AI-Powered Analytics Assistant

The Problem

The data engineering team was fielding repetitive data requests from business users and analysts — "what's the count for X today?", "give me last week's numbers for Y". These were not complex queries. They were L0 requests: simple lookups, recurring metrics, time-bound snapshots. But they were arriving constantly and consuming engineering bandwidth.

Two distinct groups needed help:

• 2–10 analysts doing L0 work — routine queries they could answer themselves given the right interface.
• 5–10 business users who needed real-time data on demand but couldn't write SQL.

The volume was variable: 10–100 queries per day depending on the time of month, with spikes at reporting cycles and month-end closes.

What Was Built

An AI analytics assistant that converts natural language questions into cloud SQL, executes them safely, and returns results — all without engineering involvement. Powered by enterprise agent frameworks, state-of-the-art LLMs, and a RAG layer over warehouse metadata.

A business user types: "What's today's count for orders pending dispatch?" — and gets a validated result in seconds, not a Jira ticket acknowledgment.

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Architecture & Tech Stack

Layer	Technology
Agent Framework	Enterprise Multi-Agent Development Framework
LLM Engine	Enterprise Large Language Models
Knowledge Base	RAG over warehouse schema metadata, column descriptions, curated examples
Query Engine	Cloud Analytical Data Warehouse
Language	Python

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Design Decisions

1. RAG for SQL Accuracy

• Problem: LLMs hallucinate table and column names without grounding, especially in a domain-specific warehouse with non-obvious naming conventions.
• Approach: Built a RAG layer indexing warehouse schema metadata, column descriptions, and curated example queries. The agent retrieves relevant context before generating SQL.
• Outcome: Reduced hallucinated column names and invalid joins to near zero across the covered query surface.

2. Handling Repeated, Real-Time Requests

• Problem: Business users were asking the same questions daily or weekly — "give me today's numbers". Each became a ticket. The need was for on-demand, repeatable access.
• Approach: The agent handles repeated natural language patterns consistently by grounding SQL generation in stable metadata. Users can re-ask the same question at any time and get current results.
• Outcome: Recurring data pulls moved entirely to self-serve. Engineering is no longer in the loop for L0 data requests.

3. SQL Validation Before Execution

• Problem: Valid-looking SQL can scan unexpectedly large datasets or return semantically wrong results.
• Approach: Added a dry-run validation step — the cloud warehouse estimates bytes scanned before execution. Anomalies are flagged before results are returned.
• Outcome: Protected against runaway queries and gave users confidence in what they were running.

4. Variable Load by Time of Month

• Problem: Query volume is not uniform — spikes at month-end reporting cycles, quiet mid-month.
• Approach: The tool handles variable load without pre-warming or capacity planning, as it runs on-demand against serverless cloud data warehouse execution models.
• Outcome: 10–100 queries/day handled without infrastructure management overhead.

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Key Learnings

• Eliminating L0 Requests is High ROI: The highest-value AI analytics tools aren't the ones that answer hard, esoteric questions — they're the ones that take simple, repeated questions completely off the engineering team's plate.
• Mental Models Matter: Self-serve works when the interface matches the user's mental model. Business users think in business concepts, not table schemas. The RAG layer bridges that gap.
• Safety Drives Adoption: Validation matters more than generation creativity. A tool that fails safely gets adopted. One that returns silent wrong answers gets abandoned immediately.