Skip to content

Data & AI Strategy


We are here to transform the traditional pen-and-paper agriculture industry to a smart and tech-enabled one. We are building a well-connected ecosystem for buyers, sellers and financiers in the agrifood trade space by bringing traceability, visibility and financing. We are turning a high risk industry to a high value one that encourages companies to be ESG-centric whilst ensuring high profitability.

AI as one of the important pillars investors will look at for series B
Series B - S2G ventures
Return on investment
Vertical farming is not making money → Controlled Environment Agriculture for lower costs and better yield

Why is having a data and AI strategy important?
Screenshot 2023-06-26 at 4.30.08 PM.png
Screenshot 2023-07-03 at 5.34.23 PM.png

Data Strategy

Enable Visibility and Transparency in the Agrifood Trade
Implement traceability solutions to track the origin, movement, and status of products across the supply chain.
Employ blockchain technology to ensure data integrity and establish trust among stakeholders.
Develop interactive dashboards and reports to present supply chain data in real-time.
Provide Credible and Trustworthy Data
Integrate the DiMuto platform with verified sources of data to ensure authenticity.
CSV Import to reduce manual data entry
ERP Integration to seamlessly integrate company data directly into our platform
Employ data quality management practices, including validation, standardization, deduplication, and cleansing.
Data cleaning of fake companies, and calculating internal metrics for real companies
Data standardisation and cleaning for effective data analysis
Utilize AI to protect ourselves against data fraud
Employ AI to look out for fake/ suspicious trades (financial services - we want to loan to companies that are really trading)
Employ AI to reduce data entry if integration of DiMuto platform into origin data source cannot be done (financial services - upload P&L statement)
Make Data More Accessible for Non-Tech Colleagues
Develop user-friendly interfaces and data visualization tools that make data insights easily understandable.
Use visual aids like graphs, charts, and maps to represent data, making it easier to interpret.
Create intuitive dashboards with drag-and-drop features for customizing views and reports.
Improve the readability and understandability of data variables in the database and on the platform
Database walkthrough in the tech team
Adopt consistent naming conventions for data variables that are descriptive and easy to understand.
Include metadata, such as descriptions and units of measurement, alongside data variables. e.g. Trade Contract browse page contains prices, but not currency
Enable data from database to be easily retrievable by non-developers
Data can be easily retrieved through our platform and database querying need not be done to retrieve the relevant information e.g. currency conversion for trade contracts for financing
Be More Data-Driven in Decision Making and Reporting
Encourage the use of data analytics in decision-making by training employees in data-driven methodologies.
Weekly huddle can be more data driven to include progress and gaps that can be tackled
Standardize reporting formats and include data insights to reinforce the importance of data in decision-making.
Ensure that data reported is understandable by the layman and tells a story, including DiMuto’s value to the customer
Make reported data in dashboards valuable to different stakeholders
Minimize usage of absolute figures, and put numbers into perspectives by using percentage
Conduct a Data Inventory to Optimize Data Utilization
Catalog all available data assets, including sources, formats, and usage policies.
Look into regulations about data privacy and sharing
Screenshot 2023-06-27 at 9.54.01 AM.png
Identify data gaps and potential areas where data can be leveraged for decision-making and innovation.
Regularly review and update the data inventory to ensure it reflects the evolving data landscape.

AI Strategy

Enable Efficiency and Reduce Manpower Dependency in the Agrifood Industry
Employ AI to automate and streamline operations like looking through trade documents and product photos.
Understanding trade documents in different languages, such as English and Spanish; managing multiple data formats, such as JPG and PDF.
Use predictive analytics for demand forecasting and optimization of resources.
Farm harvest prediction to see if demand can be met on the marketplace
Automate Routine Tasks and Enhance Platform Experience
Develop AI algorithms to automate the creation of training materials, including content curation and updates.
Employ AI to create training materials and videos e.g. Synthesia
Implement AI-driven personalization features in the DiMuto platform to provide customized insights and recommendations to users based on their usage patterns and preferences.
Personalised onboarding for new users of the DiMuto platform and mobile app
Train Team in AI Technologies and Applications
Introduce regular sharing sessions for the team to get acquainted with AI tools and technologies.
Tech team sharing sessions about ChatGPT and how we use it to help us with coding
Utilize AI-powered chatbots, like ChatGPT, for tasks productivity.
Tech team purchasing of one GPT plus account which produces more concise and accurate content. → more accounts can be purchased to enhance utilization across the whole team
Monitoring, Evaluating, and Scaling AI Solutions
Evaluate whether an AI solution is necessary as not all problem statements require an AI solution - it might just be a design fix
Define the problem - scope, factors involved, desired outcome
Assess the complexity of the problem - AI is good for problems that involve pattern recognition, decision making, prediction, manual tasks at scale
Analyse existing solutions - are these solutions effective and efficient? What are the gaps?
Examine data availability - is there sufficient high-quality data available to train and validate an AI model?
Weigh cost and complexity - can the problem be solved with a simple design fix?
Build a proof of concept - use real-world data to make an informed decision
Make a decision
Set up mechanisms to continuously monitor the performance of AI algorithms and models.
Regularly evaluate the impact of AI on business operations and user experience, and make data-driven decisions to refine and improve AI systems.
Develop a scalable AI infrastructure that can adapt and expand as the business grows.
Ethics and Compliance in AI Implementations
Develop policies and guidelines to ensure ethical AI implementations, avoiding biases and ensuring fair treatment.
Ensure compliance with data privacy and security regulations, and establish a transparent approach in how AI systems use and process data.

Want to print your doc?
This is not the way.
Try clicking the ⋯ next to your doc name or using a keyboard shortcut (
) instead.