Background

Questions

1. Can you talk to us about the state of AI in 2023? What are the different key segments in the AI market?
2. Data labeling companies have a usage-based pricing model. Historically, creating data sets to train AI has been a bottleneck to building it. Is that still true? Can you talk about how few-shot learning could impact data labeling business models? How has the recession impacted usage? Are there any 80/20 opportunities to do more with smaller data sets?
3. Another trend we are seeing is companies like Flexport, Convoy, or Workrise using a lot of computer vision to scan physical documents like invoices, purchase orders, and bills of lading and digitizing them by using AI. Do you see that growing as a use case for data labeling companies to a similar scale as say, the AV companies?
4. Tell us what inspired you to build Nyckel. How did the company start?
5. How does Nyckel work? What does your AI stack look like? Do you own your AI stack end-to-end or build on top of other AI models?
6. Help us understand the differences between AutoML and ML-in-a-box?
7. What was your initial product-market fit? How did you get your first handful of customers, and what did they use Nyckel for? What does your core customer profile look like—including company type and buyer role?
8. Do these companies bring their own data to Nyckel, train the models and then, take the models to production?
9. There’s a trend of companies building on top of existing foundational models rather than building their models with proprietary data. Do you see this as a headwind for Nyckel?
10. Critics believe that LLM/GPT latency would have to come down and costs would have to reduce about 10x before it would be feasible to integrate AI-generated results into search. How much of a bottleneck is pricing and latency for Nyckel?
11. We see companies like Labelbox, Snorkel, and Scale moving into offering not only labeling but also APIs and services to train ML algorithms for the programmatic classification of images and text. What advantages do they have from vertical integration, if any?
12. Which are the companies that you believe are building a cohesive user experience spanning across different stages of the ML lifecycle?
13. Drawing a parallel between MLOps and DevOps, a lot of DevOps people actually came from Unix and were used to working with these point solutions. Then, they switched to GitLab with versioning, CI/CD, and all of that built into one platform. Do you see something similar happening in MLOps? Why did MLOps practitioners start with point solutions? What are the drivers for them to change?
14. Scale seems to be doing something similar to Nyckel with their data annotation studio and APIs to pull the results into your app. How do you see Nyckel positioned vs. Scale?
15. Do you see this trend as similar to Webflow selling to the marketing teams so that they don’t have to run back and forth to engineers to spin up marketing websites/landing pages? Will Nyckel, Scale, or others abstract the complexities of running/managing ML so product owners from marketing, finance, or other orgs can build on top of them in the future?
16. What do you see Nyckel becoming five years from now if everything goes well? What does success look like for Nyckel?