How We Built an AI Agent for Self-Service Analytics

2026-04-22 · Source: HackerNoon · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Intermediate, medium

Summary

OLX, a large classified product company, developed Talk2Data, an internal AI agent integrated into Slack, to address bottlenecks in analytics resource availability. This agent converts natural language questions into SQL queries and returns natural language answers directly within Slack. Unlike generic chatbots, Talk2Data operates as a controlled analytics interface, focusing on a constrained model of supported metrics and dimensions rather than the entire data warehouse schema. The project emphasized that effective NL2SQL requires structured data, rigorous metric definitions, and a semantic layer, rather than merely connecting an LLM to a database. Key outcomes included reducing ad-hoc request load on analysts and decreasing stakeholder time spent on analytics tasks, with the tool also proving valuable for finance teams.

Key takeaway

For Directors of AI/ML or MLOps Engineers considering internal NL2SQL solutions, prioritize data governance and semantic layer development over complex LLM prompting. Your success hinges on standardized metric definitions and a robust evaluation framework, ensuring accuracy and preventing data-driven misdecisions. Invest in user onboarding to teach stakeholders how to formulate precise questions for optimal results.

Key insights

Effective NL2SQL for business analytics requires structured data and a semantic layer, not just connecting an LLMs to a database.

Principles

• AI amplifies data problems.
• Production systems need bounded behavior.
• Good data matters more than good prompting.

Method

Talk2Data interprets user intent, resolves metrics/dimensions, generates SQL, queries the DWH, and returns formatted natural language responses within Slack, operating within a constrained semantic model.

In practice

• Formalize metric definitions rigorously.
• Align on data sources of truth.
• Define valid dimensions and entity vocabulary.

Topics

• AI Agents
• Self-Service Analytics
• NL2SQL
• Data Governance
• Metric Formalization

Best for: AI Engineer, MLOps Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by HackerNoon.