- What is the exact nature of your relationship with brainstems.ai? Are you a subsidiary, partner, or customer?
- How does brainstems.ai's blockchain and ML technology specifically apply to your food service solutions?
- To what extent are you dependent on brainstems.ai for your core technology and operations?
- Can you provide a detailed explanation of how your ML models work in the context of food service optimization?
- How do you gather and process data from various stakeholders (manufacturers, operators, brokers, distributors) while ensuring data isolation and privacy?
- What specific blockchain features are you utilizing, and how do they enhance your solution compared to traditional write-only database systems?
- Do you train your own ML models, use commercial providers, or a combination of both? What factors influence this decision?
- For each of your key ML models, what are the primary objectives and how do you measure their success in achieving these objectives?
- What measures do you have in place to protect sensitive business data from your clients?
- How do you ensure that insights generated for one client don't inadvertently reveal proprietary information from another?
- Can you walk us through your data anonymization and aggregation processes?
- How do you comply with data protection regulations across different regions where your clients operate?
- What is your pricing model, and how does it vary across different stakeholders?
- How do you quantify the ROI for your clients?
- Is there a token economy involved in your system? If so, how does it work, and what value does it provide to participants? Why would I want to participate?
- How do you measure the accuracy and effectiveness of your ML models in improving food service operations?
- What benchmarks do you use to compare your solution's performance against traditional statistical methods?
- Can you provide examples of how your system has significantly improved outcomes for specific clients?
- For each of your key ML models, can you share specific performance metrics and how they translate to real-world improvements in the food service industry?
- How scalable is your solution across different sizes of food service operations?
- What are your plans for expanding beyond your current focus areas within the food service industry?
- How many customers do you currently have? How does your burn rate align with your growth projections?
- Are there plans to develop new or enhance existing ML models to address other challenges in the food service industry?
- What are the biggest challenges you've faced in implementing your technology, and how have you addressed them?
- How do you handle potential biases in your ML models?
- What contingency plans do you have in place for potential system failures, data breaches, etc?
- Can you walk us through the process of onboarding a new client onto your platform?
- How do you integrate with existing systems that food service businesses typically use?
- How do you tailor your ML models and overall solution to meet the specific needs of each client in the food service industry?