REQUEST FOR PROPOSAL (RFP)

For Multilingual Data Collection Services for AgriAI CollectClosing Date for Submission Oct 15, 2025, at 23:59 PM 

 LEHS is a charitable organization in India whose purpose is to offer basic health and education for the poor. LEHS in furtherance of charitable objectives through its flagship programs Wadhwani AI which aims to build equitable and sustainable systems by making quality primary healthcare available and accessible to the underserved population and to bring the benefits of modern AI technology to underserved populations by building and deploying AI solutions for social impact across domains such as healthcare, agriculture, governance and education in India. LEHS aims to promote the integration of technologies, particularly in emerging domains like artificial intelligence and innovations into the Indian mainstream primary healthcare, education, and agriculture systems through a partnership with the State and National Government, apex institutions, international agencies, and private sector partners e.g. innovators, social enterprises and other ecosystem contributors in line with its stated objectives for the betterment of society particularly focusing on projects of national and social significance. Wadhwani AI, a unit of LEHS, focuses on developing, deploying, and evaluating artificial intelligence solutions to address critical social challenges in India, particularly in domains such as healthcare, agriculture, and education. As part of its ongoing initiatives in the agriculture sector, Wadhwani AI is developing an imageto-text extraction model aimed at identifying and extracting key textual information from agricultural input product images—such as fertilizer bags, pesticide containers, and seed packets. These AI models are being trained to accurately recognize and extract product names, compositions, usage instructions, and other relevant information in English, enabling better access to product-related information for farmers and agri-extension workers. In alignment with its quality assurance framework, Wadhwani AI will conduct rigorous evaluations to assess the accuracy, consistency, and applicability of the extracted textual data to ensure the model’s effectiveness and reliability in real-world agricultural use cases. 

 India’s agriculture sector is driven by over 93 million smallholder farming households, with nearly 90% owning less than 2 hectares of land (NDTV Profit). While schemes like PM-KISAN offer income support (Wikipedia), smallholders still face barriers to accessing premium markets. Certifications such as India Organic and GLOBALG.A.P.are essential economic tools—organic produce can fetch 20–50% price premiums, and the organic market nearly doubled from ?169 crore in 2017–18 to ?330 crore in 2022–23 (Mint). However, manual and inconsistent data practices delay audits and exclude many farmers from timely certification. To tackle this problem among many others in the agriculture data ecosystem, India is rolling out AgriStack, a national digital agriculture infrastructure, with over 3.7 million Farmer IDs created and Digital Crop Surveys deployed in 436 districts (Indian Express, AgriStack). The current ecosystem is fragmented, analog, and heavily reliant on manual processes. Farm-level data— critical for audits and certification—is often captured through farmer diaries maintained by field workers or input digitally by farmers themselves. These diaries include important questions such as:

• What is the name of the fertilizer or pesticide you used?
• What is the brand name or product label?
• What input did you apply and when?  

However, many smallholder farmers are unable to answer these questions due to low literacy, language barriers, or lack of familiarity with product labeling. Recognizing this gap, Wadhwani AI has proposed a new data collection approach: allowing farmers to simply upload images of agricultural input products (e.g., fertilizers, pesticides, seed packets) in response to these questions. To support this, Wadhwani AI is developing an image-to-text extraction model that can automatically extract relevant information from these product images. The model is trained to detect and extract key product details in English, such as:

• Product name
• Brand name
• Composition details
• Usage or dosage instructions  
• Product label color codes

This innovation would enable farmers to respond more accurately to diary questions without needing to read or transcribe complex product information. The extracted data is then automatically populated into the farmer diary, improving data completeness, consistency, and accuracy. By minimizing manual entry, reducing errors, and standardizing input records, this AI-powered solution strengthens traceability, supports certification workflows, and helps smallholder farmers better access premium markets—while reinforcing India’s credibility in global agri-trade. This RFP invites qualified agencies to undertake multilingual data collection services aligned with this objective.  Proposal Submission Guidelines  For clarifications and queries, please direct all queries to rfp.lehs@wadhwaniai.org by Oct 08, 2025. Full proposal submission instructions:

• Both Technical and Financial Proposals must be submitted electronically to rfp.lehs@wadhwaniai.orgseparately.  
• Financial Proposal must be password protected.
• All documents should be in PDF format.
• Subject line of the email: 

1. “Technical Proposal for Multilingual Data Collection Services for AgriAI Collect – [Agency /
2. Vendor Name]”
3. “Financial Proposal for Multilingual Data Collection Services for AgriAI Collect – [Agency / Vendor Name]” along with password.

• Submission Deadline: [October 15, 2025 – 23:59 Hours]
• Any proposals received after the deadline for submission of proposals prescribed in the timeline of this document are liable to be rejected.
• Bidders who submit complete technical with password protected financial proposals and all mandatory enclosures (Section IX) will be considered only

 For detailed information, please check the complete version of the RFP attached below.

Download Attachment:

RFP_Image_Collection_AGCT_090925.pdf