faster metadata tagging achievable, measured against the client's manual baseline
1,000s
of assets tagged with AI-generated metadata and taxonomy terms
3
downstream systems fed by the tags: Adobe Analytics, CJA, and content supply chain workflows
Fortune 100
pharmaceutical company behind the result, anonymized by policy
01 · SITUATION
Manual tagging had become the bottleneck.
The company managed thousands of digital assets in Adobe Experience Manager. Every asset needed metadata before teams could find or reuse it, typed by hand.
01
Slow throughput
Manual tagging paced the content operation while new assets waited in the ingest queue.
02
Inconsistent taxonomy
Different taggers chose different terms, so search returned partial results and teams re-made assets they owned.
03
Analytics gaps
Adobe Analytics and Customer Journey Analytics inherited the inconsistency: reports were only as good as the tags.
02 · APPROACH
QuipTag inside AEM Assets, humans on the edge cases.
NextRow deployed QuipTag inside the client's AEM Assets environment, generating metadata and taxonomy tags against the client's controlled vocabulary.
AI-generated metadata
QuipTag writes descriptive metadata and taxonomy tags automatically, at ingest and across the back catalog.
Governed taxonomy
Human reviewers kept the edge cases, so the vocabulary stayed governed while the volume moved to AI.
Wired downstream
The same tags feed Adobe Analytics, CJA, and content supply chain workflows, so one pass serves search, reporting, and planning.
03 · WHAT WAS MEASURED
Up to 80% faster, against the client's own baseline.
Measured against the client's prior manual tagging rate, across thousands of assets.
Tagging throughput
Up to 80% faster across thousands of assets. The backlog cleared at a rate manual tagging could not reach.
Search and discoverability
Consistent taxonomy improved search accuracy, so teams found existing assets before commissioning new ones.
Downstream trust
Adobe Analytics, CJA, and content supply chain workflows now run on consistent tags.
About this case: figures come from NextRow's internal delivery measurement on the engagement described. The client is anonymized by policy; additional detail is available under NDA in an assessment conversation.
GO DEEPER
The product, the platform, the pattern.
This engagement is one of the documented outcomes on the Results page.
QuipTag for AEM
What it does, how it deploys inside AEM Assets, and how it connects to Adobe Analytics and CJA.
Up to 80% faster metadata tagging across thousands of assets at a Fortune 100 pharmaceutical company, measured against the client's prior manual tagging rate during the engagement. Search accuracy and asset discoverability improved alongside the speed gain.
By comparing QuipTag-assisted tagging throughput to the client's own manual baseline on the same AEM Assets library. NextRow reports the result as up to 80% because throughput varies by asset type, and the client is anonymized by policy.
Into AEM Assets first, then downstream. The same tags feed Adobe Analytics, Customer Journey Analytics, and content supply chain workflows, so search, reporting, and production planning all read from one governed taxonomy.
No. QuipTag works in any Adobe Experience Manager Assets environment. The Fortune 100 pharmaceutical engagement proves the pattern under strict regulatory constraints, and NextRow, an Adobe Gold Solution Partner, deploys it across industries.
Put a number on your tagging backlog.
A fixed-scope assessment ends in a scorecard, prioritized use cases with ROI, and a 90-day plan.
