FAQ

As a limited partner, what level of involvement and decision-making authority can I expect to have in the investments made with Tryvesting?

At Tryvesting, we prioritize customer centricity in our partnership, meaning that the right customer is always right. If you are approved by our existing partners to join as a new limited partner (who's effectively a customer of general partners since they charge limited partners for their services), we trust that you will involve yourself in decision-making to the extent you maximize the value of your involvement for yourself and the partnership. However, it's important to remember that with power comes responsibility. In order for our general partners to take charge of failures and our limited partners to take charge of success, there needs to be a clear separation in the level of involvement in decision-making.

We usually advise our limited partners to apply customer centricity when working with our investees, helping them adjust their business models to better cater to the customers our limited partners may represent or, even better, introduce to the startups we have invested in. This approach allows us to maintain a healthy balance between the responsibilities of general and limited partners while still capitalizing on the unique insights and connections each partner brings to the table.

How does Tryvesting differentiate itself from other VC firms operating in the AI space? What unique advantages do you offer to your limited partners?

At Tryvesting, we believe that a valuable technology is the one that is able to eventually generate profits. Typically, AI startups are driven by tech-driven engineers who don't fully understand and even don't wish to understand how the world of business works. Therefore, we focus on letting our limited partners provide business experience to the tech teams in our investees. Our unique approach involves leveraging the expertise of our T-shaped general partners who have experience both in tech and in business, acting as translators between the tech talk and business talk.

This approach not only bridges the communication gap between the technical and business sides but also creates an environment where our limited partners can actively contribute to the success of our portfolio companies. By doing so, we ensure that the startups we invest in have a strong foundation in both technology and business, increasing their chances of success.

Furthermore, our emphasis on partnership and collaboration creates a strong network of support for our limited partners, who can not only benefit from the returns on their investments but also gain valuable insights into the AI industry. This helps them to better understand the opportunities and challenges in the sector, ultimately enabling them to make more informed decisions regarding their own businesses, careers, and support for their family members as they enter the industry as first movers.

Can you provide examples of AI startups that Tryvesting has successfully invested in, and what kind of returns have been generated for the partners?

As a visionary and early adopter who likes to try everything new, you might enjoy the fact that AI as a mass-market business is a rather new industry and Tryvesting is a newly established venture capital firm focused on capturing the value of future growth. Therefore, as Tryvesting, we have not yet had the opportunity to invest in AI startups and generate returns for our partners. However, Tryvesting is founded by a team of experienced entrepreneurs and investors who have a strong track record of success in the industry. Together with our partners, we invested in a handful of non-AI fast-growing companies like Facebook, Alibaba, Xiaomi, Yandex, DeliveryHero, Revolut, etc, and a significant amount of yet-to-become successful AI startups like UiPath, Databricks, Ring, Zipline, DataRobot, etc.

The full potential of AI is just beginning to be realized. We anticipate significant returns in the future as the industry continues to mature and expand. Based on our estimates, around $17 billion was invested in AI between 2000 and 2015, while the total value of exits from AI startups from 2007 to 2022 was around $350 billion. This indicates that AI investments have been generating substantial returns for investors and that the trend is likely to continue as the industry evolves.

At Tryvesting, our focus is on identifying and nurturing the most promising AI startups, ensuring that our partners have the opportunity to participate in the massive growth that lies ahead in this exciting and rapidly developing sector.

What is your investment strategy when it comes to selecting AI startups, and how do you ensure a diverse portfolio that caters to the interests of your limited partners?

Our investment strategy involves several key components:

1. Cultural Fit and Mutual Learning: We prioritize teams that share our values and are eager to learn from our partners and vice versa, fostering a collaborative environment that drives the success of the business.

2. Targeting Underserved Regions: By focusing on regions with limited venture capital available, we can identify promising AI startups that might otherwise be overlooked, giving us a competitive advantage and increasing the chances of discovering exceptional opportunities.

3. Relocatable Teams: We value teams that are willing and at least partially able to relocate to Silicon Valley at a later stage of their lifecycle, as this ensures better access to resources, connections, and talent that can contribute to the growth and success of the startups.

4. Early-Stage Focus: Concentrating on early-stage startups allows us to actively add value by providing strategic guidance, mentorship, and resources, while also minimizing the risk associated with larger investments in more mature companies.

5. Diversification across Sectors: Our portfolio is designed to be diverse, encompassing a range of AI subsectors, such as machine learning, robotics, natural language processing, and computer vision. This diversification helps to spread risk and capitalize on various opportunities within the AI space.

6. Active Portfolio Management: We take a hands-on approach to managing our portfolio, providing ongoing support, mentorship, and access to our network of industry experts and resources. This level of involvement ensures that our investees have the best possible chance of success.

7. Limited Partner Involvement: We actively engage our limited partners in the investment process, taking into account their interests, expertise, and industry connections when selecting AI startups for our portfolio and nurturing them until the exit. This not only helps to diversify our investments but also leverages the unique strengths of our partners to support the growth and success of our portfolio companies.

By employing this investment strategy, we ensure a diverse portfolio that caters to the interests of our limited partners and optimizes our chances of generating strong returns from the rapidly growing AI industry.

As a limited partner, what kind of support and resources can I expect from Tryvesting to help me learn more about AI and its potential impact on my existing business or career?

At TryvestingTryvesting, we understand the importance of keeping our limited partners well-informed and educated about AI and its potential impact on their existing businesses or careers. As a valued partner, you can expect the following support and resources from us:

1. Access to AI industry insights: We provide regular updates and reports on the latest AI trends, technologies, and market dynamics to help you stay informed about the industry's development and its potential implications.

2. Workshops and seminars: We organize exclusive workshops, seminars, and networking events featuring leading AI experts, entrepreneurs, and thought leaders. These events provide an opportunity for you to gain valuable knowledge and insights, ask questions, and connect with like-minded individuals.

3. Personalized guidance: Our experienced team of AI professionals and business strategists is available for one-on-one consultations and guidance on how AI can be integrated into your existing business or career. We can help you identify potential use cases, evaluate the feasibility of AI implementation, and provide advice on the best way to proceed.

4. Access to our portfolio companies: As a limited partner, you will have the opportunity to engage with our portfolio companies, learn from their experiences, and explore potential collaborations that could benefit both parties.

5. Networking opportunities: We facilitate introductions and connections within our extensive network of AI experts, entrepreneurs, and industry leaders. This helps you expand your professional network and gain access to valuable resources and potential business opportunities in the AI space.

6. Online resources and educational materials: We curate a collection of high-quality online resources, including articles, whitepapers, case studies, and e-learning courses, to facilitate your ongoing education in AI and related technologies.

7. Collaborative projects: We encourage our limited partners to collaborate with each other and our portfolio companies on AI-related projects. This hands-on experience can provide invaluable insights and help you apply AI concepts to real-world scenarios.

8. Exclusive access to Tryvesting events: As a limited partner, you will receive invitations to Tryvesting's exclusive events, such as demo days, roundtable discussions, and innovation showcases. These events present an excellent opportunity to witness cutting-edge AI technologies in action and engage in meaningful conversations with industry experts.

9. Mentorship opportunities: If you're interested in mentoring AI startups, we can connect you with our portfolio companies and other AI ventures seeking guidance and support from experienced professionals like yourself.

At Tryvesting, we believe that our limited partners' success is our success. By providing you with the necessary support, resources, and opportunities, we aim to help you harness the power of AI to unlock new possibilities for your business and career.

What is the typical timeline for an investment in an AI startup, from the initial funding to the exit, and how will Tryvesting support these startups throughout this period?

The typical timeline for an investment in an AI startup can vary significantly depending on the specific company, its development stage, and market dynamics. Generally, it may take anywhere from 5 to 10 years or more from the initial funding to a successful exit. During this period, Tryvesting is committed to supporting our portfolio companies in a variety of ways:

1. Initial due diligence and investment decision: We conduct thorough due diligence before making an investment, including evaluating the startup's technology, team, market potential, and business model. This process ensures we only invest in companies that align with our investment strategy and have a high likelihood of success.

2. Post-investment support: Once we've invested in a startup, we actively support its growth by providing strategic guidance, business development assistance, and operational expertise. Our general partners work closely with the portfolio companies, leveraging their industry knowledge and experience to help them navigate challenges and capitalize on opportunities.

3. Networking and partnerships: We facilitate connections between our portfolio companies and our extensive network of industry experts, potential customers, and strategic partners. This can help accelerate the growth of the startups and create valuable synergies.

4. Follow-on funding: As the startup progresses and requires additional capital, Tryvesting can participate in follow-on funding rounds to help the company continue its growth trajectory. We also work with our network of co-investors to ensure that our portfolio companies have access to sufficient capital.

5. Exit support: When the time comes for a potential exit, whether through an acquisition, merger, or IPO, Tryvesting works closely with the startup's management team to maximize the value for all stakeholders. This includes providing strategic advice, negotiation support, and assistance in navigating the complex exit process.

Throughout the investment lifecycle, Tryvesting remains a committed partner to our portfolio companies, ensuring that they have the necessary resources and support to achieve long-term success.

What are the key metrics and indicators you use to track the performance of the AI startups you invest in, and how do you ensure transparent communication with your limited partners regarding these metrics?

At Tryvesting, we firmly believe that what gets measured gets improved. To ensure the growth and progress of our portfolio companies, we meticulously track a vast array of metrics and indicators. It is important to note that not all of the below metrics hold equal significance in determining the future success of an AI startup. Tryvesting's true value lies in our ability to identify, prioritize, and focus on the key metrics that are strongly correlated with a startup's future success.

Our expertise and experience in the AI industry enable us to concentrate on the most critical factors, ensuring informed investment decisions for our limited partners. By closely monitoring these indicators, we not only facilitate the improvement of our portfolio companies but also help attract larger venture capital firms to invest in them. By investing in these startups and gaining access to more extensive data, we provide bigger VC firms with a more comprehensive and rational basis for their investment decisions, reducing the reliance on emotion-driven investing.

This data-driven approach, coupled with our unique know-how, sets Tryvesting apart and positions us as a leading AI-focused venture capital fund that not only fosters improvement but also streamlines the investment process for larger venture capital firms.

Here is the long list of metrics that we typically choose from together with our partners and investees:

Team value metrics:

1. Time-to-market: The time it takes to bring a product or feature from conception to market, indicating a team's efficiency and execution capabilities.

2. Agile maturity level: The extent to which the team follows agile methodologies and best practices, contributing to their adaptability and responsiveness to change.

3. Team velocity and consistency: Assess the average number of user story points completed per sprint, along with the variation in points completed across sprints. This metric helps evaluate the team's consistency, predictability, and ability to manage workload effectively.

4. Sprint completion rate: The percentage of planned tasks completed within a given sprint, reflecting the team's ability to meet deadlines and manage workloads.

5. Backlog management and prioritization: Evaluate the team's prioritization and backlog management practices by considering user story point aging, carryover, and the alignment of backlog items with strategic OKRs. This metric provides insight into the team's ability to focus on high-priority tasks and maintain a manageable workload.

6. Hypothesis (in)validation time: The time it takes for the team to validate or invalidate a hypothesis. A shorter time indicates that the team is quickly testing and learning from their hypotheses.

7. Contrarian hypothesis rate: The proportion of hypotheses that are contrarian or not obvious compared to the total number of hypotheses tested. A higher rate may indicate that the team is willing to explore unconventional ideas that may lead to innovation.

8. Experimentation frequency: The number of experiments or tests conducted by the team within a given time frame. A higher frequency indicates that the team is actively testing hypotheses and learning from the results.

9. Post-experiment reflection: The extent to which the team engages in reflection and analysis of the results of their experiments. This can be assessed through team discussions, retrospectives, or post-mortems. A high level of reflection may indicate that the team is committed to learning from their hypotheses and experiments.

10. Pivot success rate: The number of successful business model pivots or strategy shifts as a percentage of total attempts, reflecting the team's adaptability and resilience in the face of changing market conditions.

11. Learning agility score: An assessment of the team's ability to acquire new skills and knowledge, adapt to new situations, and apply lessons learned from past experiences.

12. Stakeholder feedback receptiveness: A qualitative assessment of the team's openness to feedback from limited partners, board members, and other stakeholders.

13. Exit alignment index: A measure of the team's alignment with the desired exit outcomes of limited partners, considering factors like valuation, exit timeline, and potential acquirers.

14. Customer-centricity score: A qualitative assessment of the team's commitment to prioritizing customer needs and delivering exceptional customer experiences.

15. Cross-functional collaboration index: A measure of the extent to which different departments or functions within the team collaborate effectively, driving innovation and efficiency.

16. Employee engagement rate: The percentage of employees who report feeling engaged and motivated in their work, contributing to higher levels of productivity and retention.

17. Workforce scalability and agility: Examine the team's ability to pivot into new business models, perceive input from various sources, adapt to feedback, and learn from experiences. This metric highlights the team's flexibility and willingness to evolve in response to changing circumstances and stakeholder needs.

18. Employee churn rate: The percentage of employees who leave the company within a given period, reflecting the team's ability to retain talent and maintain stability.

19. Industry network score: A measure of the team's connections and relationships within the AI industry, contributing to their ability to access resources, knowledge, and partnerships.

20. AI Expertise Percentile: This metric measures the percentile of the team members' AI expertise based on public ratings of AI researchers. It considers the team's collective expertise in different AI subfields such as computer vision, natural language processing, and machine learning, and assigns a percentile ranking based on how their expertise compares to other AI researchers.

Technicalities like:

21. Accuracy: The proportion of correct predictions or responses made by the AI system compared to the total number of predictions or responses. This is a fundamental measure of the AI's ability to make correct decisions or produce accurate results.

22. Precision and recall: Precision measures the proportion of true positives (relevant items correctly identified by the AI) out of all the items identified as positives. Recall measures the proportion of true positives out of all relevant items. These metrics are particularly useful when dealing with imbalanced datasets or when the cost of false positives and false negatives is not equal.

23. F1 score: The harmonic mean of precision and recall, providing a single metric that balances the trade-off between these two measures. A higher F1 score indicates a better balance between precision and recall, which is essential for many AI applications.

24. AUC-ROC (Area Under the Receiver Operating Characteristic curve): AUC-ROC is a performance measurement for classification problems at various thresholds settings. It tells how much the model is capable of distinguishing between classes. The higher the AUC, the better the model is at predicting true positives while minimizing false positives.

25. Problem-solving speed: The time taken by the AI system to solve a problem or complete a task. Faster problem-solving can be indicative of a more intelligent AI system, particularly when comparing similar AI systems or algorithms.

26. Adaptability and generalization: The ability of the AI system to adapt to new situations and generalize its knowledge to previously unseen scenarios. This can be measured by evaluating the AI's performance on new or out-of-sample data, and assessing its ability to transfer knowledge from one domain to another.

27. Human-like reasoning: In some cases, AI systems that can mimic human-like reasoning processes are considered more intelligent. This can be assessed by analyzing the AI's ability to explain its decision-making process, or by evaluating its performance on tasks that require logical reasoning or problem-solving skills.

28. Creativity and innovation: The ability of the AI system to generate novel solutions, ideas, or outputs. This can be assessed by evaluating the diversity and originality of the AI's outputs, or by measuring its performance on tasks that require creativity or out-of-the-box thinking.

29. Training time: The time it takes for an AI model to learn from the training data and reach a satisfactory level of performance. Shorter training times can be beneficial for quickly adapting to new data or rapidly iterating on model improvements.

30. Inference time (latency): The time it takes for the AI model to make a prediction or generate a response once it has been trained. Lower inference times are critical for real-time applications or situations where quick decision-making is necessary.

31. Memory usage: The amount of memory (RAM) required by the AI model during training and inference. Lower memory usage allows for the deployment of AI systems on devices with limited memory resources, such as edge devices or mobile phones.

32. Model size: The size of the AI model, typically measured in terms of the number of parameters or the storage space required to store the model. Smaller models are easier to deploy and can be more efficient in terms of memory usage and computational requirements.

33. FLOPs (Floating Point Operations per Second): A measure of the computational complexity of an AI model, representing the number of floating-point operations required to train or run the model. Lower FLOPs can indicate a more computationally efficient model.

34. Energy consumption: The amount of energy required to train and run the AI model. Lower energy consumption can be crucial for battery-powered devices or reducing the environmental impact of AI systems.

35. Throughput: The number of predictions or responses generated by the AI system per unit of time. Higher throughput can be essential for applications that require processing large volumes of data quickly.

36. Scalability: The ability of the AI model to maintain its performance and efficiency when applied to larger datasets, more complex tasks, or when running on more powerful hardware. Scalability can be assessed by analyzing how the model's training time, inference time, and other efficiency metrics change as the size of the problem or the available resources increase.

37. Model robustness: We assess the stability of the AI models by testing them against adversarial attacks, noisy data, and other challenging conditions. This helps us understand the resilience of the technology and its ability to perform consistently in real-world scenarios.

38. Transferability: We evaluate the AI models' ability to generalize and perform well on new, unseen data or tasks. This helps us assess the versatility of the technology and its potential for broader applications across different industries.

39. Continuous learning capabilities: We analyze the AI systems' ability to adapt and learn from new data, without forgetting previously learned knowledge. This helps us determine the long-term viability of the technology in constantly evolving environments.

40. Interoperability: We examine the AI models' compatibility with various data formats, software systems, and hardware platforms. This helps us gauge the ease of integration and deployment in diverse settings, which is crucial for widespread adoption.

41. Data efficiency: We assess the amount of training data required for the AI models to achieve satisfactory performance. This helps us understand the technology's feasibility in situations where data is scarce or expensive to collect.

42. Data consistency: Consistency in data representation and format is vital for effective AI system performance. We measure data consistency by evaluating the level of standardization and uniformity in the data. This can include examining the use of common units, scales, and encoding formats across the dataset. Startups with consistent data can more efficiently train and deploy their AI systems, leading to better performance and reduced data preprocessing efforts.

43. Data accuracy: The accuracy of source data directly impacts the reliability of AI systems. We assess data accuracy by measuring the percentage of correct, valid, and precise data points in the dataset. This can involve comparing the data against known ground truth values or conducting data audits to identify and quantify errors. By investing in startups with high data accuracy, we support the development of AI systems that generate dependable and trustworthy insights.

44. Data provenance: We evaluate the origin and history of the source data used by the startup. Understanding the data's provenance helps us verify its authenticity, credibility, and potential biases. By examining the data's lineage, we can also identify potential issues related to data quality and truthfulness.

45. Data completeness: A complete dataset includes all the necessary data points and features required for AI systems to make accurate predictions or decisions. We evaluate data completeness by examining the coverage and comprehensiveness of the dataset, as well as the presence of missing or incomplete data points. Startups that prioritize data completeness are more likely to build AI systems capable of handling a wide range of scenarios and delivering reliable results.

46. Data integrity: Ensuring the integrity of the source data is crucial for maintaining its accuracy, consistency, and completeness. We assess data integrity by evaluating the startup's data quality management practices, such as data cleansing, validation, and error detection methods. Robust data integrity practices help minimize the risk of data corruption and ensure that AI systems are trained on reliable and high-quality data.

47. Data diversity: A diverse dataset is crucial for training AI systems to perform well across various scenarios and user groups. We assess the diversity of the source data by analyzing its representation of different demographics, regions, and contexts. Startups that utilize diverse data are more likely to develop AI systems that cater to a broader range of users and avoid biases.

48. Data governance: We evaluate the data governance practices followed by the startup, such as data access control, data privacy policies, and compliance with relevant regulations. Robust data governance ensures the responsible management of source data and helps maintain its freshness, truthfulness, and security.

49. Third-party data dependence: We assess the extent to which startups rely on third-party data sources and the potential risks associated with this dependence. Metrics in this area include the percentage of third-party data used, the diversity of data providers, and the level of control the startup has over its data sources.

50. Data freshness: The relevance and timeliness of the source data are essential for the effective functioning of AI systems. We measure data freshness by tracking the frequency of data updates and the average age of the data. Startups that continuously update their datasets and use the most recent information in their AI systems are better positioned to generate accurate and up-to-date insights.

51. Legal compliance: Ensuring legal compliance with data protection regulations such as GDPR is essential to minimize risks and maintain trust. We assess legal compliance by reviewing the startup's data handling and processing practices, consent management procedures, and adherence to data protection principles.

52. Data ethics: Ethical considerations surrounding data usage are increasingly important in the AI industry. We evaluate data ethics by examining the startup's data collection methods, privacy policies, and efforts to minimize potential harm resulting from data usage, such as surveillance or discrimination.

53. Data biases and fairness: To prevent AI systems from perpetuating biases and unfairness, we assess the startup's awareness of potential biases in their data and their efforts to address these issues. Metrics in this area include the identification and quantification of biases, the implementation of fairness measures, and transparency in the handling of biased data.

54. Synthetic data: The use of synthetic data in AI development can significantly reduce the need for time-consuming and costly data collection. We evaluate the quality and abundance of synthetic data being used by the startup, as well as its effectiveness in training the AI system. This helps us gauge the potential for cost reduction and scalability.

55. Human-assisted technology: To measure how much of the AI technology is human-assisted, we look at the ratio of human intervention to machine-generated results. We analyze the cost and time spent on human assistance in the development and training of the AI system, as well as the ongoing involvement of humans in the system's operation. A lower human-assistance ratio indicates a more autonomous AI system, which can lead to greater efficiency and cost savings.

56. Automation-to-labor ratio: A measure of the proportion of automated processes to the number of employees involved in manual work. A high ratio may indicate over-automation, while a low ratio may suggest an overreliance on manual labor.

57. Cost of labor vs. cost of automation: Comparing the expenses associated with employee salaries and benefits to the costs of implementing and maintaining automation systems. A large disparity between these costs can reveal whether the team is striking the right balance.

58. Explainability: We evaluate the AI models' ability to provide clear and interpretable explanations for their predictions or decisions. This is particularly important in industries where transparency and trust are critical, such as healthcare, finance, and legal applications.

59. Ethical considerations: We analyze the fairness, privacy, and potential biases in the AI systems, ensuring that they adhere to ethical guidelines and avoid unintended consequences.

60. Innovation potential: We track the cutting-edge research and advancements in the AI field, comparing our portfolio companies' technologies against the latest developments to ensure that they remain competitive and innovative.

Customer success and product-market-time fit indicators:

61. Customer readiness: Conduct surveys or interviews to gauge the level of customer readiness and their willingness to adopt the AI product or service. If customers are not yet ready, it may be too early to launch the product.

62. Media and public sentiment: Track media coverage and public sentiment around AI products and services. Positive sentiment and increasing coverage may suggest that the market is becoming more accepting of AI offerings.

63. Investment trends: Monitor the level of investment activity in AI-related startups and projects. An uptick in investments could signal that the market is ready for new AI products and services.

64. Net Promoter Score (NPS): A metric that measures customer loyalty and likelihood to recommend the AI solution to others, typically based on a scale from -100 to 100.

65. Customer effort score (CES): A measure of how easy it is for customers to use the AI solution, with lower scores indicating a more user-friendly experience.

66. Customer satisfaction score (CSAT): A metric that measures customer happiness with the AI solution, typically based on a rating scale.

67. User feedback and reviews: Positive user feedback, testimonials, and high ratings on review platforms can help attract more users to the AI startup, driving network effects and growth.

68. Customer retention rate: The percentage of customers who continue to use the AI solution over time, indicating satisfaction with the product or service.

69. Willingness to pay (WTP): A metric that evaluates whether customers find enough value in the AI solution to justify paying for it, compared to alternative solutions.

70. Switching intention: The likelihood that customers will switch from their current solution to the AI startup's offering, indicating a preference for the new product or service.

71. Referral rate: The percentage of customers who actively refer the AI solution to friends, colleagues, or professional contacts, demonstrating their satisfaction and confidence in the product.

72. Virality coefficient: A measure of how many new users are acquired through referrals from existing users, indicating the extent to which the startup's growth is driven by word of mouth and organic sharing.

73. Network density: The number of connections or interactions between users within the platform, which can indicate the strength of network effects. High network density suggests that users are actively engaging with one another and finding value in the connections they make through the platform.

74. Evangelism index: A measure of customer enthusiasm for the AI solution, including their willingness to promote it through social media, reviews, and other channels.

75. Loss aversion: A metric that evaluates how customers would feel if the AI solution was no longer available, with higher levels of aversion indicating a greater perceived value.

76. User engagement: The frequency and duration of customer interactions with the AI solution, demonstrating the level of interest and involvement in the product or service.

77. Feature usage rate: The percentage of customers who utilize specific features or functionalities within the AI solution, providing insights into the most valuable and appealing aspects of the product or service.

78. Churn prediction: An estimation of the likelihood that customers will discontinue using the AI solution, based on factors such as usage patterns, satisfaction scores, and competitive landscape.

79. Onboarding time: The average duration required for a customer to become proficient with the AI solution, with shorter onboarding times indicating a more intuitive user experience.

80. Customer support ticket volume: The number of support requests submitted by customers, which can highlight areas for improvement in the AI solution or user experience.

81. First contact resolution rate (FCR): The percentage of customer support inquiries that are resolved on the first interaction, demonstrating the effectiveness of the AI startup's customer support team.

82. Adoption rate: The speed at which new customers begin using the AI solution after signing up or purchasing, indicating the effectiveness of the onboarding process and initial appeal of the product or service.

83. Time to value (TTV): The average time it takes for a customer to realize the benefits of using the AI solution, with shorter times indicating a more impactful product or service.

Revenue drivers:

84. Market size and growth: We analyze the target market size, growth rate, and potential penetration of the AI solution. This helps us estimate the total addressable market and revenue potential for the technology.

85. Competitive landscape: We assess the competitive positioning of the AI technology by comparing it with existing solutions and potential competitors. This helps us understand the startup's unique selling points and barriers to entry, which contribute to its potential for revenue generation.

86. Target market segment size: The size of the target market segment (underserved or overserved) that the AI startup aims to serve. A focus on underserved customers may indicate a more disruptive approach, while targeting overserved customers may suggest a less disruptive strategy.

87. Bounce rate: The percentage of visitors who leave a website after viewing only one page. This metric can be used to measure the effectiveness of a website's design and content in attracting and retaining visitors.

88. Conversion rate: The percentage of visitors who take a desired action, such as making a purchase or filling out a form. This metric can be used to measure the effectiveness of a website's user experience and marketing strategies.

89. Channel effectiveness: The effectiveness of each acquisition channel, such as social media, search engine advertising, or email marketing. This metric can be used to identify which channels are driving the most traffic and conversions, and optimize marketing efforts accordingly.

90. User growth rate: The rate at which the number of users is increasing, indicating the potential for revenue and associated network effects to take hold as more users join the platform and contribute value

91. Active user growth rate: The rate at which the number of active users is increasing over time. A consistent and sustainable growth rate can indicate product-market fit.

92. Market penetration rate: The percentage of the target market that the AI startup has successfully reached. A higher penetration rate in an underserved market can indicate effective disruption.

93. Freemium conversion rate: The proportion of freemium users who convert to paid customers. A high conversion rate signifies the startup's ability to successfully transition users from free to paid offerings.

94. Time-to-premium: The average time it takes for a user to transition from a free or freemium offering to a premium plan. A shorter time-to-premium can indicate effective disruption by attracting and retaining high-value customers.

95. Platform stickiness: The ability of the AI startup to keep users, both paid and free, engaged and coming back to the platform. Metrics such as daily active users (DAU) and average session duration can help gauge platform stickiness, which is crucial for leveraging network effects.

96. Cross-platform integration: The extent to which the AI startup's technology can be integrated with other platforms or applications, which can help drive network effects by encouraging users to connect their accounts and share data across multiple services.

97. Ecosystem development: The growth of complementary products, services, and partnerships surrounding the AI startup's core offering, which can help enhance the overall value of the platform and drive network effects.

98. API availability and usage: The extent to which the AI startup provides an accessible and well-documented API for other businesses to integrate with their platform, demonstrating the team's openness to collaboration and enabling shared revenue opportunities.

99. Revenue sharing arrangements: The presence and terms of revenue-sharing agreements with partners, indicating the startup's commitment to fostering a mutually beneficial ecosystem where multiple businesses can generate income together.

100. Customer willingness to pay: A measure of how much customers are willing to pay for the AI solution or service, which can be assessed through surveys, interviews, or analysis of historical purchasing data. This metric helps startups understand the perceived value of their offering and set a price that maximizes revenues while maintaining customer satisfaction.

101. Competitive pricing: An analysis of the pricing strategies employed by competitors in the AI space, including their price points, bundling options, and discounts. This metric allows startups to position themselves strategically in the market by setting a price that is competitive while still reflecting the unique value proposition of their solution.

102. Price elasticity: The responsiveness of customer demand to changes in the price of the AI solution. Price elasticity can be estimated using historical sales data or through experiments, and it helps startups understand the potential impact of price adjustments on sales volume and revenues.

103. Pricing tiers and packaging: The structure of the pricing plan, including different tiers, feature bundles, and add-on options. Startups can track the popularity of each tier, customer preferences for specific features, and the proportion of customers who upgrade or downgrade their plans to optimize their pricing strategy.

104. Discounting strategy: The approach taken by the startup to offer price reductions or incentives, such as volume discounts, seasonal promotions, or loyalty programs. Monitoring the impact of discounts on sales, customer satisfaction, and profitability helps startups refine their discounting strategy and balance short-term revenue growth with long-term value creation.

105. Operational scalability: We analyze the AI solution's ability to scale effectively, both in terms of technical infrastructure and business operations. This is crucial for rapidly growing markets and ensuring long-term revenue generation.

106. Average revenue per user (ARPU): Comparing ARPU for free, freemium, and premium users can help determine the startup's focus on different customer segments and its ability to monetize each group effectively.

107. Monthly Recurring Revenue (MRR) growth rate: The rate at which the startup's MRR is increasing over time. Consistent MRR growth can be a sign of product-market fit.

108. Revenue diversification: We assess the startup's potential to diversify its revenue streams, either by targeting multiple industries or offering a range of products and services built around the core AI technology. This helps to mitigate risks and enhance revenue potential.

109. Intellectual property: We evaluate the strength and defensibility of the startup's intellectual property, including patents, trademarks, and copyrights. Strong IP protection can contribute to the revenue potential of the AI technology by preventing competition and creating additional licensing opportunities.

110. Partnerships and collaborations: We examine the startup's strategic partnerships and collaborations with industry leaders, technology providers, and other key stakeholders. Such alliances can accelerate market penetration, drive revenue growth, and enhance the overall value proposition of the AI technology.

Cost drivers:

111. Data collection costs: Expenses associated with gathering raw data required for training and testing AI models, including costs related to data acquisition, data partnerships, or web scraping.

112. Data annotation costs: Expenses for labeling and cleaning the data to ensure it's in a suitable format for training AI models, which may involve hiring data annotators, using annotation tools, or outsourcing to data annotation services.

113. Infrastructure costs: Expenses related to the hardware and software resources required for developing and training AI models, such as servers, GPUs, storage, and cloud computing services.

114. Personnel costs: Salaries, benefits, and other expenses associated with hiring and retaining AI experts, data scientists, engineers, and other team members involved in the development process.

115. Software licensing costs: Expenses related to acquiring licenses for AI frameworks, development tools, libraries, and other software required for building and deploying AI models.

116. Model training costs: Expenses associated with the computational resources required for training AI models, including cloud computing fees, energy costs, and the amortization of hardware investments.

117. Model validation and testing costs: Expenses related to evaluating and fine-tuning AI models, such as cross-validation, performance benchmarking, and testing on unseen data.

118. Deployment costs: Expenses associated with integrating AI models into products or services, including costs for API development, integration with existing systems, and deployment infrastructure.

119. Maintenance and updating costs: Ongoing expenses for maintaining AI models, ensuring they remain accurate and up-to-date, including costs for retraining, fine-tuning, and updating the data used for training.

120. Security and compliance costs: Expenses related to ensuring the AI system adheres to security best practices and regulatory requirements, such as data protection, privacy regulations, and industry-specific compliance.

121. Research and development costs: Expenses for exploring and experimenting with new AI techniques, algorithms, and technologies that may improve the AI system's performance or enable new capabilities.

122. Unexpected regulatory costs: Expenses related to meeting new or changing regulatory requirements, such as compliance with data protection laws, industry-specific regulations, or ethical guidelines. Startups should be prepared for potential regulatory costs by monitoring changes in the legal landscape and updating their compliance strategies accordingly.

123. Intellectual property (IP) protection costs: Expenses associated with securing and maintaining IP rights, such as patent filing and maintenance fees, trademark registration, and legal fees for IP disputes. Startups should factor in the costs of protecting their proprietary technology and brand assets.

124. Recruitment and training costs: Expenses related to hiring and onboarding new employees, including recruitment fees, training, and orientation. These costs can be significant, particularly for startups in the competitive AI talent market.

125. Infrastructure upgrade costs: Expenses associated with upgrading or expanding the technical infrastructure to support growth or increased demand, such as upgrading servers, implementing new software, or expanding data storage capacity.

126. Contingency costs: Funds set aside to cover unforeseen expenses, such as equipment failure, security breaches, or legal disputes. Startups should maintain a contingency reserve to ensure financial stability in the face of unexpected challenges.

127. Opportunity costs: The potential benefits that are forgone when choosing one alternative over another. Startups should consider opportunity costs when making strategic decisions, such as investing in R&D, entering new markets, or pursuing partnerships.

128. Customer support and maintenance costs: Expenses related to providing ongoing support, maintenance, and updates for AI solutions, including personnel, infrastructure, and communication costs. These costs can grow significantly as a startup's customer base expands.

129. Co-marketing efforts: The extent to which the AI startup engages in joint marketing initiatives with partner businesses, demonstrating their willingness to support the growth of their partners and collaborate on revenue generation.

130. Partner satisfaction: The level of satisfaction among the startup's partners, as measured by surveys, testimonials, or other feedback mechanisms. High partner satisfaction indicates that the startup is creating a supportive and profitable environment for all parties involved.

131. Partner success stories: The number of case studies or testimonials showcasing the success of partners who have leveraged the AI startup's platform to grow their own businesses, indicating that the team is enabling others to make money alongside them.

132. Ongoing partner support: The level of resources, training, and assistance provided to partners by the AI startup, demonstrating their commitment to helping other businesses succeed and generate revenue within the shared ecosystem.

Profitability drivers:

133. Customer lifetime value (CLTV): We assess the CLTV, which estimates the total revenue a startup can generate from a customer over the entire duration of their relationship. A high CLTV suggests that the AI technology has the potential to generate significant profits over time.

134. Customer acquisition cost (CAC): We compare the CAC, which is the total cost of acquiring a new customer, to the CLTV. A low CAC relative to the CLTV indicates that the AI startup can acquire customers profitably and efficiently.

135. Gross margin: We analyze the gross margin of the AI solution, which represents the difference between the revenue generated and the cost of goods sold. Higher gross margins indicate the potential for higher profitability as the business scales.

136. Operating margin: We evaluate the operating margin, which measures the efficiency of the startup's operations by comparing its operating income to its revenue. A healthy operating margin indicates that the startup can generate profits while managing its operational expenses effectively.

137. Payback period: We calculate the payback period, which is the time it takes for the startup to recover its initial investment in acquiring a customer. A shorter payback period signifies quicker returns on investment and higher profitability potential.

138. Breakeven point: We analyze the breakeven point, which is the level of sales at which the startup's revenues equal its expenses. Understanding the breakeven point helps us determine how quickly the AI startup can become profitable.

139. Unit economics: We study the unit economics of the AI solution, which considers the revenues and costs associated with a single unit of the product or service. Positive unit economics indicate that the startup can generate profits as it scales, while negative unit economics may require adjustments to the business model or pricing strategy.

140. Return on investment (ROI): We measure the ROI of the AI technology, which is the ratio of the net profit generated by the investment to the initial investment cost. A higher ROI suggests that the AI technology can deliver significant profits relative to its cost.

141. Asset turnover: This ratio measures how efficiently a company uses its assets to generate revenue. For AI startups, this can be computed as the total revenue divided by the total assets (including intellectual property, data, infrastructure, hardware, and software). A higher asset turnover ratio indicates better utilization of assets. Asset turnover may vary depending on the AI startup's business model and stage of development. For instance, early-stage startups might have lower asset turnover ratios, as they invest in R&D, infrastructure, and personnel to develop their AI technology.

142. Inventory turnover: This ratio measures how efficiently a company sells and replaces its inventory. While this metric is more relevant to manufacturing and retail businesses, it can be adapted to AI startups by considering the speed at which they monetize their AI solutions or services. AI startups may not have physical inventory, but they may have digital products, licenses, or service offerings that are sold or delivered to clients. In this case, inventory turnover could be calculated as the cost of goods sold (COGS) divided by the average value of digital products or services. For AI startups with physical products or components, this ratio is important for assessing how efficiently the company manages its inventory. A higher inventory turnover ratio indicates better inventory management and faster sales.

143. Accounts receivable turnover: This ratio measures how efficiently a company collects payments from its customers. For AI startups, this can be computed as the total revenue divided by the average accounts receivable. A higher accounts receivable turnover ratio indicates that the startup is collecting payments more quickly. AI startups may offer different payment terms depending on their products or services, such as one-time payments, subscriptions, or usage-based pricing. As a result, accounts receivable turnover ratios may vary depending on the startup's revenue model and customer base

144. Burn rate and runway: We assess the startup's burn rate, which is the rate at which it spends its capital, and the runway, which is the time it takes to exhaust its available capital. A lower burn rate and longer runway imply better financial stability, allowing the AI startup more time to achieve profitability.

Successful exit indicators:

145. Market size and growth rate: We evaluate the market size and growth rate of the AI technology's target industry. A larger market with a high growth rate indicates a greater potential for exits, as it attracts more strategic buyers and investors.

146. Competitive landscape: We assess the competitive landscape, including the number and strength of competitors in the market. A less saturated market with a few dominant players may present better exit opportunities, as strategic buyers may be more inclined to acquire a startup with unique AI technology.

147. Revenue growth and profitability: We closely monitor the startup's revenue growth and profitability trends. Consistent revenue growth and increasing profitability can make the startup more attractive to potential acquirers or set the stage for a successful IPO.

148. Customer base and diversification: We examine the startup's customer base and the degree of customer diversification. A large and diversified customer base can help reduce risks and improve exit potential, as it makes the startup more appealing to strategic buyers or public investors.

149. Intellectual property (IP) and defensibility: We analyze the startup's IP portfolio and the defensibility of its AI technology. Strong IP protection and unique technological advantages can increase the likelihood of an exit, as they provide a competitive edge and a barrier to entry for potential competitors.

150. Strategic partnerships and collaborations: We consider the startup's strategic partnerships and collaborations with established industry players. These relationships can enhance the startup's credibility and attractiveness in the eyes of potential acquirers or public investors, increasing the chances of a successful exit.

151. Management team and talent: We evaluate the startup's management team and the quality of its human capital. A strong, experienced management team with a track record of success can contribute to a higher exit potential, as potential acquirers or public investors may perceive the team as capable of driving further growth.

152. Regulatory environment and compliance: We assess the regulatory environment in the startup's target market and its compliance with relevant laws and regulations. A favorable regulatory environment and a strong record of compliance can contribute to a higher exit potential, as potential acquirers or public investors may view the startup as less risky.

153. Scalability: We examine the startup's ability to scale its AI technology and operations efficiently. Scalability is a critical factor for potential acquirers or public investors, as it indicates the startup's capacity to grow and generate higher returns in the future.

154. Exit history and comparables: We analyze the exit history and comparables of similar AI startups in the industry. This provides insights into the likely exit scenarios, valuations, and potential buyers or investors for the AI startup, helping us assess its exit potential more accurately.

Risks

155. Market Risk: Analyze the volatility of the AI industry and the potential impact of market fluctuations on the startup's performance. This metric helps investors understand the susceptibility of the AI startup to external market factors.

156. Technology Obsolescence Risk: Assess the risk of the AI startup's technology becoming outdated or surpassed by competitors, considering factors such as the pace of innovation in the sector and the startup's commitment to continuous improvement. This metric highlights the need for the startup to stay at the cutting edge of AI technology.

157. Competitive Landscape Risk: Evaluate the startup's position within the AI industry, considering the number of direct and indirect competitors, market share, and competitive advantages. This metric helps gauge the likelihood of the startup maintaining or increasing its market position.

158. Regulatory and Compliance Risk: Examine the potential impact of evolving regulations and policies, such as data privacy laws (e.g., GDPR) and ethical considerations, on the startup's operations and growth prospects. This metric highlights the importance of adhering to legal and ethical standards in the AI industry.

159. Intellectual Property (IP) Risk: Assess the strength and protection of the startup's proprietary algorithms, models, and technology, as well as the risk of IP infringement or disputes. This metric emphasizes the need to secure and defend the startup's intellectual property.

160. Team and Key Personnel Risk: Evaluate the stability and expertise of the startup's core team, including the potential impact of key personnel leaving or underperforming. This metric highlights the importance of retaining and nurturing top talent within the organization.

161. Customer Concentration Risk: Analyze the distribution of the startup's customer base, considering factors such as the number of customers, revenue concentration, and customer retention rate. This metric helps investors understand the risk associated with overreliance on a small number of customers.

162. Financial Risk: Assess the startup's financial health, including metrics such as debt-to-equity ratio, liquidity, and cash flow. This metric highlights the financial stability of the AI startup and its ability to manage financial obligations.

Besides aggregated metrics, at Tryvesting, we encourage all our partners to carefully study the raw logs of our investees and come up with their own suggestions for metrics. This hands-on approach allows our partners to gain a deeper understanding of the AI startups' operations, challenges, and opportunities. By engaging in data mining and creating customized metrics, our partners can identify previously overlooked insights and trends that can significantly improve the chances of success. This collaborative process not only helps optimize the performance of our investee companies but also fosters a strong sense of partnership and shared responsibility among all the stakeholders involved in the journey towards AI-driven growth and prosperity.

At Tryvesting, we prioritize transparent communication with our limited partners regarding metrics and other essential aspects of our investments. We achieve this through several approaches:

1. Regular Reporting: We provide detailed and regular reports to our limited partners, outlining the performance metrics of our investee companies. These reports include both quantitative and qualitative analyses, offering a comprehensive view of the progress and challenges faced by the startups.

2. Open Channels of Communication: We maintain open lines of communication with our limited partners through various channels, such as instant messaging, email, and video calls. We encourage our partners to reach out with any questions, concerns, or suggestions they may have about the metrics or other aspects of our investments.

3. Partner Meetings: We organize periodic meetings and webinars for our limited partners, where they can discuss the metrics, share insights, and stay updated on the latest developments in the AI industry. These meetings also provide an opportunity to address any concerns and to gather feedback from our partners.

4. Secure Dashboard App: We offer a secure, password-protected mobile app where our limited partners can access up-to-date information on the performance metrics of our investee companies. This platform ensures that our partners have easy access to the data they need, whenever they need it.

5. Customized Metrics: As mentioned earlier, we encourage our partners to engage in data mining and develop their own customized metrics. By involving our limited partners in the process, we ensure that they have a clear understanding of the metrics and their significance, fostering a sense of ownership and engagement.

Through these measures, we strive to maintain transparency and trust with our limited partners while working together towards the common goal of realizing the full potential of AI-driven businesses.

How does Tryvesting plan to adapt and stay ahead in the ever-evolving AI landscape, ensuring that the investments made today remain relevant and valuable in the future?

At Tryvesting, we recognize the dynamic nature of the AI landscape and are committed to staying ahead of the curve, ensuring that the investments we make today remain relevant and valuable in the future. As a new venture operating like a startup, we are agile, adaptable, and keenly attuned to the market. Our approach is deeply rooted in the agile methodology, which equips us with the ability to respond promptly to changes and effectively channel insights to our investees. Here are some ways we plan to adapt and stay ahead in the AI landscape:

1. Continuous Learning: We foster a culture of continuous learning within our organization and among our partners, staying informed about the latest technological advancements, industry trends, and market shifts. By keeping our fingers on the pulse of AI developments, we can make well-informed investment decisions and provide timely guidance to our investees.

2. Flexible Investment Strategy: Our investment strategy is designed to be flexible, allowing us to pivot and adapt as the AI landscape evolves. We actively reassess our portfolio, focus areas, and investment criteria in response to emerging opportunities and challenges, ensuring that we remain at the forefront of the industry.

3. Collaborative Ecosystem: We cultivate a strong network of industry experts, academia, and other AI-focused organizations, enabling us to exchange knowledge and insights, collaborate on innovative projects, and stay abreast of the latest developments in the field.

4. Active Portfolio Management: We take a hands-on approach to portfolio management, working closely with our investee companies to help them navigate the ever-changing AI landscape. We provide strategic guidance, share insights about market changes, and facilitate connections with other stakeholders in the ecosystem, empowering our investees to adapt and thrive.

5. Embracing Innovation: We embrace innovation not only in the AI startups we invest in but also in our own operations and processes. We continuously seek ways to improve and refine our investment strategies, due diligence, risk management, and communication practices, ensuring that we remain agile and effective in an ever-evolving industry.

By combining our startup mindset, agile methodology, and a relentless focus on staying ahead of AI advancements, Tryvesting is well-positioned to adapt and thrive in the dynamic AI landscape, safeguarding the value and relevance of our investments for the future.

What is the minimum commitment required to become a limited partner at Tryvesting, and are there any additional fees or expenses I should be aware of?

At Tryvesting, we aim to make it accessible for investors to participate in the exciting opportunities presented by the AI industry. The minimum commitment required to become a limited partner in our fund is in line with the industry standards for AI-focused funds, which can range from $100,000 to $1,000,000 or more, depending on the size and target of the fund. However, we encourage you to reach out to us for specific details on the minimum commitment for our current fund offering. Typically, our limited partners commit around $250,000.

In addition to the capital commitment, limited partners should be aware of the standard fees associated with venture capital funds. These fees are:

• Management Fee: This is an annual fee that equals 2% of the committed capital, paid to the general partners for managing the fund, sourcing investment opportunities, and providing ongoing support to the portfolio companies.

• Carried Interest: This is the general partners' share of the profits generated by the fund, set at 20%. Carried interest is only paid once the limited partners have received their initial capital back, ensuring that the general partners are incentivized to generate strong returns for the limited partners.

It is important to carefully review the terms and conditions of the fund offering documents to fully understand the fees and expenses associated with becoming a limited partner at Tryvesting.