Most firms bolt AI onto existing processes. Kavak's success came from a radical question: What would our company look like if founded today with AI? This prompted a complete organizational redesign, not just superficial adoption, leading to a 10x improvement instead of a 10% one.
Kavak abandoned the common multi-agent workflow model. They now instantiate a unique, long-running agent for each customer, tasked with maximizing that person's lifetime value. This agent maintains memory of all interactions and plans long-term, shifting the company from transactional to relational.
To innovate quickly and safely with AI, Kavak treats evaluations as a first-class citizen. They invest equal engineering time and money into building robust evals as they do into the agents themselves. These "brakes" enable them to accelerate development while ensuring quality and business alignment, focusing on conversion, not vanity metrics.
Kavak rethinks "human-in-the-loop." When an AI agent gets stuck, it doesn't just escalate the task to a human queue. Instead, the agent makes an API call to a human for help, retains ownership of the problem, and learns from the interaction. This closes feedback loops and makes human teams a resource for the primary agent.
To enable its AI transformation, Kavak created a mandatory internal training program. The "JEDI Academy" taught all employees—not just engineers—how to build and launch production-ready AI agents in six weeks. This company-wide upskilling created a culture where everyone understood how to collaborate with AI.
To test AI's strategic capabilities, Kavak assigned an agent to be the "CEO" of one of its city operations. The agent handled forecasting, micromanaged daily execution plans for human workers, and optimized all business KPIs. It increased the city's profits by 50% in its first month, proving AI's value in core leadership roles.
Kavak avoids measuring AI adoption by token consumption. Instead, they use a three-tier framework to evaluate token quality. Tier 3 (most valuable) are tokens with direct, measurable ROI, like those in sales agents. This brings financial discipline and focuses investment on high-impact AI applications, rather than unmonitored usage.