Podbit · The MongoDB Podcast
Most enterprise AI projects don't fail because the model is bad. They fail because the data is messy, the people aren't bought in, and nobody can prove the ROI. Fix those three things first.
Where this was said
At 30:10 · chapter starts 28:40
Shane raises the concern that stops many enterprise AI conversations in their tracks: are third-party AI vendors secretly training their models on sensitive customer data? Akshaya's answer is unambiguous — no — and he explains the three-layered protection that justifies that confidence. At the contractual level, every AI model vendor Zendesk works with is bound by zero-retention and zero-training clauses, making it legally prohibited to store or learn from Zendesk's data. At the product level, Zendesk's reputation as one of the most secure CX platforms on the market means security is culturally embedded, not an afterthought. And at the infrastructure level, enterprise AI APIs ship with role-based access controls, encryption in transit and at rest, as a baseline. Internally, Zendesk follows the NIST playbook to ensure no customer data is compromised. His takeaway for any company building an AI product: security and privacy must be first-principles decisions, not bolt-ons, because customer trust is the asset you can least afford to lose.
Sam had no coding knowledge, so he used ChatGPT voice mode to generate his entire codebase and copy-pasted it into Notepad. A friend later introduced him to Cursor, and he never looked back.
Copy days of Discord chat history, paste it into ChatGPT, and ask it to list recurring pain points. The ones that come up most often are your best product bets.
Sam's top advice: when prompting Cursor, tell it to architect code for 100,000 users from day one. The AI changes its approach, building scalable frameworks instead of brittle one-user code.
With AI coding tools like Cursor, Bhanu replicates an existing free tool for a new keyword in under 5 minutes. What used to be a multi-day build is now a lunch-break task.
Ahrefs, SiteGPT, Cal.com, PostHog, Datafast, Sibyl AI, Bento, Feather, Featurepace, Mintlify, Cloud Code, ChartMogul — Bhanu runs his entire business solo with these 12 tools.
PropGPT runs on React Native with TypeScript and Python for ML, Neon for the database, RevenueCat and Superwall for monetization. LLM costs are $20/month, data APIs $100/month, and after $10K in monthly marketing spend, margins sit at roughly 50%.
Game apps keep users engaged long enough for ads to pay off. Tool apps don't — so if you're building a utility, ads are almost always the wrong call and subscriptions are your only real lever.
Inside SEO Bot, a single button labelled 'Boost My Domain Rating' routes users directly to Listing Bot. That one interaction converts a user of one tool into a user of two — without any marketing cost.
Directory listings are a powerful but underrated growth channel — but only if your product is genuinely interesting enough to earn the click. AI products have a natural advantage here because they're easy to package in a compelling, clickable way.
We use essential and analytics cookies to run Vuci. To understand how the site is used: Privacy Policy.
Install Vuci on your phone
Add it to your home screen for a faster, app-like experience.
Install Vuci on your phone
Tap the Share button, then “Add to Home Screen”.
A new version is available
Reload to get the latest Vuci.