Client identity withheld under signed NDA.
"Every time we scaled, I kept waiting for the costs to catch up. They never did. That level of scalability, without the cost growing with it, is honestly insane."
The agency was already using Claude (the standard desktop app, not a custom integration) across their teams to handle outbound tasks: LinkedIn messages, connection requests, comment engagement, email sequencing. It worked as a starting point. But as they pushed toward scale, three problems compounded fast.
They needed to replace a patchwork of human judgment and paid AI tools with something engineered, cost-controlled, and self-sustaining.
The first step was auditing every outbound action across all four teams and separating what actually required intelligence from what was repeatable and rule-based. The finding: over 80% of outbound actions are deterministic. Only a small subset (ICP qualification, negotiation, personalised writing) need frontier-level reasoning.
We deployed a local AI orchestrator on the client's own machine: their infrastructure, their control, near-zero marginal cost per inference. The architecture works like this:
The system was integrated across all four teams. LinkedIn and email sequences now run in coordinated cadence: if a prospect is warm on LinkedIn, email timing adjusts automatically. When a lead crosses a warmth threshold (replies, engagement signals), a call task is triggered for the sales rep. One operator initiates the process. The AI runs it.
The biggest cost mistake in AI outbound is defaulting every action to the most capable model. Most outbound actions don't need frontier intelligence; they need consistency, speed, and correct execution. Route expensive AI only to the moments that move the needle: personalisation, qualification, negotiation. Run everything else on a local model and code. The result is a system that is simultaneously smarter, faster, and a fraction of the cost.