Yield-optimisation agent crews with policy-bound spend control. Production AI for SSPs, DSPs, and ad operations — with audit trails per campaign change.
AdTech publishers use LeafMesh ADK to orchestrate AI agent crews for real-time yield optimization across thousands of placements. One agent analyzes performance, another proposes changes, a policy engine applies spend rules, and humans approve anything above threshold. The result: 22% yield uplift with full audit trails per campaign change.
Pattern
Analyzer → optimizer → approver → executor crew
Outcome
22% yield uplift, full audit trail per change
Pattern
Budget watcher + policy engine + auto-pause + human review
Outcome
Zero overspend incidents post-deployment
Pattern
Multi-source agent crew with shared memory + escalation
Outcome
Faster fraud detection, fewer false positives
LeafMesh's runtime is built for sub-second agent decisions. For real-time bidding scenarios, latency-sensitive agents run in-memory with capability-based LLM routing — small fast models for routine decisions, large models for novel situations.
Yes. LeafMesh's shared memory means agents on the SSP side share state with agents on the DSP side. Cross-agent policy prevents conflicts (pricing and inventory making opposite decisions on the same placement).
You define spend policies in YAML — caps per campaign, thresholds for human approval, anomaly thresholds. LeafMesh's policy engine evaluates every spend-changing decision against the policy. Breaches are automatically escalated.
Talk to our team about your adtech use cases — or try the platform yourself.
We use cookies to enhance your browsing experience, analyze site traffic, and provide personalized content. By clicking "Accept All", you consent to our use of cookies.