Podbit · The a16z Show
Kavak's AI sales agents now convert customers at 2.1x the rate of its human team, and also tripled NPS. The agents handle everything from car recommendations to financing to trade-in quotes — a 15-skill job that no single human could match.
Where this was said
At 11:00 · chapter starts 8:10
Gabriel asks how Kavak evaluates whether agents are working at the scale of 96–98% of all interactions. Alejandro's answer reframes the question entirely: evals are not a safety tax, they are the accelerator. His analogy is elegant — you'll only floor the gas if you have good brakes. Most companies go slow on AI deployment because they lack rigorous evaluation, not because the models are weak. Kavak inverts this by spending roughly equal engineering time, tokens, and money on building evals as on building agents themselves [1] — Alejandro Maza Ayala "Equal eng effort on evals vs agents: Kavak spends roughly equal engineering time, tokens, and money on building evals as it does on buildin…" 09:05 . The measurement focus is ruthlessly commercial: did the customer convert? Did they come back? Not vanity metrics like call duration or number of interactions. This discipline, Alejandro argues, is what separates genuine agentic deployment from theatre.
96% of all customer interactions and 95% of all transactions at Kavak are now handled by AI agents. Between 100,000 and 200,000 agent instances wake up every day, each with its own virtual machine, working anywhere from 3 minutes to 3 days before setting an alarm for their next task.
Kavak's AI agents handle 96% of all customer interactions with no human involvement.
95% of all Kavak transactions are completed end-to-end by AI agents, not humans.
Between 100,000 and 200,000 unique agent instances are spun up at Kavak every single day, each with its own virtual machine.
Most companies treat evals as an afterthought. Kavak treats them as the foundation. Spending equal engineering effort on evals and agents is what allows Kavak to deploy at massive scale without blowing up — because better brakes mean you can press the gas harder.
Kavak spends roughly equal engineering time, tokens, and money on building evals as it does on building agents themselves.
SiteGPT attracted over 1 million visitors and $500K in total revenue without spending a cent on paid marketing. The secret: engineering as marketing — building free tools that rank on Google.
Bhanu quit his first job after just 8 months, moved back to his parents' house to cut costs, and started building. One product sold for $250K; the next hit $10K MRR in its first month.
90% of SiteGPT's Google search traffic comes not from the main product but from ~50 free tools Bhanu built. Each tool targets a low-competition keyword and funnels users back to the paid product.
50,000 monthly visitors become 200 leads, 60 trials, and roughly 15–24 new customers per month at ~$100 average revenue each. Add a $1,700–$1,800 LTV and you have a very healthy SaaS.
Start with a blank Ahrefs search, layer in keyword filters (include term, KD < 10, volume > 1,000), list candidates in Notion, design a CTA linking to your main product, then score by volume, difficulty, build effort, and product relevance. That's the whole playbook.
Marketing feels painful for most builders. Engineering as marketing flips the script: instead of writing cold emails or blog posts, you build things — and those things rank on Google forever.
Don't spend months perfecting before launch. Ship the core feature, get real users, and let their feedback dictate the product roadmap. Premature polish is a trap.
SiteGPT launched and hit $10,000 MRR within its first month. That momentum was so overwhelming that Bhanu sold his existing SaaS, Feather, for $250,000 to free up all his time.
PropGPT launched with 20 downloads a day and strong influencer marketing but hit a ceiling at $1,000–$2,000 MRR. High download numbers masked a critical flaw: almost nobody stuck around after the free trial ended.
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