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

0:00May Habib: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? 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. Writer 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 Ryder's co-founder and CEO, May Habib.

0:35May pivoted her startup Cordoba 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.

0:44May Habib: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. 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 Conrad, founder and editor of Upstarts Media, and 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. Mae, welcome to the podcast.

1:20May Habib:Thanks for having me, Alex. 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, Mae, I know what Ryder is, but for anyone who isn't familiar, give me the sort of elevator version of what makes Ryder exciting. 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 Writer makes that possible. And so Writer 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.

2:05Is that right?

2:05May Habib: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.

2:47Got 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 Rider for help, is there a business problem or a productivity gap that they are often trying to address?

3:02May Habib: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? All 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, et cetera. 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.

3:38May Habib: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 agentifying 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.

4:18May Habib: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 roles and responsibilities and leadership paths. And all of that is, unlike the coding market, painstakingly slow in the enterprise. You just packed in a lot, but I think there were two pieces there that immediately popped for me.

4:58One was this idea that customers have kind of tried everything. And I'm curious, what is the driver there? Is it experimentation that they just have FOMO and they're like, hey, we should be using these tools, but we're not sure which one? Or then to the point of the cost savings you mentioned sort of at the end of your answer, are they feeling pressure to save money or to find efficiencies in some specific way?

5:21May Habib:Pressure is an understatement. If you are a Fortune 500 executive, like bold-faced names, Alex, are meeting on the weekend about AI. Pressure is an understatement. They feel an incredible amount of stress actually trying to get the rewiring of their organization done, of getting agentic and AI use cases that are worth telling the street about. Right. And that really is the North Star for a lot of these folks is have I actually been able to to get value? And from the meetings I had this morning that I told you about to literally just about every executive conversation, it's, you know, we've got adoption.

6:01May Habib:I forced people to use this stuff, but I have no results to show for it. That's a huge gap that we step into because despite the trillions of dollars of value that has been created, you've got anthropic and open AI salespeople who walk in like heroes, get a contract and leave to literally never be seen again. And it leaves so much to be done in these companies for startups like us. We first met because you had made the Cloud 100 Rising Stars list. Yeah. And I co-created the Cloud 100 franchise for Forbes a decade ago. and so you were working on a company called Cordoba. Yes, machine translation.

6:38Yeah, using machine learning and sort of the pre-LLM AI tools that were exciting a decade ago. What was the through line of sort of the idea and the mission of Cordoba with those customers through to sort of what you're doing now with Rider?

6:51May Habib:We've been working at the intersection of NLP, language, machine learning, the enterprise for 12, 13 years now, with Seem and I. And, you know, through Cordoba is how we started to build transformers. Now, you know, those first transformers were writing haiku and correcting language. That was really kind of their highest use. And so for us it was, well, there's probably a very big business to be built correcting all of the English for brand compliance and ease of translation and the translation market itself. And so when we started Writer, you know, our vision statement was we're going to go from AI-assisted writing to AI writing to who knows what's beyond.

7:34May Habib:And I think it's the who knows what's beyond that has really surprised us just, you know, how far we've all been able to get with the brute scaling of these models. And so I think we're very good at the enterprise because we have been doing it across two companies. And, you know, the core of our company are people who have been working together for a very long time. So it has been very fun to feel like, you know, I've been doing, in a lot of ways, working on the same mission for such a long period of time because you do start to feel that mastery. I think the thing that has not changed is, you know, working late into the night and simply not being able to use software without filing Jura ticket reports and product design requests, right?

8:15May Habib:Like, I cannot use the product if I have not also figured out all the ways to correct it. So something's never changed in the development side. From the capability side, though, you know, there's this wave of companies like Cordoba. Grammarly starts around this time. There are a bunch of these sort of pre-LLM tools. What do you think you guys were able to get the most right or deliver the most? And where did the technology not just be ready yet, you know, so that LLMs maybe really changed the game? Like if we think about sort of that was 1.0, maybe now you're working on some version of 2.0. Well, I think it's 4.0.

8:48May Habib:I think, you know, writer versus Grammarly in that first year, we'd always been marketing the fact that Grammarly is rules-based. We're this like new thing based, right? Now, LLM wasn't there, but Transformer was there. And we described it as encoder decoders for folks who really got under the hood. We've always sold to people who just wanted the thing to work. Even today, Palmyra powers 90 % plus of our LLM calls, our own frontier models. What is the temperature in the room with these big companies that you're meeting with? Are they feeling coming from a place of negativity that they haven't realized the results that they want and you're kind of cheering them up?

9:28Are they still excited? Give us that heat check right off the top.

9:31May Habib:It's such a good question because I think it's also what helps really distinguish our team as folks who will partner with these executives to get results versus folks who are speaking at them or trying to sell them something. And the answer depends on the level of politics. Like we have been in situations where a CIO, a CMO, and a CEO are literally trying to like split the baby, right, on strategy. or the sunk costs fallacy of I've already spent 20, 30, 40 mil on some internal company GPT that I feel so much allegiance to and everything I buy needs to fit into that. Well, it's not working. And so much of the home-built stuff is accumulating tech debt as soon as it is launched.

10:18May Habib:And folks who are really struggling with the politics of that haven't built the kind of trust that says, look, we need to move on to the next thing and get people to move along with us. So, so much of the temperature really depends on, you know, is this a political organization? Has politics entered the chat, so to speak? Or is it a very collaborative organization where the executives have all worked together for a long time, they've made mistakes together, but are working in a highly iterative experimental way to do things? And depending on that, Our ambition level deals with that. And so with some of our most ambitious organizations and Edward Jones, a Vanguard, a Northwestern Mutual, we can dream along with them.

11:03May Habib:And, you know, the number of times executives or folks in the middle of the organization at those companies have said, oh, my God, like we were going to go to level two of our plan. Thanks to you guys, we get to go to level 10 of our plan. And so you really co-create together when there is incredible organizational health, I would say. When it's more political, it's just, you know, you get in a corner with somebody and you help make their patch as bright and beautiful as possible and hope for the best. Got it. And that's more the saving their mood, keeping them from despairing about the future.

11:40May Habib:Yeah, and it's not just us. I mean, we've got a real set of executive programs that pairs everybody together. We have a CIO council. We have a CISO council. We have a CMO council. And we're starting ones by industry as well because people need to learn from everybody else. We just had an advisory board meeting where tokenomics came up. And we have very efficient token economics in our business. You're not setting out a new set of plates every time someone comes over for dinner with Ryder. So not only are the underlying models much more efficient, but the way that we scaffold and set preferences means that you're using 30%, 40%, 50 % fewer tokens each time.

12:17May Habib:And because we're built for shared and scaled work, all those workflows get set before, right? And yes, you can, you know, augment them, et cetera. But also, you're not overusing tokens as a result of the model needing to rethink how to do something. And so, everybody had a lot of questions about that. And we said, all right, what we're going to do is Ryder's own CFO is going to interview each of you anonymously. I mean, you know, that's obviously not an anonymous conversation. But we will aggregate the learnings. And at our September meeting, we're going to share out everything we've learned about what everyone is doing on the FinOps side with regards to AI.

12:50May Habib:And so 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. Like, not Palantir, not Salesforce, and the customer themselves. I mean, the first time I was called a vendor, I sort of like almost teared up and cried because I was like, ugh, vendor, what? But you really work beyond that so quickly because this is, it is existential for folks where they're putting their chips on AI and, you know, where their secret weapon and, you know, not so secret in many pockets. But it's incredible to have that level of co-creation.

13:30May Habib:It's like having design partners across every business, right, across every customer. Now, not every pocket, right? But you find who are the power users, the innovators, the executives who really are ambitious, right, and can think big about what the technology can do. And you become the tip of the spear for the account together. I don't think anyone's ever built software that way. The labs certainly don't build software that way. So it's very exciting for us to get to do it. When you guys were sort of first going out into the market a few years ago, did the product kind of speak for itself and people were able to sort of plug and play a bit?

14:07Or did you have to be going in there and almost acting like a forward deployed engineer showing people how to get the value from Rider?

14:14May Habib:Yeah, I would say like three kind of big phases, right, for us. Pre-GPT, pre-chat GPT, where the advantage was we were very unique, right? The disadvantage was, and I remember I'll never forget, it was like I, over my dead body, will AI writing come into this organization? And I was like, fuck, like are we seriously building the wrong thing if the people that this is meant for like hate it, right? And in a lot of ways that instinct was right in that the people who took to our first generation of like content generation, et cetera, were the non-experts, were the non-craftspeople, right? Then ChatGPT happened where the risk for bringing AI into the organization went negative, right?

14:55May Habib:Like you were crazy if you didn't try to do it, right? And that was like incredible inbound, right, as a result because our built-in brand and compliance really, really resonated with people. I would say in the agentic era and as we went horizontal and everyone went horizontal, differentiation absolutely was a challenge because everyone was using the same words. I remember a prospect asked one of our reps, could you redo your sales materials to not use any of the words that Copilot uses? And we were like, fuck. Okay, well, a writer could help with that. But also, how interesting. And this is where I think our capacity for customer empathy comes in, where we understand, right, the box of, hey, Alex, how can I help you today?

15:43May Habib:It all looks the same everywhere. It's the new hamburger menu. And I think what we tried to tell customers was the differentiation is everything under the hood, right? Everything that you can use to build shared and scaled workflows. And so, yes, it was us also getting better at showing off the deliverables and working backwards, right? If I am building a, you know, Monday morning briefing for my pharma salespeople, they better all look the same. They better be all audited the same, right? Which means I need to build a workflow for 4 ,000 people that plugs into Outlook, that plugs into the CRM, that for the regions that don't have a CRM, plugs into their spreadsheet.

16:20May Habib:And being able to do that centrally are interfaces that are not the, hey, how can I help you? And so we got much better at telling that story. But this is why at the beginning, like the customer that's tried everything also understands, right, like even the enterprise companies, right, a Microsoft or enterprise-styled anthropic have not built that functionality. It's not even on their roadmap. They don't even know it's a problem, right, which is the problem. Was there a use case or a skill that you guys were really good at early on that was sort of the killer door opener with some of these really big companies?

16:55May Habib:Yeah, I mean, especially in CPG, pharma, FINS, if you sell REITs or if you sell financial products, right, or financial advice, you simply cannot email somebody with LLM voice. Your client knows you. They'll be like, Alex, get out of my inbox. What's the shit you're sending me? I don't care. I don't trust it, right? And so from literally the very beginning, our ability to build in brand voice, style, compliance has meant the world to these customers because it means I can plug into Alex's inbox. And now I know when he's on the road, it's like tons of typos. It's two spaces after a period. Well, no, you're a crimson guy, so I know it's not two spaces after a period.

17:39May Habib:But you know what I mean. It's you, right? And that has always been a huge calling card for us because customers want to scale AI, but they got to do it while scaling brand, while scaling authenticity, while reducing brand risk. Like nobody is going to take the trade of productivity versus brand dilution or risk. And in the enterprise, especially in the regulated industries that we serve, writer is for the stuff you have to get right, that you cannot afford to get wrong. And that's always been a huge differentiator. How do you feel about preserving care, connection, respect when people might be using that tool to work faster or to reply at volume?

18:22Because I think there's a key distinction between transactional emails or messages and more relationship-driven ones. So how do you balance it?

18:31May Habib:Well, we have policies in Rider, right? And this is, you know, the feature set that you build when you actually talk to customers versus talk at customers. And those policies help an admin. It doesn't have to be an IT admin. Sales ops or sales enablement or marketing ops say, here are the ways in which I want my people using these connectors, right? And so you can say, you have to check an email before it sends out, before it gets out. You're not allowed to build automated email flows for one-to-one messaging, right? I'll give you an example from my workflow. So that same advisory board meeting that I told you about, I hope everybody is listening.

19:08May Habib:And you will now know where this email came from. But I don't mind because I do think this is where the world is going. I asked writer, and this is just talking to Slack. I talk to my phone now just about more than I talk to anybody else, everybody else combined. And I said, writer, this is an agent, a writer agent inside of Slack. The meeting we had two days ago, the attendees are all in. I, you know, broadly named the channel. First, go get the attendee names. Then go to Gong. Draft an email for all those 14, 15 people that were in the meeting. try to remark on something they brought to the conversation in my email, and then invite them to the Enterprise Brain beta.

19:45May Habib:And, you know, we had both writer channels, which is basically our, like, Enterprise Open Claw, the proactive agent, as well as Enterprise Brain we had gone through. And then we had had the FinOps conversation as well. So, you know, if they wanted to be in the FinOps conversation, just remark on that. And it was, you know, a 60-second voice note. And then that was on my way somewhere. And then on my way back, got to look through all of the drafts. And the ones that were perfect, so I think like 11, 12 were perfect, send, send, send, send, send, send, right? I'm literally already responding to the responses faster than me even drafting a single email.

20:19May Habib:Now, writer knows my voice, right? It knows our company style. It knows writer is written in all caps. And because of the custom instructions and the policies that we've set, it knows what not to reference in the other calls, right? Like other people may have said in those calls. And so I have a lot of trust, right? Plus, I've got an audit trail to understand, like, if writer, and this wouldn't happen because I have the policy, but let's say I didn't have the policy on and it did send something to Alex that I didn't want to get sent, right? Do I have a audit trail of it sending, right, that IT can look at, that I can look at?

20:53May Habib:And so these are all features that are required to actually trust AI at scale in this kind of like high-end relationship management that we build agents for that customers love. And it works out of the box. And the thing that they'd spent two years building doesn't even come close to it. What was a moment that you were glad you fought through and executed on that you would not want to do all over again? Yeah. A lot of amazing moments. I would love to relive. The thing I would never want to relive is really the pivot from Cordoba to Ryder because it was not nearly as well understood as it is now what this technology could do.

21:32May Habib:And if you could have been in those rooms where we were trying to explain encoder decoders to people and why they would be so massive, you know, we would all be pretty amused. Was it really just a clear pivot or was it more of a— Yeah, it was a recap restructuring. I mean, we would have started a brand new cap table, but both mine and Wasim's visas were tied to the old cap table. So, you know, it was basically a complete restart except for the legal entity. But then the mission and the product, it does feel like there are traces, like there is a through line from Cordoba through Rider. Absolutely, yeah.