In this episode of The Startup Project, host Nataraj sits down with Molham Aref, CEO of RelationalAI. With over three decades of experience in enterprise AI and machine learning, Molham shares his journey from pioneering neural networks in the ’90s to founding his latest venture. RelationalAI is tackling a fundamental challenge for modern enterprises: the sheer complexity of building intelligent applications. By creating an AI coprocessor for data clouds like Snowflake, RelationalAI unifies disparate analytics stacks—from predictive and prescriptive analytics to rule-based reasoning and graph analytics—into a single, cohesive platform. Molham discusses the evolution of the modern data stack, the practical applications of GenAI in the enterprise, and offers hard-won advice on founder-led sales for B2B startups. This conversation is a masterclass in building a deep-tech company that solves real-world business problems.
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Nataraj: My guest today is Molham, the CEO of Relational AI. He was the CEO of Logicbox and PredictX before this. Relational AI recently raised a Series B of $75 million from Tiger Global, Madrona, and Menlo Ventures. They’re widely known for solving use cases like rule-based reasoning and predictive analysis for large enterprise customers. So this episode will try to focus on what the modern data stack looks like, what applications can be built, and a lot more interesting things around those topics. With that, Molham, welcome to the show.
Molham Aref: Thanks, Nataraj. Pleasure to be here. Looking forward to the discussion.
Nataraj: I think a good place to start would be to talk a little bit about your career and all the things that you’ve done before this. How did you end up with RelationalAI?
Molham Aref: Great. I have been doing machine learning and AI-type stuff in the enterprise under various labels since the early 90s, so it’s over 30 years now. I started out working on computer vision problems at AT&T as a young engineer coming out of Georgia Tech and worked there for a couple of years. Then I joined a company that was commercializing neural networks, a company called HNC Software, and worked on some of the early neural network systems, particularly in the area of credit card fraud detection. I focused on retail, supply chain, and demand prediction. I was very fortunate to have a wonderful experience there learning about the technology and all the things you have to do when you put together what today we would call an intelligent application. We had a very nice IPO in 1995. We bought a small company called Retek that was working specifically in the retail industry and learned a lot from that experience. We grew Retek quite substantially and spun it out in another IPO in 1999. You start thinking, this is easy. Anyone can do this.
So in the early 2000s, I started getting involved in startups myself. One was also trying to apply computer vision technology to a brick-and-mortar environment, a company called Brickstream. Unfortunately, Brickstream was a good idea too early. I learned a little bit about how too early is indistinguishable from wrong in the startup game.
Nataraj: Was it similar to what Amazon Go stores was like? What were you trying to do?
Molham Aref: Not nearly that sophisticated, but yes, the idea is that you can put stereo cameras in the ceiling of a retail environment—a retail bank, a retail store—and start collecting information about what consumers are doing, where they’re dwelling, what products they look at, within certain error tolerances, and then connecting the whole customer journey because you would get handed off from camera to camera as you walk through the store. Anonymously, we didn’t know who people were; we were just looking at the top of their heads. You build a picture of what the brick-and-mortar experience is like. At the time, this was before deep learning, and everything was handcrafted computer vision algorithms. It worked, but it was expensive. A lot of the challenge was what do you do with that data? So we weren’t just solving a problem around computer vision; we had to justify the data. It was a good experience, but not a good commercial outcome.
Then I helped start a company in the wireless network space where we built network simulation optimization systems for AT&T, Cingular, and American Mobile, a bunch of wireless operators. We helped them migrate from the 2G systems they were on to 2.5G and 3G systems and helped them manage infrastructure and spectrum and a whole bunch of other stuff that you couldn’t deploy without the benefit of very sophisticated intelligence.
Nataraj: How did you go from machine learning and vision to wireless networks? How did that idea come up?
Molham Aref: At the core, a lot of what we do in machine learning and AI is about modeling. We have deployed a handful of modeling techniques: simulation, machine learning, GenAI more recently, rule-based reasoning, mathematical optimization, things like integer programming, and graph analytics. The AI toolbox has half a dozen tools that you deploy in a variety of contexts, and it’s perfectly normal for the domain to vary. Whether you’re modeling a wireless network or a retail supply chain, there are entities that are involved. In a retail supply chain, you might have raw material in the form of fabric. In the wireless industry, you have raw material in the form of spectrum. In the retail industry, you might change that fabric through manufacturing to make it into a t-shirt. In the wireless industry, you take that raw spectrum and, using Ericsson and Nokia equipment, you convert it into a wireless minute or a wireless kilobyte. Then you manage supply and demand accordingly. So in both contexts, you have to predict how many customers are going to want this wireless minute or this t-shirt. You try not to make too much of your product because if you make too much, it’s perishable. It loses value.
I’m not a wireless expert, but you learn enough about the core concepts in that domain. That company did reasonably well and was acquired by Ericsson. After that, I went back into retail and helped build one of the first companies that built retail solutions on the cloud. We went all in on the cloud in 2008-2009, when that wasn’t an obvious choice, and leveraged the cloud to do a new class of machine learning. My whole career was spent working at companies focused on building one or two intelligent applications, and in every situation, it was a mess. You had to combine different technology stacks: the operational stack, with the BI stack, with the planning stack, the prescriptive analytics stack, with the predictive analytics stack. You end up spending a lot of time and energy just gluing it all together. That’s fundamentally the reason building intelligent applications is hard. I thought it would be cool to build a generic technology on a popular platform to make it so that you don’t need so many components and so much duct tape.
Nataraj: It would help for the listeners if you can talk a little bit more about what predictive analytics, prescriptive analytics, or rule-based reasoning mean.
Molham Aref: Broadly speaking, you have descriptive analytics. It answers the question of what happened in my business. Business intelligence is a form of that. You’re looking backward and saying, what were the sales of flat-screen televisions in Boston last year? You can aggregate the data by region, by different time granularity, by different types of products. If you just have descriptive analytics, it’s up to the human to look at that and then project forward as to what you’ll sell in January in Boston or Philadelphia. There’s a ton of data to look at, and you can improve that with a variety of modeling techniques, everything from time series forecasting to today’s graph neural networks to help you understand what drives demand. If you can now predict what the demand is going to be in January or February next year under various promotional and pricing scenarios, you can now leverage prescriptive analytic technology. Descriptive, predictive. Prescriptive is, I know the relationship between, say, price and demand. What should I set the price to to maximize revenue or profit or market share? The technology you use for each of these tasks is different. GenAI, of course, is a very powerful new technique that we have in our toolbox. But at its core, it’s predictive because it’s trying to predict the next word in a sentence.
Nataraj: You figured out it’s very hard to deploy these solutions and you started RelationalAI in 2017, before GenAI. What were the primary use cases and types of customers you were targeting at that point, and how did it evolve?
Molham Aref: My area of expertise and our team’s area of expertise is in the enterprise, deploying all these different techniques, including rule-based reasoning, which is symbolic reasoning. The idea was to take all of these techniques because for any hard problem in the enterprise, you can deploy all of them to solve it. We help build applications today that have elements of GenAI, graph neural networks, integer programming, graph analytics, and rule-based reasoning. I strongly believe that the combination is what wins. My view is AI and machine learning, in particular, drive us towards data-centricity because the datasets involved are big. The old architectures that use the database in a dumb way and then pull data out to put in a Java program stop working when you have to pull a terabyte of data out. We wanted to move all the semantics, all the business logic, all of the model of your business as close to the data as possible. We built a system that we take to market as an extension to data clouds like Snowflake. We call it a co-processor. We plug in inside Snowflake and build a relational knowledge graph that facilitates queries that do graph analytics, rule-based reasoning, predictive analytics, and prescriptive analytics. It’s all in one place: your data, your SQL, your Python, and all of these capabilities. We see a 10 to 20x reduction in complexity and code size.
Nataraj: What forced you to build on top of, or as you call it, a co-processor on Snowflake? There are other platforms like Databricks. What pushed the edge in Snowflake’s direction?
Molham Aref: This is a very important decision. In the 90s, I was at a company that picked Oracle and Unix as a platform. We were competing against companies that picked Informix or some other relational database. If you don’t get the platform decision right, you can jeopardize your go-to-market motion. From my perspective, we talked to a lot of enterprises, and what we see in practice in the Fortune 500 and the Global 2000 is for SQL and data management, Snowflake is by far the leader. We see them first. We see BigQuery a distant second. Databricks, until recently, didn’t have a SQL offering. They’re everywhere with Spark, but we still don’t see them that much for SQL. For us, it was a really obvious choice to build on Snowflake because they’ve got the traction. Now, there’s a lot of competition, and we’ll see how it all evolves, but my bet is still on Snowflake.
Nataraj: Can you tell us a little bit about your journey of finding your first five customers and what it took to get them?
Molham Aref: It gets progressively easier as you work in the B2B space more. When I was earlier in my career, I didn’t source the customer at all. At HNC, we were selling neural networks. You go to a bank and say, ‘Buy my neural networks.’ The bank goes, ‘What’s a neural network and why would I buy it?’ At some point, they realized that wasn’t effective. It was better to go to a bank and tell them we’re solving a problem they have in their language. If you say, ‘You’re losing $500 million a year in credit card fraud, and if you use our system, you’ll only be losing $200 million,’ any banker’s going to understand that. Then it just becomes a matter of proving you can create those savings. I learned the importance of learning the language of the industry I’m selling into. The folks that are most effective in sales are not the slick talkers; it’s the people who can bring content to a conversation so the prospect doesn’t feel they’re wasting their time. Fortunately, after being at it for a while, you get a multiplicative effect. We had some early customers at Relational AI that used to be customers of mine 15 or 20 years ago at a different startup. Because of the good work we did for them then, there was a level of trust.
Nataraj: Which field did you pick initially and what was the value that you were offering them?
Molham Aref: Starting from graduating college, I liked computer vision and stumbled into an internship at AT&T. Then when I went to HNC, it was because I was interested in neural networks, not particularly in industries. The group I was attached to was selling into retail, manufacturing, and CPG. So you start learning about forecasting problems, supply chain, and merchandising. You learn the language of the industry that way. You have to do the hard work of seeing it from the eyes of your customer.
Nataraj: Right now, what type of customers are you mainly catering to? Is there a sweet spot?
Molham Aref: RelationalAI is more of a horizontal infrastructure play. We are a co-processor to Snowflake. Instead of going to the line of business executive, we’re targeting the Snowflake customer. There’s usually a CTO, CIO, or someone senior who understands infrastructure and data management. To that person, we seem like magic. They spent the last two or three years moving all their data into Snowflake. The last thing they want to do is take that data and pull it back out from Snowflake to put it in a point solution for graphs, rules, predictive, or prescriptive. We come along and say, keep it all there. We plug into that environment. We run inside the security perimeter, same governance. You don’t have to worry about data synchronization because our knowledge graph is just a set of views on the underlying Snowflake tables. We’re relational, which is the same paradigm as Snowflake. So you get something that feels like magic.
Nataraj: When you’re building on top of Snowflake, how do you think about competing or getting cannibalized by Snowflake itself?
Molham Aref: It’s not a new phenomenon. It used to be hardware was the platform. Then operating systems came along. Then Oracle came along. There’s always this tension between the platform and the thing running on it. If the thing running on the platform starts to generate a lot of value, the platform provider can try to make it a feature. You see this all the time. You have to be really good and have a substantial enough solution where it’s either very difficult or very expensive to copy. Look at vector databases. Very hot for about six months, but now it’s a feature in everything. With us, our technology has deep moats. We have a new class of join algorithms, new classes of query optimization, new relational ways of expressing certain things. It creates deep enough moats where I think everyone will have an easier time working with us than trying to compete with us, at least the platform providers.
Nataraj: Can you talk a little bit more about this concept of the modern data stack and where RelationalAI fits into it?
Molham Aref: The modern data stack is a term that came into existence about 10 years ago. It was about the unbundling of data management. There are two ways to make money in our industry: bundling and unbundling. The modern data stack was basically a term used to describe an unbundling of data management so that you could pick different things. You can pick your cloud vendor, your data management platform, your semantic technology, your BI technology. They weren’t coupled together. From that, you had certain things emerge, like Snowflake, DBT, and Looker. It continues now with Open Table Formats and Open Catalogs. I think the next big fight is going to be around semantics and business logic.
Nataraj: What do you mean by business logic and semantics?
Molham Aref: It’s like DBT makes it possible to express semantics in SQL in a way that you can then pick whatever SQL technology you want to run it on. With business logic, you’re kind of tied into certain stacks. A lot of the business logic people write is in procedural programming languages that are not open. If you can capture the semantics of your business logic in a generic, declarative way, then you can map that to whatever is popular that day. A lot of energy is spent managing accidental concerns, not fundamental concerns. If you had your semantics defined in a way where they were not platform dependent, then whatever replaces cloud computing, you would just target that. You separate the business logic from the underlying tech.
Nataraj: As someone who saw machine learning and AI evolve, how do you see the current GenAI hype cycle? What are you excited about?
Molham Aref: GenAI is super exciting. For the first time, we have models that can be trained in general and then have general applicability. Up until GenAI, you built models for specific problems. Now you have models that just learn about the world. I think we are a little bit past the peak of the hype cycle. In the enterprise, what people are finding out is having a model trained about the world doesn’t mean that it knows about your business. What I see happening now is a bit more sobriety and the realization that to have GenAI impact, I need to be able to describe my business to the GenAI model. It doesn’t come with an understanding of my business a priori. We’re starting to see a lot of appreciation for combining that kind of technology with more traditional AI technology like ontologies and symbolic definitions of an enterprise.
Nataraj: Are you leveraging GenAI for your own company? If so, in what ways?
Molham Aref: We don’t build models; we’re not an Anthropic or an OpenAI. Our core competency is how you combine a language model with a knowledge graph to answer questions that can’t be answered otherwise. We’ve been doing work with some of our customers to show how much more powerful GenAI can be if it’s combined with a knowledge graph. Internally, all our developers have the option of using coding copilots. We are exploring some new companies that will make all our internal information searchable via a natural language interface. But we’re still a relatively small company.
Nataraj: You emphasized how sales is perceived in B2B. Can you talk more about your framework for approaching B2B sales?
Molham Aref: I think it’s a mistake for the founders of the company not to take direct responsibility for sales. You have to go out there and do the really hard work of customer engagement and embarrassing yourself to see what really works, what really resonates, and where the pain is. Trying to hire a salesperson to do that for you early on is a huge mistake. Once you’ve figured out what works, now you have the challenge of simplifying it and establishing proof points so it becomes easier for someone who is not as close to the problem or technology to come in and sell it. But even then, you want that person to be able to have a content-rich conversation with a buyer. People are worried about their jobs, their careers, their companies. They want to spend time with people who can really help them.
Nataraj: Where do you see RelationalAI going next?
Molham Aref: We just launched our public preview last June. It’s been amazing, all the inbound customer interest from the Snowflake ecosystem. We have a GA announcement coming out next week. There’s just so much alignment between us, our customers, and our partner Snowflake, that I think we will spend a lot of energy in the next two or three years building a very sizable business there. Beyond that, we will see. I do think intelligent applications represent a great opportunity because they’re still hard to build. If the world starts to appreciate the value of this data-centric, knowledge-graph-based way of building applications, I think we will enjoy serving the market as it figures this out.
Nataraj: What do you consume that forces you to think better?
Molham Aref: I really enjoy reading about history and listening to various historians characterize history at many different scales. There’s a lot to learn from history. It does repeat itself. There’s so much to learn in terms of human beings, our behavior, how we organize, how we get excited about pursuing certain ideas, and how ideas emerge and die. I think there are analogs of that in the enterprise because our job is to motivate a group of people around a mission to go do something great.
Nataraj: Who are the mentors who helped you in your career?
Molham Aref: Many people have been kind and generous. I’ll call out two people. One is Cam Lanier. He was just an amazing guy who passed away earlier this year. He was a great role model of someone who became very successful in business because if he shook hands with you on something, you could take that to the bank. He understood how integrity and trust really drive profit. I’m forever indebted to him for his mentorship. Another person is Bob Muglia. I met Bob after moving to Silicon Valley. He and I connected very much on what we do at RelationalAI. He’s studied the history of the relational movement and how it became dominant. Bob is just an amazing product person and an amazing human being.
Nataraj: What do you know about being a founder or CEO that you wish you knew when you were starting?
Molham Aref: It’s hard. It’s very difficult. This will probably be the last time I do this. I’ve been very fortunate to be part of some very successful ventures. I couldn’t not do this because I’m on this mission to make this kind of software development possible, and I work with some amazing people. But this stuff ages you. It’s really difficult, and you have to be ready for a lot of difficult times. Also, working well with people. A lot of the challenges you have are with people dynamics, creating an environment where you can have a diversity of expertise and skills and have people work together and appreciate each other. That’s super challenging.
Nataraj: Well, that’s a good note to end the podcast on. Thanks, Molham, for coming on the show and sharing your amazing insights.
Molham Aref: Thanks. Thanks for having me.
This deep dive with Molham Aref highlights the shift towards data-centric architectures and the immense opportunity in simplifying complex enterprise workflows. His insights provide a clear roadmap for leveraging modern data platforms to build truly intelligent applications.
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