What it is. An interactive credit workbench built around a simple question: how does a leveraged borrower behave when the world changes? It brings together the metrics lenders rely on, lets you stress the business through higher interest rates and weaker earnings, shows where covenant headroom disappears, and models how value flows through the capital structure in a downside scenario.
My role. I work in and around leveraged finance and private credit, and this project grew out of my own curiosity about how these pieces fit together. The credit framework, analytical workflow, assumptions and calculations are based on my understanding of the space. The implementation itself was built by directing AI coding tools, iterating on the logic, and validating the outputs against my own understanding and public market information. I'm not a software engineer by profession and don't claim to be — my contribution was defining what the engine should do, how it should think about a credit, and checking that the implementation reflected that intent. If you asked me why a calculation exists or what assumptions sit behind it, I should be able to explain it.
Where AI fits. One thing I wanted to explore was where AI genuinely adds value and where deterministic logic is still the better tool. Every financial output here is calculated directly from explicit assumptions — the numbers are deterministic, reproducible and traceable back to their inputs. The only place AI is used is the one-page investment summary: it receives the computed outputs and turns them into a short narrative, and never performs the calculations itself. That separation is intentional — I wanted the quantitative analysis to stay transparent while using AI only where natural language is genuinely helpful.
I spend most of my time around leveraged finance and private credit, but over time I realised that understanding individual metrics wasn't the same as understanding how they interact. I could explain leverage, coverage or covenant headroom on their own, but I wanted to see how they moved together. What actually happens if interest rates rise? How quickly does a covenant become binding? How much of the reported leverage is driven by EBITDA adjustments? Which lenders are still protected if the business gets into trouble?
Those questions eventually turned into this project. I built it over a few weekends as a way to learn by building rather than just reading — changing assumptions and seeing the knock-on effects taught me far more than another spreadsheet or textbook could.
This isn't intended to be production software, and it certainly isn't a replacement for a full underwriting model. It's simply my attempt to understand leveraged credit in a more practical way while continuing to deepen my knowledge of the space. I'm also planning to pursue the FRM, and projects like this are one of the ways I try to connect theory with real-world decision making.
I'm sure there are assumptions here that could be challenged or improved. If you work in leveraged finance, private credit or restructuring and think I've misunderstood something, I'd genuinely appreciate the feedback. Conversations like that are one of the main reasons I wanted to share it.
No model captures reality perfectly, and this one deliberately simplifies a number of areas. The goal was to build something transparent and educational rather than to replicate every detail of a credit agreement. Some of the key simplifications:
I believe models become more useful when their limitations are clear, so I've tried to make those trade-offs explicit rather than hide them.
The featured example uses Cloud Software Group following the 2022 leveraged buyout and subsequent 2023 refinancing. It was chosen because it contains several characteristics that make it an interesting credit case — a layered capital structure, significant EBITDA adjustments, floating-rate exposure, covenant dynamics and refinancing risk.
All figures shown are illustrative, consolidated from public information and simplified for educational purposes. They should not be interpreted as current balances.
Primary references:
Some operating inputs — cash balances, capital expenditure, cash taxes, working-capital movements and scheduled amortisation — are reasonable illustrative estimates rather than directly sourced figures, and are labelled as estimates within the application.
The objective of the example is not to recreate the company's exact balance sheet at a point in time, but to demonstrate how the analytical framework behaves using a realistic capital structure that reflects situations commonly encountered in leveraged finance.