A High-Leverage Bet: $10k to Remove the Biggest Bottleneck in Wheat Flour Fortification

By Tony Senanayake @ 2025-11-18T13:48 (+10)

TL;DR

• Fortify Health is seeking $10,000 USD in marginal funding to deploy a solution that has the ability to eliminate the most significant bottleneck for wheat flour fortification, namely: ensuring the right level of micronutrients are being added in close to real time. We would also happily accept pro-bono or in-kind support from individuals or organisations that may have the skill set to deliver this project. The full project budget is $60,000 USD.
• The problem: Anaemia is one of India’s (and frankly the globe's) most severe public health challenges, affecting 67% of young children and 52% of pregnant women. Across the 16 states where Fortify Health operates, an estimated ~459 million people suffer from anaemia, driving substantial health and economic losses (1.18% of GDP annually).
• Fortify Health is on a mission to reduce iron-deficiency anaemia (IDA) in India. We do this by making it hassle-free to fortify wheat flour. Fortify Health partners with wheat flour mills to fortify flour with iron, folic acid, and Vitamin B12 to address anaemia. Last month, we reached more than 11 million people at an annualised cost of $0.22 per person and estimate we averted 2,928 years of life with anaemia. We estimate that our intervention is 13.6x as cost-effective as cash transfers (GiveWell CEA).
• The bottleneck: There is no way to accurately know if the right amount of micronutrients are being added to fortified wheat flour either cheaply or in real time. Current systems are either slow and expensive or are not quantitatively accurate. Lab-based tests cost $20 USD per sample, require in-person visits, and generally return results too late to fix any issues. This means that real time quality control measures are infeasible.
• The proposed innovation: Fortify Health is developing an AI-enabled, end-to-end tool that identifies and troubleshoots challenges with the quality of wheat flour fortification. We have already completed a proof of concept with great results and we are looking to productionalize this and share it as a free, public good, globally.
• Projected impact on quality of fortified wheat flour - this tool would allow for real time identification and troubleshooting, thereby ensuring that the correct level of micronutrients are added in the fortification process.
• Projected impact on costs and cost effectiveness (see our BOTEC here):
  • Cost of testing falls from $20 to $0.10 per test.
  • Expected net savings: $864,000 by 2031, resulting in a 14.4x return on investment - just within Fortify Health.
  • Cost per beneficiary decreases by 4.4%, leading to an increase in cost-effectiveness of 1.2 multiple of cash transfers (from 20.6 to 21.8) by 2031.

Anaemia is a massive public health issue

Fortify Health is on a mission to reduce iron-deficiency anaemia (IDA) in India. We do this by making it hassle-free to fortify wheat flour. Last month (October 2025) we reached more than 11 million beneficiaries and estimated that we averted 2,928 years of life with anaemia as a result. Over the past year we estimate that we have averted 25,943 years of life with anaemia (see BOTEC, tab 4)

IDA is a major public health and economic issue in India: 67.1% of children between 6-59 months of age and 52.2% of pregnant women in India are anaemic (NFHS-5), and both of these figures have increased over the past 5 years (NFHS-4). In the 16 states where Fortify Health currently operates, we estimate that ~459 million people suffer from anaemia.^[1]

Anaemia has a substantial impact on an individual’s quality of life, and particularly affects women and children. Anaemia has been shown to cause impaired physical growth and cognitive development. Women with severe anaemia have double the risk of dying during or shortly after childbirth and women with anaemia are more likely to give birth to infants with low birth weights, who have increased risk of disease and death (M. Young, 2018). 

Fortification is a highly cost-effective intervention

Iron-deficiency anaemia undermines India’s human capital. It impairs cognitive development in children, increases maternal risks, reduces productivity, and perpetuates cycles of poor health and poverty. India’s national policy think tank, NITI Aayog, estimates IDA alone costs the country 1.18% of GDP annually, or $25 billion USD.^[2] Wheat flour fortification is a proven response:

• Scale: Wheat is consumed by over 500 million Indians, particularly in wheat-heavy states such as Uttar Pradesh, Madhya Pradesh, Rajasthan, and Bihar.
• Evidence: Global and Indian studies show wheat flour fortification can reduce anaemia prevalence by up to 40%.
• Cost-effectiveness: At ₹0.10 ($0.11) per kg, fortification has benefit-cost ratios of 18:1 to 27:1.
• Global precedent: Over 90 countries have adopted mandatory wheat flour fortification, including South Africa, Chile, and Indonesia, with measurable reductions in anaemia and neural tube defects.

India has already led the way on salt iodisation and rice fortification, and wheat flour fortification is quickly gaining traction. The Government of India and several states, including Himachal Pradesh and Ladakh, have begun fortifying through social safety net programs, while Fortify Health works with ~220 industrial millers nationwide—about 10% of the sector (see our realtime dashboard here). We also support governments in Gujarat, Punjab, Uttar Pradesh, Rajasthan, Maharashtra, and Madhya Pradesh. With growing state interest, wheat flour fortification is poised to scale, but its success depends on assured quality.

After iterating our fortification programme over the past eight years, Fortify Health is now working to implement this solution at scale. We’ve grown from reaching 410,000 people per month in 2022 to over 11 million in October 2025.^[3] At just $0.22 per person per year,^[4] our programme is highly cost-effective. GiveWell’s cost-effectiveness analysis (CEA) found that our model is 19.2x as effective as cash transfers (weighted 10-year average).^[5]

A key bottleneck: quality assurance 

Fortify Health aims to reach over 40 million people by 2030,^[6] and develop the systems and processes necessary to support government and millers to independently adopt wheat flour fortification. A key process bottleneck currently limiting this scaling is the existing set of quality assurance and quality control (QA/QC) processes that ensure mills fortify wheat flour with the correct concentration of iron to address IDA.

Current QA/QC systems are cumbersome, costly, and reactive:

• Expensive: I-Check tests cost ₹1,800–₹2,000 ($20 - $22 USD) per sample, making frequent monitoring unviable.
• Slow: Lab-based results take days or weeks, delaying corrective action.
• Fragmented: Limited regulatory manpower means inspections are rare and uneven.
• Reactive: Failures are often identified only after fortified flour has already been distributed.

Fortify Health has demonstrated what rigorous QA/QC can look like in practice:

• 30+ QA/QC professionals monitoring 200+ mills across 14 states.
• Layered methods: premix verification, spot tests (~₹10 [$0.11]/test), in-house i-Check, and accredited labs.
• Systematic training: mill staff trained in dosing, feeder calibration, and monitoring.
• Digital systems: QA/QC data logged in real time via cloud-based tools.
• Innovation: validated i-Check for Indian wheat flour (with IIT Delhi), ongoing Ferrozine assay work, and an AI-based iron spot test pilot.

These methods work but are too resource-intensive to scale across thousands of mills nationwide. Without a technological leap, regulators will continue to struggle to assure quality at the scale India requires, and problems will continue to often go undetected until after under-fortified flour has already reached consumers. 

Anticipated impact of an AI-enabled system: Lower costs, improved quality

The solution is an AI-enabled, end-to-end QA/QC platform that equips regulators and millers with tools to identify risks early, diagnose causes, and implement corrective actions. It has three integrated components:

1. Automated AI Spot Test Tool (Detection)
   • Use multimodal computer vision pipelines and deep learning architecture to analyse flour samples for added iron content. Employs MobileNextV2 convolutional neural networks trained on both annotated field and synthetic datasets to distinguish iron concentration patterns invisible to the naked eye. Images of the iron spot tests in a petri dish are automatically segmented, followed by a two-stage inference pipeline. and a Bayesian calibration layer to harmonize predictions across different flour types, iron salt variants and heterogeneous lighting conditions
   • Provide near-instant real-time inference on smartphones or edge devices, low-cost (<₹10 ([$0.11] /test), with accuracy benchmarked against reference lab assays.
   • A minimum viable product (MVP) has already been developed with 600 iron spot test images, increasing the speed and scale of testing to <2 minutes per sample and >1000 tests per month.
2. Predictive Risk Model (Prediction)
   • Build a unified data pipeline by integrating historical QA/QC data, mill operational metadata, equipment details (e.g. sensor logs), and compliance history. Uses an ensemble machine learning stack to detect temporal patterns in mill operations.
   • Predict likelihood of lapses and anomaly detection (e.g. gradient boosting, random forests and temporal risk scoring with time-series forecasting), enabling quality control officers and regulators to adopt risk-based inspections (prioritising mills with higher non-compliance probabilities). A risk stratification algorithm with explainable AI can be employed so that regulators can understand which factors (e..g. equipment age, mill downtime, past non-compliance) drive elevated risks.
   • Continuously improve through online learning pipelines, refining predictions as new test and inspection data are ingested. Cross-validation ensures generalization across geographies and mill scales, while a complete audit trail and bias detection module ensure regulatory integrity.
3. Automated Troubleshooting & Action Tool (Prevention)
   • Integrate test results and risk data into a decision-support knowledge graph capturing semantic links between mill processes, QA protocols, and FSSAI standards.
   • Generate corrective and preventive actions (CAPA) using a rule-based reasoning engine combined with generative AI copilots (fine-tuned LLMs trained on operational manuals and case histories) for context-sensitive troubleshooting.
   • Provide step-by-step guidance for millers, tailored to specific mill equipment and process conditions, to resolve issues before distribution.
   • Enable simulation of corrective actions (e.g., parameter adjustments) before physical implementation, reducing downtime and product wastage.
   • Offers millers and food inspectors a mobile-first, multilingual interface with Whatsapp integration for field usability or conversational troubleshooting.
      

Together, these tools transform QA/QC from fragmented and reactive into predictive, actionable, and preventive — ensuring that fortified wheat flour consistently meets standards at scale.

In 2025, we have taken the first step towards developing this system by piloting an AI-driven iron spot test that can determine the iron content in a sample of fortified flour far more accurately and quickly than existing tools. We anticipate the full end-to-end QA/QC platform can be developed in 12 months, and rolled out across our partner mills serving > 11 million people soon after. Based on a BOTEC calculation, we anticipate that this AI-enabled QA/QC platform will enable cheap, large scale fortification by:

• Generating substantial return on investment :
  • Cost of testing declines from $20 USD per test using current tools to $0.10 USD per test using the AI iron spot test.
  • We estimate net cost savings of $864,000 USD by 2031, reducing our cost per beneficiary by 4.4%. The intervention will pay for itself within one year and generate 14.4x return on investment.
• Improving quality of fortified wheat flour:
  • We assume that a cheaper, more proactive AI-enabled QA/QC platform will enable mills to produce a higher proportion of wheat flour with the correct concentration of micronutrients to effectively tackle anaemia. In our last CEA conducted by GiveWell, a 10% discount to account for the fact that not all flour is currently effectively fortified. In our BOTEC analysis, we estimate that we could reduce this discount to 5% if the AI-enabled QA/QC platform was working effectively. Incorporating updated cost per beneficiary into the GiveWell CEA, we estimate that this tool could lead to an increase in cost-effectiveness of 1.2 multiple of cash transfers (from 20.6 to 21.8) by 2031.

Impact throughout the fortification sector

In addition to the impact we expect this upgraded QA/QC platform would have on Fortify Health’s programming, we anticipate that such a system would also generate impact for other stakeholders throughout the fortification sector, including:

• For Regulators:
  • Replace costly, slow lab testing with instant AI-enabled testing.
  • Shift to risk-based inspections, increasing coverage and accountability.
  • Build on the Food Safety and Standards Authority of India’s openness to AI.
• For Large-Scale Flour Producers:
  • Provide real-time diagnostics that reduce disruptions and compliance failures.
  • Provide automated corrective and preventive action guidance that helps millers solve problems before inspections.
  • Strengthen trust with regulators and consumers.
• For Doers at Scale
  • Offer an easy and simple bundle of QA/QC services to ensure fortified wheat flour consistently meets regulatory standards.
  • Avert tens of millions of cases of anaemia annually, at <$0.25 per person per year.
• For Global Fortification Efforts
  • Establish the first regulator-ready AI-enabled QA/QC system in fortification.
  • Adaptable to wheat, rice, maize, salt, bouillon cubes and supplements.
  • Create a replicable model for all LMICs.

Major Uncertainties

We have a number of major uncertainties about this project. These include:

• Technology Uncertainties:
  • We are uncertain of the quality of the final tool, and it is possible the quality will be lower than we anticipate, leading to less return on investment than initially estimated.
  • The tool will require validation, including by regulatory authorities in India (and globally if rolled out). While we believe that validation and regulatory approval can be secured, there is a small to moderate chance this would not happen, limiting the uptake of the tool by Fortify Health and other actors
• Impact Uncertainties:
  • There are minor uncertainties around the number of tests we would conduct with the new tool vs existing technology in the coming years, and in particular the rate at which we would scale down the use of existing tools and scale up the use of the new tool.
  • We are uncertain about the potential of this tool to significantly reduce the number of visits currently required by our team to conduct QA/QC at partner mills (which would be a key driver of the cost savings from this tool). It is possible that there would be anticipated and unanticipated reasons why our team would need to continue these visits, including for instance troubleshooting the new tool in the medium to long term, or if validation is delayed or not achieved.
• Program Uncertainties:
  • Our estimates of return on investment for this tool are based on a number of assumptions related to ongoing scaling of our mill partnerships; if these partnerships do not scale as planned, this would lower the return on investment.
  • We believe this tool is likely to be accepted by mill partners, in particular following tailored training, though it is possible that acceptance from partners would be lower than expected, limiting the correct use of the tool.

Funding Gap and Marginal Funding

Our funding gap to fully develop and implement this AI-enabled QA/QC platform is $60,000 USD. This includes the following costs:

• Data collection and annotation
• AI model development and engineering
• Tech infrastructure and maintenance
• Field testing and scale-up
• Regulatory workshops and validation
• Project management
• Overheads

Within this $60,000 USD budget, marginal funding of $10,000 USD will help us launch the next phase of this project–building a predictive risk model–while we continue fundraising for the remaining cost.

Contributions over $60,000 USD would be used to support ongoing scaling of our open market intervention (and we would also be open to recommending alternative funding opportunities within the Global Health space that may have greater marginal return on investment). 

We would also happily accept pro-bono or in-kind support from individuals or organisations that may have the skill set to deliver this project.

Donation link: https://give.cornerstone.cc/fortifyhealth

Thanks to Elizabeth Robinson (Chief of Staff) and Dr Prasad Bogam (Director of Monitoring, Evaluation and Research) for their support in preparing this post. 

1. ^{^}

   Calculated based on anaemia prevalence data from NFHS-5.

2. ^{^}

   Nutrition International, Cost of Inaction Tool, accessed 16 November 2025.

3. ^{^}

   Based on figures from August 2022 and August 2025. See our external dashboard to view other time periods or see how these numbers are calculated.

4. ^{^}

   Based on a cost-effectiveness analysis of our open market intervention. Costs include a proportion of the direct costs of running the open market intervention as well as a proportion of indirect costs of running the organisation. The methodology behind beneficiary calculations can be read here.

5. ^{^}

   Note that the cost per beneficiary figures in the model have been updated from the original GiveWell model. The figures in orange are based on actual figures from the past three years and the figures in yellow have been updated based on forecasts. Please note that these figures have not been verified by GiveWell.

6. ^{^}

   Based on a detailed exercise projecting future mill onboarding, production volumes, and beneficiary reach. We used field data and operational insight, and applied conservative assumptions.