Neutral’ Funding

Experts told Bellingcat that there was less regulatory scrutiny of grants distributed through DAFs than some other types of charitable donations.

“For donor-advised funds, there’s actually less oversight by the IRS because donor-advised funds are considered public charities rather than private foundations,” said Samuel Brunson, a law professor at Loyola University Chicago.

“If a private foundation were giving money to organisations in India, that would be a little bit trickier. But if it’s a public charity … there’s not really a ton of oversight on that as long as the money that the US tax-exempt organisation is giving meets its charitable purpose.”

Lloyd Hitoshi Mayer, a law professor at the University of Notre Dame who specialises in non-profits, said that it was “irrebuttably presumed” that funds granted to US organisations with the non-profit 501(c)(3) tax status were being used for good charitable purposes.

“The existing federal tax laws related to donor-advised funds put some restrictions on what grants donor-advised fund sponsor organisations, which actually control the funds, can make. But the big exception is that Congress said, ‘If you make a grant to a Section 501(c)(3) charity, you’re good.’ You don’t have to do any additional oversight.”

Some DAF sponsor organisations have previously faced criticism for granting money to charities that have promoted homophobic, anti-immigrant or anti-Muslim views. Several were also criticised for pausing funding to the Southern Poverty Law Centre earlier this year, although this case demonstrated how DAFs can prevent funds from going to some groups recommended by donors, if the DAFs deem them unsuitable. 

Brunson told Bellingcat that DAF sponsor organisations “absolutely have the right and ability” to halt processing donations to certain organisations. “Whether we want them to do that or not is a separate question,” he said. 

Bellingcat reached out to the top three DAF sponsor organisations that donated to the two non-profits we identified – Fidelity Investments Charitable Gift Fund (Fidelity Charitable), American Online Giving Foundation (AOGF) and Charles Schwab-affiliated Donor Advised Charitable Giving (DAFgiving360; known before 2024 as Schwab Charitable) – to ask about their internal policies on assessing charities receiving donations through their funds. In total, the three DAFs accounted for almost $20 million, or 81.7 percent, of the DAF grants to Ekal USA and Sewa International for full reporting years between 2020 and 2024. 

Fidelity Charitable, AOGF and DAFgiving360 all told Bellingcat that they had due diligence processes in place when evaluating non-profits. 

Fidelity Charitable’s website states that it is a “cause-neutral” charity, but it ensures grants are used solely for charitable purposes. AOGF said non-profits must comply with eligibility requirements regarding hate speech, discrimination and the use of funds. 

Meanwhile, DAFgiving360 said that a grant recipient may be subject to monitoring or placed under temporary suspension “due to circumstances that DAFgiving360 deems appropriate”, such as if there is a public interest consideration or if the recipient does not further DAFgiving360’s mission.

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Bellingcat is a non-profit and the ability to carry out our work is dependent on the kind support of individual donors. If you would like to support our work, you can do so here. You can also subscribe to our Newsletter and follow us on Bluesky here, Instagram here, Reddit here and YouTube here.

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