Case studies

Client work, documented as engineering.

Quorlytic client case studies: the situation, the architecture delivered and what it enabled, published with client approval and without invented figures.

Published case studies

Illustration: operational sources flowing through ingestion and ETL into a curated data platform, feeding reports and a demand forecast.

Manufacturing
Data Engineering
Analytics
Machine Learning
AWS

An AWS data platform for manufacturing demand intelligence

Quorlytic built an end-to-end data platform on AWS for a manufacturing enterprise. Automated ingestion and ETL brought ERP, sales and order, production, inventory and spreadsheet data into a curated Amazon Redshift layer; Power BI reporting and a Python demand-forecasting capability were then built on that single, trusted foundation.

A single, curated data foundation in place of manually consolidated sources.
Repeatable pipelines in place of ad-hoc data preparation.
Business reporting built on one trusted analytical layer.
A forward-looking demand signal alongside historical reporting, to support planning.

Amazon S3AWS GlueAWS LambdaAmazon RedshiftPower BIPythonRead the case study

Related insights

Guides behind the work.

All engineering guides

Illustration: sources flowing through three processing stages into a destination store.

Engineering guideData Architecture

What Is a Data Pipeline?

What a data pipeline is, how batch, streaming and change data capture differ, and what production pipelines must guarantee to be trusted.

Sep 19, 20268 min read

Illustration: staging, core and mart layers stacked in a warehouse, serving downstream reports.

Engineering guidePlatform Engineering

Data Warehouse Architecture

Warehouse layers, grain, marts and semantic models: how to design a warehouse that answers the same question the same way every time.

Sep 19, 20269 min read

Illustration: a noisy signal becoming a forecast with a confidence band, leading to a decision.

Analytics guideAnalytics & BI

Predictive Analytics: From Signal to Action

How predictive analytics becomes a decision: model families, label quality, evaluation, intervention design and monitoring in production.

Sep 19, 20269 min read

Illustration: forecast errors counted three ways: equally, weighted by volume, and squared.

ComparisonData Architecture

MAPE vs WAPE vs RMSE

Which forecast accuracy metric to use and when: how MAPE, WAPE, RMSE and MAE behave on low-volume items, outliers and portfolio-level reporting.

Sep 20, 202612 min read

How we publish

Every study meets the same standard.

A study is published only with the client's written approval, and only when each field below can be answered without exaggeration. Where a client has not approved quoting figures, outcomes are described as capabilities, never as invented statistics.

SituationWhat the organisation was trying to decide, and what made it hard at the time.
ConstraintsRegulatory, timing, platform, budget or team limits that shaped the design.
ArchitectureWhat was built, the boundaries chosen and why the alternatives were rejected.
ImplementationThe sequence actually followed, including what changed during delivery.
Measurement methodHow the result was measured, by whom, over what period, and what was not measured.
OutcomeWhat changed, stated without invented statistics and without claiming credit for the client’s work.
LimitationsWhat remains open, unproven or dependent on the client’s operating model.
Technology stackThe platforms and tools used, as names only; no partnership or certification is implied.
Related servicesLinks to the Quorlytic capabilities that delivered and now operate the work.

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Discuss your data project Read the engineering guides