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General Motors AssetIQ Content Agents

Content operations for more than 16,000 users across multiple agencies, with 90% of metadata creation automated.

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Five agent types plus an orchestration layer run content operations for more than 16,000 users across multiple agencies, with 90% of metadata creation automated.

What the agents do

Plans, catalogs, produces, critiques, and checks content for compliance, writing metadata a downstream check validates against more than 130 regulatory fields.

Built by
General Motors, on the Aprimo platform
Contributed by
Aprimo (Multi-agent content operations platform)
Industry
Automotive, Manufacturing & Industrial
Audience
B2C
Category
Content & ExperienceTrust, Safety & GovernanceIntegration & Orchestration
01
The problem

Why this exists

Content operations at automotive scale is a metadata problem wearing a creative costume. An asset that is not described cannot be found, cannot be reused, and cannot be checked for compliance, and describing assets by hand is the work everyone defers.

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Regulatory validation compounds it. An automotive marketing asset has to be checked against a long list of regulatory fields, and a reviewer cannot do that against an asset whose metadata is missing or wrong. With multiple agencies producing work, the inconsistency is structural rather than occasional.

02
Outcomes

What changes when this is in place

  • More than 16,000 users across multiple agencies work in the environment.
  • 90% of metadata creation is automated.
  • Compliance validation is 70% faster and checks more than 130 regulatory fields.
03
How it works

Architecture

AssetIQ coordinates five agent types plus an orchestration layer. Planning agents produce campaign and content briefs. Librarian agents classify and enrich assets. Production agents generate variants, renditions, and localizations. Critic agents review quality, tone, and clarity. Compliance agents check brand, legal, and regulatory rules. The orchestration layer coordinates them.

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The published example shows why the coordination matters more than any single agent. Librarian agents create structured metadata, and that metadata becomes immediately available to compliance agents validating against more than 130 regulatory fields and to production agents generating reuse. The output of one agent is the input another was waiting for, which is the difference between five tools and one system.

GM's implementation is publicly identified as AssetIQ built on Aprimo's multi-agent platform, covering Aprimo's digital asset management and content operations agents plus orchestration. No public material identifies AssetIQ's foundation models, cloud vendors, GM source systems, integration middleware, or the exact business unit covered, so none of that is asserted here.

The AssetIQ orchestration layer, on Aprimo's multi-agent platform, coordinates five agent types: planning agents for campaign and content briefs, librarian agents that classify and enrich assets, production agents for variants, renditions, and localizations, critic agents for quality, tone, and clarity, and compliance agents for brand, legal, and regulatory rules. Structured metadata from the librarian agents feeds the compliance agents, which validate against more than 130 regulatory fields, and the production agents.

The AssetIQ orchestration layer, on Aprimo's multi-agent platform, coordinates five agent types: planning agents for campaign and content briefs, librarian agents that classify and enrich assets, production agents for variants, renditions, and localizations, critic agents for quality, tone, and clarity, and compliance agents for brand, legal, and regulatory rules. Structured metadata from the librarian agents feeds the compliance agents, which validate against more than 130 regulatory fields, and the production agents.
Open standards used
REST APIs
Tech stack
Aprimo multi-agent platformAprimo digital asset managementLibrarian agentsCompliance agents
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Resources

Published case study
Next step

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Everyone credited on this solution

  • AprimoMulti-agent content operations platform

Every organization named here has a page listing the rest of its work on the Hub.

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Verification

Published case study

The detail on this page is drawn from a case study published by the contributor or the MACH Alliance. Any figures on this page are the ones that case study states.

What this page does not establish

  • No public dollar figure, revenue lift, content-volume baseline, or measurement period has been published for the improvements described here.

Where this came from

Published from the winner's case study and the MACH Alliance announcement of the 2026 Impact Awards, whose Agentic Achievement Award was presented at MACH X: Toronto.

Status: Live in productionAdded Apr 29, 2026A listing is an editorial decision, not a MACH Alliance certification.
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