6 Weeks to Production: How a SaaS Analytics Firm Cut Data Request Time by 80% with AI

6 Weeks to Production: How a SaaS Analytics Firm Cut Data Request Time by 80% with AI

A leading SaaS company used StackAI to deploy a secure, text-to-SQL AI assistant connected to Snowflake—cutting 80+ hours of manual querying per month and giving sales and support teams instant access to insights.

A leading SaaS company used StackAI to deploy a secure, text-to-SQL AI assistant connected to Snowflake—cutting 80+ hours of manual querying per month and giving sales and support teams instant access to insights.

Client

Digital Advertising Firm

Challenge

Sales and support teams were slowed by dependence on data scientists for SQL queries, delaying insights and client responses.

Solution

A secure text-to-SQL AI agent that lets non-technical users query Snowflake in plain English, delivering instant insights.

Overview

A top retail and financial analytics SaaS company partnered with StackAI to democratize access to customer and retail data. Using StackAI’s no-code AI orchestration platform, the company built a text-to-SQL “data whisperer” agent that lets sales and support teams query Snowflake databases conversationally, without ever touching SQL.

Today, sales representatives can ask questions in natural language (“List the top 25 brands by unique shoppers in the last 12 months”) and receive structured insights, charts, and summaries instantly. This AI-powered assistant now enables analysts, sales engineers, and support teams to focus on higher-value analytics instead of repetitive query writing, improving speed, accuracy, and collaboration across departments.

80+ hours saved monthly across sales and support
4 hours per week saved per user (with just five active users)
80% lower AI development cost vs. traditional builds
6 weeks from concept to production deployment
Two full-time data scientist roles effectively replaced, without reducing headcount, as employees now focus on higher-value analysis and model development

The Problem: Sales and Support Bottlenecked by Data Requests

Sales and support teams frequently needed to generate on-the-fly reports to support client pitches or resolve data inquiries: everything from “What are the top demographics for this retailer?” to “Which brands had the fastest growth this quarter?”

Each request required a data scientist to craft, test, and validate custom SQL queries. That process took 15–20 minutes per request, translating to dozens of hours weekly spent on repetitive reporting work.

This workflow not only delayed sales cycles and client responses but also limited leadership’s ability to make rapid, data-driven decisions.

The company needed a way for non-technical teams to query Snowflake data directly securely, accurately, and at enterprise scale.

The Solution: Text-to-SQL Powered by StackAI

In just six weeks, the company’s technical sales and data teams built and deployed a text-to-SQL assistant using StackAI. This agent has four key aspects:

Conversational Input: A user asks a natural-language question like, “Show me top-selling brands among loyalty shoppers in the Northeast.”
Knowledge Base Context: The agent references three sources—sample SQL queries, documentation on Snowflake schemas, and the internal data dictionary.
Secure Query Generation: A private GPT-4o model converts the question into SQL, executes it directly in Snowflake via governed API, and cleans the output through Python preprocessing nodes.
Readable Output: Another model translates results into plain-English summaries and data visualizations ready for client presentations.

This virtual data analyst runs in seconds what used to take hours of back-and-forth with the analytics team.

“The team already had world-class data infrastructure in Snowflake — they just needed a way to make it usable by anyone. Working alongside their data scientists and sales leaders, we built an agentic layer that turns natural language into precise SQL in seconds. Watching non-technical users go from idea to query-driven insight instantly was one of those ‘this is what AI should be’ moments.”

Alberto Arrighi
AI Strategist

80+ Hours Saved Monthly, and Growing

Today, sales and support teams use the AI assistant daily to generate fast, reliable insights without touching SQL. With just five active users, the company already saves over 80 hours per month, roughly 4 hours per user, per week. As rollout expands to other teams, those gains are expected to scale dramatically.

This company continues to expand its AI footprint with StackAI, making internal data smarter, more accessible, and radically faster to use.

Encouraged by early success, the company plans to extend the workflow across hundreds of brands in its dataset, enabling looped query generation and drill-down reports for each brand on demand.Future iterations will allow internal users to create entire client-ready reports automatically—complete with SQL-backed insights, visual summaries, and customizable text sections. Leadership also envisions an eventual client-facing analytics portal powered by the same StackAI framework, once access control and data-governance refinements are complete.

By turning complex Snowflake data into conversational insight, this analytics leader eliminated one of its biggest operational bottlenecks. With StackAI, sales and support teams no longer wait on data scientists for every client request: they simply ask, and the answers arrive instantly.

The company now reports faster sales enablement, more informed leadership decisions, and measurable ROI from its AI investment—all achieved through a no-code, secure StackAI deployment.

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