New York and Milan, Italy-headquartered Cardo AI seeks to bring transparency to private markets for investors, banks and funds. A-team Group’s Data Management Insight spoke to co-founder and chief executive Altin Kadareja about Cardo AI’s mission and operations.
Data Management Insight: Hello Altin. When was Cardo AI created and how does it serve financial institutions?
Altin Kadareja: Cardo AI was founded in 2018 to bring advanced technology to the complex, often opaque world of private markets. We serve asset managers, banks, lenders and fund administrators by providing a comprehensive AI-powered technology stack that manages the entire lifecycle of asset-based finance and private credit transactions.
DMI: What is the driving mission behind Cardo AI?
AK: Our mission is to make private markets more transparent and efficient through “intelligent” technology. Historically, private credit has relied on fragmented data and manual spreadsheets. Cardo AI aims to bridge this gap by creating a unified digital infrastructure. We want to help teams elevate their capabilities and focus on new ways of working, aligning with the market shift towards AI. Our platform integrates data from various sources, automates cash-flow modeling, and provides real-time monitoring of underlying portfolios. By providing clear insights into complex asset classes, we help financial institutions make better investment decisions and scale funding.
DMI: What are the most common pain points that Cardo AI solves for its clients?
AK: Cardo AI most often solves the “fragmentation problem” in private credit and asset-based finance: messy data coming from many sources, no single source of truth, and too much time spent reconciling spreadsheets instead of making decisions. We standardise ingestion, validate and enrich inputs, and provide full traceability so numbers are consistent, explainable and audit-ready across teams. We also take the most operationally painful workflows, borrowing base and eligibility rules, concentration limits, covenant monitoring, reporting, and make them repeatable through configurable logic and automation. The result is lower operational risk, faster reporting, stronger early-warning signals, and the ability to scale efficiently.
DMI: What are the newest challenges that Cardo AI is helping clients overcome?
AK: One of the newest challenges we see is scale meeting complexity. Private credit and asset-based finance portfolios are growing fast, but the underlying data is becoming more fragmented, less standardised and more heterogeneous across asset classes. Clients are also under increasing pressure to produce faster, more transparent reporting, stronger risk signals and clearer audit trails, often across multiple strategies and jurisdictions. Cardo AI helps address this by making complex credit data structured, explainable and operational at scale. We embed AI directly into core workflows like onboarding, eligibility testing, covenant monitoring, and reporting, so teams can adapt to new asset types, deal structures, and regulatory expectations without re-engineering their processes each time.
DMI: How is provisioning for private credit investors changing in the age of AI?
AK: Provisioning is becoming more “alive” and less of a periodic spreadsheet exercise. Investors are increasingly expected to take a forward-looking approach, considering past experience, current conditions, and forecasts. AI makes it practical to refresh those inputs much more often by cleaning and reconciling data feeds, spotting anomalies early, and extracting useful signals not just from structured reporting, but also from alternative data sources and unstructured documents. What isn’t changing is the need to explain the result. A simple way to do it is to use AI to move faster, but keep the process fully documented and explainable with clear rules, checks, and an audit trail, because auditors and regulators won’t accept “the model said so”.
DMI: What does Cardo AI see as the next big thing in private credit data management?
AK: We see the next big shift as moving from static data management to continuous, decision-grade data. In private credit, data has traditionally been collected for reporting after the fact. Going forward, it needs to be structured, validated, and explainable in real time, so it can actively support risk management, valuation, and portfolio decisions. That also means tighter integration between data, documents, and deal logic. The firms that win will be those that can trace every number back to its source, adapt quickly to new asset classes and structures, and rely on platforms that make data trustworthy enough to power automation and AI, not just dashboards.
DMI: What’s in the pipeline in 2026?
AK: The total addressable market for private credit is around $5 trillion, and as it grows, investors are looking for smarter, more efficient ways to manage complexity. Integrating purposeful AI is becoming important, not just for analysing data, but for actively supporting decision-making across investment origination and portfolio management. Our focus is on making AI an enabler, helping teams work more efficiently, empowering clients with self-serve insights, and creating scalable solutions that adapt as the market evolves.
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