StarAether

Solution

Growth and content operations

Ad-operation digital employees analyze campaign data, detect anomalies, generate strategy, and assist authorized adjustments.

Status: Demo validatedWorkflow diagnosis · System integration · Permission governance
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  1. 1

    Strategy start

  2. 2

    Anomaly detection

  3. 3

    Smart control

  4. 4

    Approval

  5. 5

    Data review

  6. 6

    Learning

Business problem

Growth teams must continuously read data, detect anomalies, adjust budgets, and tune content strategy, while human response speed is limited.

Execution model

The system monitors ROI, GPM, conversion, and refund rate, generates control suggestions, executes high-impact actions after approval, and feeds reviews into a strategy library.

Measurable metrics

Response latency / Anomaly detection / ROI / GPM / CTR / CVR / Budget use

Connectable systems

Ad platforms / Live data / Creative library / Dashboards

Best fit: Live-commerce teamsContent-growth teamsEcommerce operators

This solution is based on our digital employee platform. It connects intelligent capability to a concrete workflow through orchestration, tool use, permission control, and continuous feedback.

Solution notes

Solution notes

The following explains fit, integration pattern, and key boundaries so teams can decide where to start.

Solution Goal

The growth and content operations solution serves teams that continuously watch advertising data, content performance, and conversion outcomes. We have implemented an ad-operation digital employee for Douyin ecommerce and Ocean Engine campaign scenarios. Its value is not replacing buyer expertise, but freeing people from 24/7 monitoring: the system watches anomalies and humans approve key actions.

Automatable Campaign Steps

  • Strategy start: users start a strategy in Lark, and the system collects campaign and creative data.
  • Anomaly detection: detects traffic drops, ROI decline, creative fatigue, and abnormal spend.
  • Smart control: generates budget, bid, and campaign-status adjustment suggestions based on goals.
  • Action approval: high-impact actions go through Lark approval before execution.
  • Data review: outputs live-session reviews and converts GMV, orders, ROI, and refund rate into action suggestions.
  • Self-learning: each execution result feeds back into the strategy library, making strategy more business-specific over time.

Business Value

An experienced media buyer can cost around RMB 20,000 per month and still needs to monitor, judge anomalies, adjust budgets, and review outcomes continuously. The digital employee can save at least one buyer's workload while providing 24/7 fatigue-free monitoring.

Control Boundaries

Budget changes, account operations, and high-risk strategies must stay within explicit permission boundaries. Unauthorized actions are not executed automatically, and failures are not packaged as success. Strategy suggestions retain evidence and context for tracing and review.