AI-native media trafficking infrastructure for a creator campaign marketplace

Client Snapshot

Seismic

Seismic operates a high-volume creator marketing marketplace across YouTube, TikTok, Instagram, and podcast platforms. At the scale the business had reached, the operational constraint was not revenue or creator supply. It was infrastructure. Campaign management teams were spending the majority of their bandwidth on logistics - distributing brand briefs, confirming flight dates, chasing product shipment details, reviewing ad integrations - work that had to happen for every campaign across thousands of creator relationships, but that added zero strategic value to the client relationship.

The diagnosis was structural: post-sale campaign execution had been built as a manual coordination function, and the team had scaled headcount to absorb volume rather than redesigning the system to handle it. The result was a campaign management team that was technically revenue-facing but operationally consumed by trafficking work, with no remaining capacity for the renewal and cross-brand expansion activity that actually drove account growth.

The intervention was a full operational redesign anchored by a purpose-built agentic AI system: Seismo.

Seismo was designed as an orchestration layer - not a single AI tool, but a coordinated pipeline of specialized agents operating on structured inputs from the business's existing data environment. The CRM Bot parsed deal terms, IO statuses, and creator event briefs from internal records and converted them into clean structured data that downstream processes could act on without human interpretation. The Brand Brief Converter ingested raw brand requirements and transformed them into creator-ready, platform-optimized briefs calibrated to the specific creator profile and event type. The Ad Integration Review Bot ingested creator ad read submissions against brand requirements, scored each submission across defined compliance dimensions, and returned structured feedback to creators in real time - eliminating the manual review backlog that had been consuming campaign management hours before each launch.

System inputs: audio transcripts via Fireflies API, CRM and operational data via Google Workspace, Slack communications, and YouTube metadata. System outputs: Brand Briefs, Creator Event Briefs, Ad Review Submissions, and Ad Integration Reports - all structured, timestamped, and routable without human intervention in the standard case.

Alongside the AI infrastructure, we redesigned the team structure. Creator-side logistics were transferred to a centralized Traffic function. Campaign management was reoriented toward renewals, cross-brand strategy, and post-campaign analysis. Creator accounts were tiered by revenue LTV, and time allocation was matched to tier.

The outcome was a campaign management function that operated at more than three times its previous revenue efficiency - the same team, a redesigned system, and a fundamentally different relationship between headcount and throughput.

Executive Summary

The operational problem at Seismic was not that the team was underperforming. It was that the team was performing the wrong work. Campaign managers who should have been owning renewal conversations and multi-brand expansion strategy were instead coordinating logistics that had no business being done by humans at that stage of the company's development.

The AI system we designed was not built to replace judgment. It was built to systematically remove the decisions that did not require judgment - the brief formatting, the compliance scoring, the status tracking, the exception flagging - so that the decisions that did require judgment could receive the attention they deserved.

The three-times efficiency improvement in CM-to-revenue ratio was not a productivity gain in the conventional sense. It was a structural reallocation: the same people, freed from low-leverage work, operating in the part of the business where their relationships and experience actually created value.

That is the pattern we look for in every AI implementation. Not what can be automated. What should never have required a human to begin with.