On May 7th, RevGenius hosted Shiran Brodie, Head of Marketing at Softr, and Patrick Ford, Customer Success Manager at Softr, for a session called Build Real GTM Tools With AI.

Quick disclosure before I get into it: the RevGenius job board is built on Softr. I've watched the platform evolve from a no-code site builder into something even stronger and more useful, and the demo Shiran and Patrick ran last week made me realize how much we've been leaving on the table. If you run GTM at a company without a dedicated engineering team, this one's for you.

Vibe coding is having a moment. Generate a prompt, get an app, ship it. The problem shows up the moment that app needs to be shared with a team, connected to mission-critical data, or scaled past a demo. At that point, the generated code starts demanding the one resource most GTM teams don't have on tap: a developer.

Softr's argument, as Patrick framed it, is that the developer dependency is the wrong default. "It's not that you don't need a developer. It's just you've got a team of them at your back already."

The security, the permissioning, the database scaffolding, the auth flows, the mobile responsive rendering are pre-built. The AI layer only kicks in where it adds value. Everywhere else, the platform uses building blocks that have already been pressure-tested by more than a million users. The result is an app that ships ready for real use, not a prototype waiting for engineering review.

The clearest moment of the demo was Shiran building a creative asset approval app live. She entered a prompt, the AI co-builder asked clarifying questions, and within a few minutes the app existed end to end: tables generated, relationships mapped between them, dummy data populated, permissioning configured, theming applied.

The detail that separates Softr from most AI app builders showed up underneath the UI. The database wasn't a placeholder waiting to be replaced later. It was a real, connected, queryable backend with the front end already wired into it.

This is the gap most no-code tools never close. Generate a beautiful UI, then realize the database still needs to be stood up, the auth still needs configuring, the user groups still need setting, and every edge case still needs to be handled manually. Shiran summarized the alternative cleanly: "Anything the AI co-builder can do, I can do visually on my own without having to consume any additional AI credits to do it."

That line is the entire platform philosophy. AI where it saves time, manual control where it matters, no lock-in either way.

The demos got sharper once Shiran and Patrick moved past the basic build flow.

The standout for revenue teams was the Fathom-to-Softr workflow. Call transcripts feed automatically into a Softr database. An AI step extracts pain points, objections, and competitor mentions. The structured output lands in a dashboard the entire revenue team can query, filter, and roll up over time. Not another AI summary lost in a Slack channel. A persistent intelligence layer built on top of every sales conversation the team runs.

Patrick walked through a deal room application built on the same primitives. Documents, deal data, AI agents that can web-search and enrich records, branded portals with granular permissioning. The version he highlighted automatically compares an incoming MSA against a standard MSA and surfaces the differences. Most companies pay an external tool to do exactly that.

The sales enablement portal was the third example. Brand assets, sales collateral, and influencer materials behind role-based access, with the AI Vibe code block enabling custom visualizations (Patrick built a geofencing dashboard for a customer the day before) pulling from real data sources.

The HubSpot integrations solve a different class of problem. Branded forms that submit directly into HubSpot, AI enrichment on submission, deduplication logic at scale, and client portals that surface HubSpot data without requiring outside collaborators to take up a paid seat. For any team that has paid for a contractor's HubSpot license just to give them visibility into one project, the math here is obvious.

The gap between teams shipping AI-built GTM tools and teams still talking about it is widening fast. The tooling is here. The infrastructure is here. The teams that move first will run leaner, ship faster, and learn from production data while their competitors are still scoping the build.

So what's stopping you?

Want to keep this conversation going with operators figuring it out in real time? Join the RevGenius community and be part of the movement rewriting the GTM playbook.

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