The Challenge
A major media company's ad sales and campaign delivery operations were trapped in legacy, manual workflows. Valuable data existed across the organization but sat fragmented across disconnected systems — an order management system, CRM, ad server, and analytics platform — linked only by loose primary keys. Sales teams missed upsell opportunities because relevant context was buried in documents and siloed tools. Campaign delivery ran reactively with high manual load across trafficking, QA, and reporting. Inaction meant continued revenue leakage and operational drag at scale.
What They Built
3C Ventures built bespoke LLM-powered modular agents layered on a federated BigQuery data backbone, delivering intelligent audience segment recommendations during RFP responses, automated campaign trafficking and QA, and anomaly detection across delivery signals.
3C Ventures began with a structured diagnostic of the full pitch-to-pay process, interviewing stakeholders across sales, trafficking, and analytics to map every step, tool, and handoff. The most critical first move was constructing a federated data layer using BigQuery and internal APIs to unify four previously disconnected systems. With a clean data backbone in place, the team built three bespoke LLM-powered agents: one for RFP audience recommendations, one for anomaly detection, and one for automated trafficking and reporting.
AI Role
Automated trafficking, QA, and campaign summary generation removed manual grunt work from post-sale workflows, freeing operations teams for higher-value activities.
AI Model
Custom / proprietary
Infrastructure
• BigQuery (federated data layer) • Order Management System (OMS) • CRM platform • Ad server • Analytics platform
Integration Points
• OMS + CRM + Ad Server + Analytics → BigQuery • BigQuery → LLM-powered agents • Agents → custom internal UI tools
Enterprise media owners, ad tech platforms, and publishing companies whose ad sales operations are weighted down by manual workflows and fragmented data.
