Lumen: Production-Grade Data Insights Platform

Leadership discussions are spontaneous, and those on-the-fly decisions demand systems that are highly accurate. We spent weeks building a natural language-based business insights platform for top retail players in India. In this talk, we will share the practical lessons we learned from building this system, detailing the failures encountered at various layers and what the reinforcements for those failures look like in practice.

We will explore the journey from 0 to 1, breaking down the technical hurdles of translating raw text into enterprise-grade analytics.

Key Technical Takeaways:

- From Prompt to Production: We will trace the evolution of the system—starting with the basic prompts one would put into a chat UI to generate SQL, all the way to deploying a production-grade product with robust guardrails built across every layer.
- Schema Readiness: We will tackle the challenge of evaluating and understanding exactly which kinds of database schemas are truly ready for LLM-driven querying.
- Domain Grounding & Hallucination Reduction: We will explore how to deeply ground the domain to handle multi-layered business logic. For example, we will demonstrate how to ensure a complex, nested query like, "In 2024, take the top branch by sale. In this branch, give a category-wise breakup of sales. For these categories, also give me the stock holding value," consistently maps to the correct internal metrics and returns precise facts rather than confident AI fabrications.

Bonus: We will share our architectural strategies for cutting LLM inference costs by 80% for this type of system.

1. How to go from Prompt to Production
2. Schema Readiness with LLMs
3. Domain Grounding & Hallucination Reduction