Editorial platform automation for a US publishing company
An AI-assisted classification and review workflow built into an existing CMS, cutting manual editorial routing and freeing editors for higher-value work.
Client context
An NDA-protected US publishing company running a high-volume editorial operation on an established CMS, with editors spending significant time routing and tagging incoming content.
Challenge
Manual content routing and tagging slowed publishing and pulled editors away from editorial judgement. Any automation had to fit the existing CMS and keep a human in the loop.
What we did
- Designed an AI-assisted classification and routing workflow integrated into the existing CMS.
- Added a human review queue for low-confidence items so editors stay in control.
- Built reporting so the team can see throughput and accuracy over time.
Process
- AI discovery to frame the use case, data and success criteria.
- Prototype against real editorial content and measure accuracy.
- Integrate into the CMS with a human review queue and reporting.
- Harden, document and roll out with monitoring.
Result and impact
Editors spend less time on manual routing and more on editorial work, with a review queue keeping quality under control.
Metrics are illustrative until confirmed with the client.
This is an NDA-protected engagement. Client name and identifying details are withheld; industry, region, challenge, solution and outcome are shared in an approved form.
It feels like the workflow finally works with us, not against us.Editorial operations lead (quote representative, under NDA)