martini.ai, a provider of AI-driven credit analytics, announced the launch of Agentic AI Company Research, a solution that empowers businesses with comprehensive, real-time insights into individual companies’ credit profiles.
By merging credit spread data with corporate information, this feature reportedly “provides decision-makers — including those in private credit — with data-rich intelligence that highlights key trends, risks and opportunities.”
Against the backdrop of limited financial transparency, high default risk, and resource constraints — challenges that routinely plague private credit investors — Agentic AI Company Research reportedly offers “a streamlined and scalable way to identify and monitor risk across a wide range of private companies.”
Notably, all martini.ai requires is the borrower’s name, and in a few minutes, the platform “delivers an independent signal on a company’s credit risk.”
Rajiv Bhat, CEO of martini.ai said:
“In today’s fast-paced financial markets, access to timely, integrated information is crucial for effective risk assessment. With Agentic AI Company Research, our customers — particularly in the private credit space — gain a clearer picture of each company’s credit health, enabling them to move faster and make data-driven decisions with confidence in a sector often challenged by poor disclosures, lack of traditional credit ratings, and the need for intensive due diligence.”
martini.ai’s new offering seamlessly combines daily credit risk modeling with agentic research to provide a dynamic, 360-degree risk assessment.
Situates each company alongside up to 100 peers, drawing on all available market data and applying advanced graph algorithms to estimate the probability of default for over 3.5 million companies every day. This continuous modeling is especially valuable in private credit, where data can be sparse, and speed is essential for managing risk in illiquid portfolios.
Agentic Research:
Builds upon these quantitative insights by “orchestrating multiple advanced large language models (LLMs) with relationships and insights from martini.ai’s knowledge graph.”
This approach synthesizes over “30 distinct points of research, capturing each company’s history, key events, sector and microsector developments, competitor analysis, macro factors, benchmarks, and the latest news — all in one cohesive view.”
By layering in the nuances of private credit markets, agentic research “offers a richer perspective on emerging risks and opportunities.”
Expected Benefits for Portfolio Managers are as follows:
- Reduce losses by 50 basis points: Early identification of potential defaults through martini.ai’s platform can significantly decrease portfolio losses.
- Increase yield by up to 100 basis points: Rapid identification of high-yield deployment opportunities enhances returns.
- Decrease portfolio monitoring time by 85%: Evaluate a portfolio of 500 names in just 30 minutes, freeing up valuable time for strategic decision-making and higher-value activities.
- Receive fast, independent credit risk signals: Access near-instant analysis by simply providing the borrower’s name, expediting credit decisions.
By uniting contextual data with quantitative models, martini.ai reportedly delivers “deeper, more timely insights for informed decision-making.”
Agentic AI Company Research is the latest addition to martini.ai’s suite of credit analytics solutions, “designed to help financial institutions, investment managers, and corporate decision-makers optimize their risk management practices — especially crucial for private credit investors who require more visibility into portfolio risks.”
As noted in the update, martini.ai is an AI-driven fintech “specializing in credit analytics for institutional investors, risk managers, and corporate financial teams.”
By leveraging machine learning and data models, martini.ai delivers real-time insights that “drive smarter decision-making.”
Headquartered in Palo Alto, California, the company serves clients worldwide with solutions that aim to effectively “streamline credit risk evaluation and portfolio management.”