Data Readiness: A Pipe Dream in Real-World AI Applications

As an applied research leader at PromQL, I'm here to shatter the myth that data readiness is a pipe dream, and instead, introduce a revolutionary approach to building AI systems that truly understand your business language.

1. Anushut leads the applied research team at PromQL, a sponsor for the reliability track at AI engineers worldwide.
2. The talk is about how data readiness for AI systems in production environments is a myth.
3. Perfect, clean, annotated, and well-named data is not common, and even if it exists, there are always differences in naming conventions and structures across various systems.
4. In 2019, the solution was thought to be standardization and moving everything into platforms like Snowflake or Data Bricks, but this has only been 40% successful.
5. With the rise of AI and agents in 2023, semantic layers that understand specific data domains were proposed, but these also face challenges due to changing data domains every quarter.
6. In 2025, it is expected that AI will still require perfect data to work, which remains an unmet goal.
7. Poor data quality costs Fortune 500 companies an average of $250 million annually.
8. Semantic layers and knowledge graphs have been tried but cannot capture all the intricacies and exceptions in data.
9. AI does not understand business languages, jargon, or context-specific definitions; for example, "GM" could mean gross margin or general manager, depending on the domain.
10. Analysts and engineers with tribal knowledge have traditionally bridged this gap by understanding both the business and data systems to answer questions accurately.
11. The goal is to create an AI system that behaves like a day-zero smart analyst who learns from their mistakes, improves over time, and understands the business and domain context.
12. PromQL has been working on such a solution by decoupling language models (LMs) from answer generation; LMs are used to create plans in PromQL's deterministic runtime language instead.
13. This approach reduces hallucinations and allows for more accurate, distributed query execution across various data sources.
14. An example is provided where an AI system using PromQL can handle complex tasks like summarizing support tickets, extracting sentiment, and issuing credits based on the analysis.
15. The learning process in PromQL involves a prompt learning layer that improves the semantic graph and creates a company's business language (e.g., AcmeQL, GoogleQL).
16. Over time, the AI system becomes better at understanding context, naming conventions, relationships, and calculations across various systems and data sources.
17. PromQL aims to create an agentic semantic layer that reduces months of work into immediate starts, allowing AI to learn from interactions and improve itself with version-controlled semantic layers
18. Customers have reported success with PromQL, including a Fortune 500 food chain company and a high-growth fintech company.
19. PromQL's agentic semantic layer helps AI reach 100% accuracy on complex tasks, providing reliable AI solutions for businesses.

Source: AI Engineer via YouTube

❓ What do you think? What are your thoughts on the ideas shared in this video? Feel free to share your thoughts in the comments!