martini.ai is an AI-driven fintech company specializing in credit analytics for institutional investors, risk managers, and corporate financial teams. Pulse 2.0 interviewed martini.ai co-founder Rajiv Bhat to gain a deeper understanding of the company.
Rajiv Bhat’s Background
Could you tell me more about your background?
Bhat said:
“Absolutely! I have a physics background — I studied it through undergrad and went on to do a Ph.D. in quantum mechanics. Early in my career, I spent a couple of years at McKinsey, but I eventually returned to research and had the opportunity to work alongside several Nobel laureates. That experience really deepened my interest in data, modeling, and complex systems.”
“As physics increasingly intersected with machine learning and AI, I found myself drawn to those tools and how they could be applied to real-world problems. I transitioned into the tech world, always in roles focused on data and machine learning. I led data efforts at Kosmix, which became Walmart Labs, co-founded a data-intensive startup, and more recently led machine learning and AI for a unicorn’s marketplace where we were making real-time predictions across hundreds of billions of auctions daily. Working at that scale was incredibly challenging and rewarding — it really shaped how I think about building systems and solving problems with data.”
“Throughout my journey, I’ve always gravitated toward the most challenging and interesting problems in data — especially those involving messy, sparse or inconsistent information. That mindset ultimately led me to co-found martini.ai with Rohit Singh, who brings deep expertise in computer science and finance from his Stanford AI lab background.”
Formation Of The Company
How did the idea for the company come together?
Bhat shared:
“We founded martini.ai based on a fundamental belief: The world is changing faster than ever — supply chain disruptions, geopolitical events, and market volatility can reshape entire sectors overnight. Yet most risk models update too slowly to keep pace, leaving finance professionals making decisions with outdated information.”
“Private credit stood out to us because of the massive capital flows, yet most of the information investors rely on is delayed and limited. Financial statements are often only available to lenders, arrive quarterly — and often months late — and use inconsistent formats across companies. That creates information gaps of six months or more, during which business conditions can change dramatically.”
“But here’s what really drove us: Sophisticated risk intelligence has historically been locked behind expensive paywalls at major firms. We believe that when markets move this fast, everyone needs access to real-time insights — not just those who can afford premium subscriptions.”
“That disconnect sparked the idea for martini.ai. We realized that with the right combination of large-scale data infrastructure and advanced machine learning, we could offer investors and lenders a radically better view of risk — one that reflects the true pace at which businesses evolve — and make it accessible to everyone.”
“By filling the gaps between financial statements with real-time insights and democratizing access to sophisticated risk intelligence, we can help enable better capital allocation, fairer pricing, and ultimately a more responsive and efficient financial system.”
Evolution Of The Company’s Technology
How has the company’s technology evolved since its launch?
Bhat explained :
“Both Rohit and I come from heavy tech backgrounds – he was part of Stanford’s AI lab. Building on our foundation of processing nearly 600 billion predictions daily in our previous work, we knew we needed to bring that same scale and sophistication to credit risk.”
“When we started, we focused on understanding bonds, using sophisticated algorithms like temporal convolutional networks with fairly complex models and pipelines. But once we moved into private companies, we realized general ensemble models weren’t sufficient. So, we built our own graph-based systems with knowledge graphs, specialized algorithms running on those graphs, and novel ways to analyze datasets.”
“Now we’ve layered in agentic workflows and LLM capabilities — that’s what powers our ‘Cursor for Credit’ AI assistant. It’s been a very organic integration that’s made our work so much more accessible. We’re no longer talking to customers about probabilities of default and credit spreads — instead, we’re simply saying, ‘You want to understand a company? We’ll help you understand that company.'”
“The real breakthrough has been our proprietary knowledge graph that processes 10,000 to 20,000 reference points daily, connecting companies through business relationships, supply chains, and market events in real time. It’s become much more intuitive and powerful.”
Significant Milestones
What have been some of the company’s most significant milestones? Bhat cited:
“Our recent public platform launch in June 2025 was huge — we’re now the first company to offer free, AI-powered credit intelligence for over 3.5 million companies. The response has been incredible, and it’s validating our belief that credit risk intelligence should be accessible to everyone.”
“Working with some of the biggest players in the space — two of the three largest — has also been transformative. Getting validation from them, hearing them say, “These are problems we never expected solutions for, but now we can systematically see the risk associated with private companies” — that’s been a major milestone.”
“Our platform’s accuracy has really proven itself through extensive backtesting. Across our full universe of companies, 80% of defaults occurred within the bottom 20% of our rated names. In a recent engagement with a bank, our early warning signals flagged bankruptcies an average of seven months before they occurred. That kind of predictive power is what sets us apart.”
“As my co-founder likes to say, we’ve essentially standardized probability of default and risk assessment across all types of companies. Previously, every company had different risk estimates from different providers, and even within teams, analysts would evaluate companies differently. Now we have a unified scale where you can compare a small company with $100 million in revenue directly with Apple. That consistency allows our biggest customers to use this across their entire massive portfolios.”
Customer Success Stories
When asking Bhat about customer success stories, he shared a compelling example:
“. One of our customers came to us with an insurance deal on a credit portfolio — but this wasn’t just any portfolio. It was a portfolio of portfolios: 60 individual portfolios, each containing 200 to 500 private companies, totalling 11,000 to 12,000 companies, all private.”
“They needed to understand how to price this and assess the associated risk. Because martini.ai comes with everything built-in, we were able to price that entire transaction in one day. When the presentation went to the CIO at one of the biggest firms, he was stunned. His exact words were, ‘We have never seen anything like this on the asset side of the business for pricing risk.'”
Follow-up: Was he more impressed by the quick turnaround or the information quality?
“Both completely floored him. They didn’t believe it was possible before — they used to price only based on high-level portfolio metrics. Suddenly, they could drill down to every single company and have real, granular insights. The speed was incredible, but seeing that level of detail? That’s what really blew their minds. They went from guessing to knowing.”
Funding
When asking Bhat about the company’s funding and revenue details, he disclosed:
“We’ve secured about $6 million in funding from Neotribe Capital and Rocketship VC, which has positioned us well for our current growth phase. We’re keeping our revenue metrics confidential at this stage.
Total Addressable Market
What total addressable market size is the company pursuing?
Bhat assessed:
“We’re operating in the $10 trillion corporate credit market, but our specific addressable market is $50 billion — $30 billion in private credit and $20 billion in trade finance.”
“A good way to think about it: In any credit transaction — whether it’s a loan to a company, buying a bond, or providing payment credit to business customers — about 10 basis points, or 0.1%, of the transaction value goes toward risk assessment. We’re targeting that piece – the risk assessment portion of credit transactions. When you consider the entire transaction volume of $40 trillion to $50 trillion globally, that translates to our $40 billion to $50 billion addressable market within the broader $10 trillion corporate credit ecosystem.”
Differentiation From The Competition
What differentiates the company from its competition?
Bhat affirmed:
“martini.ai is completely differentiated by our intense focus on using AI to address a very specific, very difficult problem: understanding risk in illiquid, volatile markets. The innovation we bring — combining cutting-edge AI with deep statistical understanding of credit — is what sets us apart.”
“We’re very targeted, almost micro-focused, but on a massive, difficult problem. Credit risk estimation is characterized by very noisy, sparse, missing data, and while there’s a lot of signal, it’s inconsistent and difficult to combine. We use the latest AI to make that happen — whether through knowledge graphs, agentic workflows, advanced statistical methods, or traditional bond mathematics. We bring it all together to provide a coherent understanding of credit.”
“What really differentiates us is that we put our neck on the line. We don’t just provide tools or services, we actually provide a score, a number for every business. We’re providing real insight and intelligence.”
Future Company Goals
What are some of the company’s future goals?
Bhat concluded:
“We’re excited to become the go-to place for understanding business risk, period. Whether it’s a bank giving a loan, a credit asset manager evaluating their portfolio, a company doing business with hundreds of other companies trying to set credit limits or payment terms, or a company seeking financing, we want to be the intelligence layer for credit in the entire ecosystem.”
“We want to do this globally. Instead of being just an AI company, we want to be an intelligence company — the single point anyone goes to for this kind of insight. That’s consistent with what differentiates us: We actually put out definitive scores and insights, not just tools. We’re providing intelligence, and that’s where we see the future heading.”
