Published 14 Jun 2026· via Microsoft· Customer story
NIQ built Capture as a Service (CaaS) using Microsoft Foundry to automate product item coding
NielsenIQ · Retail · United States
−90%
item coding time
As reported by the source; not independently verified.
Summary
NielsenIQ (NIQ) built Capture as a Service (CaaS) to automate item coding from packaging images and scale product data extraction across markets.
The solution uses Microsoft Foundry services to extract, verify, and structure packaging information faster and more accurately than a manual workflow.
The problem
- 01Manual item coding from packaging images took about four minutes per item and limited NIQ's ability to scale to thousands of products daily.
- 02NIQ needed to accelerate product data collection and reduce the operational cost of packaging-based item coding across many markets.
What they built
- 01NIQ designed and released CaaS as a generative AI-powered workflow that simulates the manual coding process with AI experts.
- 02The implementation uses Azure OpenAI in Foundry Tools, Azure Document Intelligence in Foundry Tools, Azure AI Search, and prompt flow orchestration to extract, interpret, validate, and structure packaging data.
- 03The system was built as an MVP with Microsoft Foundry and later became the foundation for NIQ Product Insights.
Reported results
- 01NIQ cut item coding time by 90%.
- 02NIQ launched product insights in 25 new markets.
- 03NIQ processed 32,000 products in 10 hours instead of 300 hours.
- 04NIQ now processes tens of thousands of products in hours instead of weeks.
- Launched product insights in 25 new markets
- Turned hours of work into an hours-based automated pipeline
- Created a scalable product data coding service
Source
How this deployment compares
Computed from the library · data 10 Nov 2026This is the only NielsenIQ deployment in the library. Its reported 90% reduction is above the typical range (median 50% across 10 figures).
NielsenIQ in the library
The only NielsenIQ deployment in the library
Results against peers
90% reduction is above the typical range
Across 10 time & speed figures (Retail), the median reduction is 50%; the middle half falls between 24% and 63%.
This recordMiddle half of peers
- Revenue & growth23 figures, Retail · middle half 5%–64%23%median increase
- Cost savings11 figures, Retail · middle half 29%–68%43%median reduction
- Adoption & scale8 figures, all industries · middle half 134%–1100%250%median increase
Peer figures are as reported by their sources; percentage claims only.
How we checked this record
Evidence tier: supported
A credible source with a named customer, but not the organization's own account.
- Named organization
- NielsenIQ
- Who published the source
- The organization itself
- Deployment status
- Production (confidence 95%)
- Quantified result
- The source reports at least one figure
- Summary checked against source
- 5 / 5 agreement
Last re-checked 26 Jul 2026.
This record is evidence for
Same pattern, other organizations
All quality management deployments- 23 Jul 2026AutomotiveJapan
Toyota Industries paint shop quality improved with Azure-based industrial AI data foundation
Toyota Industries Corporation · Quality management · Microsoft
Toyota Industries Corporation aimed to improve paint quality across high-volume automotive plants as fragmented factory data limited root-cause analysis and slowed decisions. The company partnered with Microsoft and Sight Machine to build an Azure-based semantic layer with unified factory data from Azure IoT Hub for near real-time analysis and visibility into paint defect drivers. The deployed foundation was used to accelerate defect analysis, improve shared operational visibility, and support more scalable quality decisions on the shop floor.
Production
−80%daily standup preparation time - 25 Sept 2025ManufacturingSouth Korea
LG Electronics HS division deploys CHATDA agentic AI platform with Azure OpenAI for product quality and R&D
LG Electronics · Quality management · Microsoft
LG Electronics' Home Appliance Solution (HS) division developed CHATDA, an enterprise-scale big-data analysis AI platform powered by Microsoft Azure and Azure OpenAI. CHATDA analyzes data collected from tens of millions of connected devices worldwide and extends access to home-appliance R&D knowledge through unstructured documents.
Agentic
- PharmaUnited States
Catalent deploys Qai agentic AI to accelerate pharmaceutical quality investigations
Catalent · Quality management · Microsoft
Catalent built Qai, a bespoke agentic-AI solution on Microsoft Azure for deviation management, root-cause analysis, event classification, investigation drafting, and CAPA recommendations. The solution uses enterprise quality data with read-only access and documented human validation in a regulated manufacturing environment.