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How One Media Company Lifted Ad Sales 8%
An ad sales team layered LLM agents over its BigQuery data — recommending audience segments mid-RFP and automating trafficking and QA, lifting deal value 8%.
+8%
Gained in incremental deal value
2–4 months
Implementation Time
Not disclosed
Project Cost
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 — with no path to modernization that didn’t require a years-long system overhaul.
what they built
3C Ventures began with a structured diagnostic of the full pitch-to-pay process, mapping every step, tool, and handoff to identify where value was being lost. Rather than replacing existing systems, the team built purpose-built AI-native internal tools with clean UIs designed to sit on top of the client’s federated data stack, using BigQuery and internal APIs as the backbone. Bespoke LLM-powered modular agents drove three core capabilities: intelligent audience segment recommendations during RFP responses, anomaly detection across campaign delivery signals, and automated trafficking, QA, and summary generation. The unexpected outcome: embedding engineering in the diagnostic phase from day one allowed the team to demo a working AI product before the data backend was fully cleaned — generating executive buy-in earlier than anticipated.
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 — the OMS, CRM, ad server, and analytics platform — which shared only loose primary keys. With a clean data backbone in place, the team built three bespoke LLM-powered agents: one that generates intelligent audience segment recommendations during RFP responses, one that detects delivery anomalies across campaign signals, and one that automates trafficking, QA, and summary generation. All three were surfaced through purpose-built internal tools with clean user interfaces designed for sales and operations teams. Notably, engineering was embedded in the diagnostic phase from day one — allowing the team to demo a working AI product before the data backend was fully cleaned, securing executive buy-in earlier than expected.
best fit for
Enterprise and growth-stage media owners, ad tech platforms, and publishing companies whose ad sales and campaign operations are weighted down by manual workflows and fragmented data, and who need to demonstrate AI value quickly without committing to a multi-year system overhaul.
Ai ROLE
Bespoke LLM-powered modular agents perform three distinct functions: generating intelligent audience segment recommendations during RFP responses by analysing available data; running anomaly detection across campaign delivery signals to surface problems before they escalate; and automating trafficking, QA review, and campaign summary generation in post-sale operations. The agents operate on top of the client's existing data infrastructure without replacing underlying systems.
impact
+8% Incremental Deal Value
AI-powered audience segment recommendations during the RFP process gave sales teams faster, more informed pitches — increasing close rates and surfacing upsell opportunities previously buried in unstructured documents and disconnected systems.
~30% Post-Sale Operations Time Savings
Automated trafficking, QA, and campaign summary generation removed manual grunt work from post-sale workflows, freeing operations teams for higher-value activities and increasing throughput without adding headcount.
~25% Reduction in Campaign Issues
Modular anomaly-detection agents flagged delivery problems across federated campaign signals early, shifting the team from reactive firefighting to proactive campaign management and reducing client-facing errors significantly.
implementation complexity
The implementation required significant custom engineering: building bespoke LLM-powered agents, constructing a federated data layer on top of BigQuery and multiple internal APIs, and integrating with an existing multi-system stack (OMS, CRM, ad server, analytics). Embedding engineering in the diagnostic phase from day one added complexity but accelerated executive buy-in.
Alex Brownstein
Partner at 3C Ventures, advising Fortune 500s, startups, and investors at the intersection of media, advertising, AI, and technology
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Talk to this teamindustry
Media & Entertainment
business organization
Sales & Revenue
Operations
Marketing
AI TYpe
AI-Accelerated Custom Software
Process Automation (RPA + AI)
Recommendation Systems
value type
Revenue Growth
Time Savings
Cost Reduction
frequently asked questions
How did a large media and advertising company lift deal value 8% with AI agents over its data?
The experts built a federated data layer in BigQuery to unify four disconnected systems, then layered three bespoke LLM agents on top — one recommending audience segments during RFP responses, one detecting delivery anomalies, and one automating trafficking, QA, and summaries. The audience-recommendation agent lifted incremental deal value by about 8%.
What AI tools and models were used in this ad sales project?
The build used custom, bespoke LLM-powered agents on a federated data layer constructed with BigQuery and internal APIs, surfaced through purpose-built internal tools. The approach combined AI-accelerated custom software, process automation, and recommendation systems.
What results did the media company achieve?
Three outcomes: about 30% time savings in post-sale operations from automated trafficking, QA, and summaries; roughly 25% fewer campaign issues from early anomaly detection; and an 8% increase in incremental deal value from AI audience-segment recommendations during RFPs.
How long did the AI ad sales project take?
About four to six months. Embedding engineering in the diagnostic phase from day one let the team demo a working AI product before the data backend was fully cleaned, securing executive buy-in earlier than expected.
Who is this AI ad sales approach best for?
Enterprise and growth-stage media owners, ad tech platforms, and publishers whose ad sales and campaign operations are weighed down by manual workflows and fragmented data, and who need to show AI value quickly without a multi-year system overhaul.
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