Introducing TagQuest: Expert-in-the-Loop Metadata Review for Your DAM
- Metadata cleanup fails because no single person knows every asset, not because the tools are missing.
- TagQuest spreads review across invited experts in tasks that take seconds.
- Peer consensus and recognition keep quality high and people engaged.
Why do metadata cleanup projects fail?
Most metadata cleanup projects fail for a simple reason: nobody wants the work. Adding, checking and validating metadata is repetitive, and very few people enjoy it.
There is a second, less obvious reason. One person can rarely know every answer about every asset in the DAM. The archivist knows the history, the product manager knows the SKU, the brand team knows the campaign. When review sits with a single administrator, the gaps stay.
TagQuest exists because this problem is solvable. Not with more automation, but with better process design.
What is expert-in-the-loop metadata review?
Expert in the loop (EITL) is a review model in which the people who know the content best (brand managers, product specialists, archivists, rights owners) confirm, correct or reject metadata, including AI-generated tags, and their decisions are checked by peer consensus before they become the permanent record.
It builds on human-in-the-loop review, with one difference that matters: the reviewer is chosen for what they know, and no single reviewer has the final word. Read more about expert in the loop.
How does TagQuest work?
TagQuest connects to your existing DAM and turns review into short Quests. Invited contributors see one asset at a time and make one decision. Each task takes seconds and adds to a team effort that recognises the experts in the loop for their knowledge and their contribution.
| Quest | What the expert does | DAM problem it solves |
|---|---|---|
| Keyword Quest | Picks or adds keywords from your approved vocabulary | Missing or inconsistent descriptive metadata |
| Yes or No Quest | Confirms or rejects a tag, caption or statement | Unverified AI-generated tags |
| Star Quest | Rates quality and relevance | Deciding what to promote, update or retire |
| People Quest | Names who is in the image | Identifying people without biometric face recognition |
Identifying people is handled by colleagues who know them, not by facial-recognition software. That keeps biometric data out of the process entirely.
Why does the expert matter more than the tool?
The hardest part of making a DAM work is creating great metadata. AI can produce a first draft quickly, but only people with context can say whether “spring campaign”, “2024-Q2” or “stuff” is the right answer.
TagQuest gives your library consistent, sustained attention from those people. Metadata review becomes an organised activity your team does in the margins of the working week. It is not a separate project competing for headcount. It is a shared activity that strengthens teams and shows everyone the breadth of content they already own.
Who is TagQuest for?
DAM administrators
Who need a backlog cleared without chasing colleagues for answers.
DAM providers
Whose customers renew when they can find their assets.
Brand, product and archive teams
Whose knowledge is the missing ingredient in good metadata.
Where to start
If your library has thirty years of accumulated gaps, you do not need a bigger team. You need a better way to use the team you already have.
Start with one collection and one Quest. Within a week you will see which assets your experts agree on, and which ones nobody could have tagged alone.
Frequently asked questions
What is expert in the loop?
Expert in the loop is a review model in which the people who know the content best confirm, correct or reject metadata, including AI-generated tags, and peer consensus decides what becomes the permanent record.
How is TagQuest different from AI auto-tagging?
AI auto-tagging creates suggestions. TagQuest is the review layer on top: invited experts confirm or correct those suggestions in short tasks, so the tags that go live are ones your team agrees on.
Which DAM systems does TagQuest connect to?
TagQuest is DAM agnostic and connects securely to ResourceSpace today. Contact the team about other DAM platforms.
Does TagQuest use facial recognition?
No. People in images are identified by colleagues who know them, so no biometric data is processed.
See expert-in-the-loop review on your own assets
A 15-minute walkthrough with real Quests on your content.
Book a 15-minute demo