Case Study
Agentic AI · Quick Service Restaurants

Turning 490K+ transactions of dark data into real-time intelligence for a Singapore QSR chain

From manual labour reports to AI-powered conversational analytics — designed, built, and deployed in four weeks.

Industry
Food & Beverage · QSR
Region
Singapore
Engagement
4 weeks to production
Platform
Serverless AWS + Amazon Q

490K+

Transactions processed across 13 months of data

46%

Hidden productivity gap uncovered between time periods

4 wks

From architecture to production deployment

Plain-English analytics via Amazon Q — no SQL, no analysts

Fully serverless AWS architecture — scalable by design

Data access democratised across the whole business

Overview

A leading QSR operator managing multiple outlets across Singapore was making critical workforce decisions in the dark. Labour analytics relied entirely on manual reporting and fragmented data sources, leaving managers unable to answer the questions that mattered most.

The answer wasn't more reports. It was real-time, AI-powered intelligence that anyone in the business could access — instantly, in plain English, without needing a data analyst.

Labour analytics dashboard with KPI tiles, a productivity bar chart and an Amazon Q natural-language query panel
The conversational analytics platform — managers ask workforce questions in plain English through Amazon Q and monitor live labour KPIs in QuickSight dashboards.
The challenge

Workforce decisions were being made without the data to support them — and the existing reporting stack couldn't close the gap:

  • Labour analytics relied entirely on manual reporting and fragmented, disconnected data sources.
  • Managers couldn't see which time periods were genuinely productive versus loss-making.
  • Overstaffing and understaffing went undetected across outlets until after the fact.
  • There was no visibility into how labour cost was impacting outlet-level performance.
What Algorims built

A Conversational Analytics Platform on AWS — a fully serverless, cloud-native intelligence system that transforms raw operational data into actionable insights through natural language, engineered across six layers.

01

Data ingestion & storage

Raw labour and transaction data ingested and structured in Amazon S3 — creating a single, reliable source of truth across all outlets.

02

Data engineering pipeline

AWS Glue crawlers and ETL processes clean, transform, and optimise raw data for high-speed analytical querying at enterprise scale.

03

Analytics layer

Amazon Athena computes critical business KPIs — TCPMH (transactions per crew man-hour), SPMH (sales per man-hour), and labour cost % — all validated against real business logic.

04

AI-powered natural language interface

Amazon Q lets business users ask workforce questions in plain English and instantly receive AI-generated charts, insights, and contextual follow-up recommendations. No SQL, no analysts, no waiting.

05

Real-time dashboards

AWS QuickSight delivers interactive, real-time visualisations — KPI monitoring across time periods, zones, and operational areas — giving every manager complete visibility at a glance.

06

Enterprise security & governance

Full encryption, IAM role-based access control, and comprehensive audit logging — built in from day one, not retrofitted after launch.

Solution architecture

Raw labour and transaction data flows from source systems into Amazon S3, is catalogued and transformed by AWS Glue, queried through Amazon Athena, and surfaced to business users through Amazon Q natural-language queries and QuickSight dashboards.

QSR conversational analytics architecture on AWS — S3, Glue, Athena, Amazon Q and QuickSight
Technology stack
Amazon S3 AWS Glue Amazon Athena Amazon Q AWS QuickSight AWS IAM AWS KMS
Results & benefits
Instant decision-making — real-time workforce insights through natural language; decisions that took days now happen in seconds.
490K+ transactions across 13 months of operational data processed with complete visibility.
A previously invisible 46% productivity gap between time periods uncovered — enabling targeted optimisation.
4 weeks from architecture to production — faster than any traditional analytics programme.
Managers now make proactive staffing decisions driven by AI, controlling labour cost across every outlet.
Data access democratised — non-technical users interrogate operational data freely, no SQL required.
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