Since the AI revolution started, many have wrongly assumed that the analytics they use for traditional products are the same they need for AI-native products. This is a mistake: Traditional tools like Google Analytics and PostHog are insufficient—the infrastructure is inadequate, and they will not take sufficient advantage of data (all the prompts and their outputs) generated by an AI-native product, making you miss on valuable insights that could provide your business with a competitive advantage.
Take the example of the vast array of language data generated in AI products. Traditional analytics are focused on engagement data (page visits, clicks, session duration, retention over time, etc.) and certain business metrics (usually revenue). While these analytics are useful, they are far from the granular insights (quantitative and qualitative) you could gain from the complex language-based interaction between your users and AI models.
In case you’re unfamiliar, to start making sense of language in a quantitative way you should learn the concept of text vectorization and word embeddings. Embeddings is data—like words—that has been converted into an array of numbers, that is, a vector.
Embeddings also highlight why the nature of the infrastructure needed to make sense of AI analytics is different from traditional analytics. In AI analytics, you will need a robust solution for your vector databases that will store the word embeddings. Popular choices in the industry are Pinecone, Single Store, Chroma, Weaviate, Faiss, and Qdrant. Even databases traditionally used for other purposes, like Redis (which started as a cache), have become a popular choice among developers for vector databases.
Here’s where an AI Analytics solution like Props AI comes into the equation: Props AI takes care of building real-time data pipelines and transforming data for vector databases, saving you time and effort. With just a few lines of code, Props AI handles all the data engineering for you. Additionally, Props AI offers a dashboard that provides unique insights for AI-native apps, including clustering of LLM (or any AI model) inputs. This can give you a competitive edge in understanding user segmentation and how different audiences are using your AI product.
Preparing Your Business for AI Analytics
To effectively transition to AI analytics, your business needs to take several steps:
Understand the Requirements: Assess the specific needs of your AI-native product. Identify the type of data being generated and the insights you aim to gain.
Invest in the Right Tools: Choose the appropriate vector database that suits your needs. Consider the scale, performance, and integration capabilities of options like Pinecone, Chroma, or Redis.
Leverage AI Analytics Solutions: Implement a fully-managed solution like Props AI to automate the creation of real-time data pipelines and data transformation. This reduces the burden on your data engineering roadmap and ensures efficient data handling.
Train Your Team: Equip your team with the knowledge and skills needed to work with AI analytics. This includes understanding the concepts of embeddings, vector databases, and the unique metrics and insights (e.g., clustering) relevant to AI-native products.
Continuously Monitor and Optimize: Regularly review the insights provided by your AI analytics tools. Use this data to refine your product, enhance user experiences, and stay ahead of the competition.
By taking these steps and utilizing Props AI, your business can fully leverage the power of AI analytics, gaining deeper insights into user behavior and improving your product’s performance. Embrace the change, and position your business for success in the AI-driven landscape with the robust capabilities that Props AI offers.
Our future content will go beyond AI analytics, focusing on how Props AI can help you leverage RAG (Retrieval-Augmented Generation) and real-time optimization techniques to elevate your AI product.
Book a call with the founder Peter Kirkham at https://calendly.com/props-ai/intro.
