Martini.ai isn’t just a clever name; it’s the model that could rewrite how corporate credit risk is priced, traded and assessed.
When Rajiv Bhat named his company Martini, it wasn’t a nod to a drink but a nod to propagation theory in physics. Start with a known state, then tweak it slightly to see how the system responds.
That logic now powers a platform built to detect the financial equivalent of tremors across the $10 trillion corporate credit landscape. The platform has a free interface that puts models once reserved for hedge funds into the hands of anyone who wants them.
Martini.ai isn’t a credit rating agency. It doesn’t publish letters. It doesn’t wait for quarterly financials. It’s a credit risk assessment interpolation company. That’s Bhat’s term for an artificial intelligence-powered model that ingests market data, runs it through graph neural networks, and produces real-time risk signals based on how one company’s tremor could become another’s tailspin.
Martini is one of several emerging, specialized large language models built to assess credit risk using publicly available data on 3.5 million private and public firms. It’s amplified by real-time data on market conditions, industry risk and other relevant data. Bhat said the model processes 600 billion predictions every day.
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That’s a big leap for anyone not trained in quantitative finance. But that’s the point. Martini’s free model gives everyone — from institutional investors to startup chief financial officers — access to what Bhat called “the most sophisticated risk infrastructure anywhere.”
No training required. Just a prompt.
From Risk Analysis to Risk Access
“Just because assets are illiquid doesn’t mean risks are,” Bhat said during a conversation with PYMNTS CEO Karen Webster. “And we’ve reached a point where risk needs to be evaluated in real time. Not quarterly. Not monthly. But minute-by-minute.”
It’s a mindset shaped by Bhat’s time at hedge funds and MIT’s AI Lab. He and his co-founder, Rohit Singh, who has a doctorate from MIT, spent years watching the credit market make slow, costly decisions using stale data and outdated tools. By the time risk showed up in financial statements, it was too late to act. That’s what Martini.ai is trying to fix.
“Every company is connected to about a hundred others — suppliers, customers, logistics providers, even competitors,” Bhat said. “When one moves, the rest feel it. We track that ripple effect in real time.”
The platform builds dynamic credit models by mapping those connections into a massive knowledge graph. It ingests news, financials, pricing data and market signals, then runs deep learning models to assess systemic exposure. If Carvana stumbles, for example, the platform immediately reweights the risk of CarMax and CarGurus. If red metal exports are paused in China, that signal gets priced into the risk profile of every buyer in the value chain.
“We’re not just modeling an entity,” Bhat said. “We’re modeling a network of cause and effect.”
Free Because the Prompt Is the Signal
So why make it free?
The model doesn’t get stronger by staying in the hands of the few, Bhat said. It gets stronger with scale and prompts.
“The more users we have, the more we learn,” he said. “We made it free because we wanted to see what problems people would throw at it. The prompt is the new interface. It tells us what the market actually wants to know.”
That strategy also opens the door to thousands of use cases that traditional models can’t serve, such as small business underwriting, real-time supply chain finance, mid-market credit analysis and alternative asset pricing. The prompt becomes the product. And every prompt expands the platform’s intelligence.
As Webster noted, real-time credit insights can’t just be a luxury for big banks and hedge funds.
“The world is moving too fast for closed systems,” she said. “We need tools that let people act before the risk becomes a loss.”
From Insight to Infrastructure
Martini.ai doesn’t stop at analytics. The vision is to create infrastructure, or what Bhat called the EC2 of corporate credit, an open marketplace where risk, capital and liquidity are matched dynamically, in real time, without the weeks-long underwriting slog.
The platform automates repetitive workflows such as scanning financials, industry benchmarking and risk factor tagging, ultimately allowing users to shift from research to action.
“Companies should be able to plug in, say they need capital, and the system already knows their risk, their financials, their network,” Bhat said. “It’s instant underwriting. Instant access. Instant trust.”
For companies that have historically struggled to get a fair credit assessment — not because they’re risky but because they’re opaque — the implications are massive, he said.
“It’s not about whether someone is fundable,” Bhat said. “It’s about whether they’re legible. We make them legible.”
As the model expands into CLOs, revenue-based financing, insurance and lending, the data flywheel only accelerates. Every prompt refines the signal. Every signal sharpens the model.
“The adjacencies are limitless,” Bhat said. “But it all starts with giving people access to tools that used to be locked behind six screens and a quant team.”
Martini.ai doesn’t ask you to stir or shake the model. Just prompt it.
“Instead of spending time understanding the risk, now teams can spend time addressing the risk,” Bhat said. “The future belongs to companies that act faster, not those who analyze more.”
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