Writer’s May Habib: Building AI Tools For Corporate ‘Normal People’ The Labs Leave Behind

**May Habib** (0:00)
No one wants to talk about ROI, right? All the cool boys aren't talking about ROI. Why is May talking about ROI? Customer cares about ROI, right? Are they paying for software that does something?

**Alex Konrad** (0:09)
There are lots of cutting-edge AI tools helping engineers write faster code. But at the world's biggest companies, other roles, from marketing to sales and even IT, can benefit from on-brand AI output that doesn't break the bank. Rider is a startup unicorn that trained its own large language models and spent a decade on software to make that non-technical work flow faster. Our guest today is Rider's co-founder and CEO, May Habib.
May pivoted her startup Qordoba in 2020 to leverage that new wave of AI tools. She now works with some of the world's largest businesses, from Accenture to Hilton.

**May Habib** (0:44)
It is really fun to be a builder in the enterprise right now because I don't think anyone has had this level of intimacy with the customer ever.

**Alex Konrad** (0:53)
Today on the podcast, we're going to talk about token maxing and solving for corporate AI sticker shock, what companies need to get value out of LLMs today, and the challenge of running a woman led startup in AI's male-centric Wild West.
I'm Alex Konrad, founder and editor of Upstarts Media. This is The Upstarts Podcast, our weekly show where we talk to emerging startup founders who are punching above their weight to take on the status quo. May, welcome to the podcast.

**May Habib** (1:20)
Thanks for having me, Alex.

**Alex Konrad** (1:22)
This episode is brought to you by Rippling AI, the only AI built to give you full visibility into your startup and the ability to take action across every department. So, May, I know what Rider is, but for anyone who isn't familiar, give me the sort of elevator version of what makes Rider exciting.

**May Habib** (1:38)
Yeah, we are an agentic platform for the enterprise. And yes, the models are so infinitely capable today, but enterprises need built-in brand, built-in compliance. They need to scale workflows and share workflows. And Rider makes that possible.

**Alex Konrad** (1:55)
And so Rider is not necessarily intended for the engineers that we hear about, using a lot of tokens to code new apps or anything, but maybe more other roles within a company. Is that right?

**May Habib** (2:05)
It is primarily for business users. And so our big North Star from a product perspective is a normal person needs to be able to use this. And so if they're building an app and need a database or are doing a session and have shared deliverables, we're taking care of all of the infrastructure behind the scenes for them.
In reality, you get the emergence of a builder class within a team that is adopting AI. And so the kind of persona behind the business persona is a power user, but that is a non-technical person. It's a non-technical person who can think in systematic ways and we really adopt that to kind of build the workflows that everybody else benefits from.

**Alex Konrad** (2:47)
Got it. Now you get to work with some of the largest companies in the world, like a Clorox or Marriott. When these companies are coming to writer for help, is there a business problem or a productivity gap that they are often trying to address?

**May Habib** (3:03)
So, so much of what the inbound is about is, hey, I hear my peers are using you, like how do you actually fit into my landscape, right? And the reality is, when you serve the Global 2000, they're already using all your competitors. And that actually is excellent for us, because they have now understood what they can or cannot get done with a co-pilot, a co-work, a chat, etc. And so, when we come in, we get to give them a proactive point of view on what the future of the front office could look like. So, if it is wealth and asset management, that's a viewpoint. If it is pharma, that is a viewpoint. But essentially, what it comes down to is sales and marketing, working as one team.
And so much of the shared workflows that we build for customers are about gentifying those core go-to-market processes for them. It means a radical rethinking of their org design.
And that really is honestly the biggest blocker to scale. We can get in and within 30 days, 60 days, show incredible agentic capabilities that is running on their workflows, connected to their systems, that the power users are very excited about. But production and scale, we have learned, are two very different milestones in a company. And that initial production-grade workflow gets everybody excited. The number of times our stuff has been presented to a board, like hundreds of times. But then, how long it takes for you to actually get the value, right? The 80 mil of savings because you've turned off all these agencies, or the 100 mil of savings because you have rewired an organization. That takes time because it's about people's jobs and livelihoods and controls and responsibilities and leadership paths. And all of that is, unlike the coding market, painstakingly slow in the enterprise.