Building the Easy Button for Generative AI | May Habib, CEO, Writer
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What is the main topic discussed in this episode?
Hi, I'm Matt Turk from FirstMark.
What is Writer and how does it differ from other generative‑AI platforms?
Welcome back to the Mad Podcast. Today's episode is a great conversation with May Habib, the CEO of Ryder, a full-stack generator AI startup that just announced a $200 million round at a $1.9 billion valuation. We talked about the Ryder fanning story.
We got our seed stage term sheet March 3rd.
How did May’s experience with Qordoba shape the founding story of Writer?
I remember that date. Never closed money faster in my entire life.
Went into more technical topics such as the merits of graph vector databases and the benefit of synthetic data.
Being able to really visualize literally the nodes and edges of the graph helps us help the customer. Synthetic data has been our friend for a long time. We have seen the use of synthetic data to have a lot of advantages that I think other folks are already starting to catch up and copy.
And also talked more generally about generative AI in the enterprise, including the rise of super apps.
Why does Writer describe itself as a full‑stack AI company?
The next generation of writer apps is almost super apps that can both use writer autonomously on behalf of the customer, things that they have been more declarative about from a business logic perspective. 2025 is gonna be about AI workflows. I'm really excited for what's possible.
What enterprise use cases does Writer target and how are they solved?
This episode was recorded live at Data Driven NYC a few days before Rider announced their round of financing. Data Driven NYC is our in-person event series that runs monthly in New York, currently hosted at the headquarters of REMP, the financial technology company. If you're ever in New York and would like to attend the events, simply find the Eventbrite link in the show notes. The event is free and open to everyone. In the meantime, please enjoy this great conversation with May.
How does Writer’s knowledge‑graph RAG approach work compared to vector‑based methods?
May? Welcome.
Hi Matt.
Thanks for doing this and uh uh joining our evening and uh community tonight. Um so excited for the chat and uh let's start from the top. What would be the two minute version of what Ryder does?
Awesome, two minutes. So Writer is a full stack generative AI platform. So we help enterpr enterprises quickly get to value with generative AI by combining LLMs with zero engineering rag, AI guardrails, and an AI studio. And so a lot of the problems in enterprise um right now around generative AI are being able to get to quality with efficiency and with adoption. And we've solved a lot of those problems in one uh all in one solution.
And uh for further context, you are a venture-backed uh San Francisco-based startup. Officially the last round of uh VC was 100 million series B in 2023, uh, but there's a wide rumor, which I'm not gonna ask you to confirm unless it has been announced, but I don't believe so, uh, that you may or may not have raised two hundred million at close to a two billion dollar valuation.
What are Writer’s AI Guardrails and how do they protect against PII and hallucinations?
Um in which case uh I may or may not congratulate you. This is the information I have. And um in terms of history, before you started Writer, you founded a company called uh Cordoba that was doing uh machine learning and uh I believe a focus on machine translation, sort of a gram uh grammarly kind of uh focus. Can you talk about that story?
It it's been it's been really one continuous journey for me and Wasim truly.
How does the AI Studio enable customers to build custom applications and autonomous actions?
And uh we have been in the realm of automating language for the entirety of the 10 years we have been working together. So in the the first company, Cordoba, um, we started with statistical machine translation. So this was years ago. And we started using uh transformers and map business encoder decoders at the time, um, really to solve NLP problems associated with um with translation. But it was uh very easy to see, you know, how powerful um this tooling was and um We we in a way felt like uh after years of working on a um a very mission driven business around um you know making The language you were born speaking, just a non-issue, uh, especially in the workplace. Uh, to to go work on source language really felt like abandoning um that initial mission.
But the technology was just too exciting. So, in a lot of ways, the story of Rider is the story of the transformer. And it was in the first business that um we really started um to to use the technology.
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Chapters
8 chapters
1
What is the main topic discussed in this episode?
0:00–0:02
2
What is Writer and how does it differ from other generative‑AI platforms?
0:02–0:20
3
How did May’s experience with Qordoba shape the founding story of Writer?
0:20–0:53
4
Why does Writer describe itself as a full‑stack AI company?
0:53–1:09
5
What enterprise use cases does Writer target and how are they solved?
1:09–1:34
6
How does Writer’s knowledge‑graph RAG approach work compared to vector‑based methods?
1:34–2:46
7
What are Writer’s AI Guardrails and how do they protect against PII and hallucinations?
2:46–3:12
8
How does the AI Studio enable customers to build custom applications and autonomous actions?
3:12–35:45
Speakers
1 identifiedMore from The MAD Podcast with Matt Turck
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