Training
We train you to become the modeling master.
Best practice financial modeling (BPFM)
Participants will learn how to build a working financial model from scratch by building their own training models along with the instructor starting with a blank inputs tab. Participants will have a safe environment in which to make mistakes and “fail forward” by asking important questions, and developing intuition on using the model as a tool for deal negotiation.
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Learning Outcomes
- Best practice financial modeling
- Timing
- Construction costs and funding requirements
- Operational modeling and building the cash waterfall
- Senior debt
- Returns and dividend policy
- 3 way financial statements
- Sensitivities and scenarios
Financial modeling for renewable energy projects (FMREP)
FMREP combines many elements of APFM with market insights to help practitioners in the renewable energy space keep apace with rapidly changing policy frameworks and the attendant impacts on deal structures and models.
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Learning Outcomes
- Tax equity overview
- Depreciation deep dive
- Tax equity case study
Advanced Project Financial Modeling (APFM)
APFM builds on concepts learned during BPFM to help participants master the critical elements of navigating deal-ready financial modeling situations.
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Learning Outcomes
- Depreciation and tax
- Reserve accounts
- Advanced macros
- Real world bid case study
Stay up to date
BESS Development for Dummies
April 20, 2026
BESS development hinges on modeling dynamic, stochastic revenue streams—rather than relying on static assumptions—so developers can accurately value and optimize assets in volatile energy and ancillary markets.
AI and the advisory model
April 22, 2026
Agentic AI compresses the traditional advisory pyramid by replacing execution roles, shifting value to relationship-driven deal origination, expanding rainmaker margins, and ultimately driving down client fees.
Model Geeks are the New Audiophiles
April 22, 2026
Like a custom-built stereo system, well-designed financial models embrace transparent, modular complexity—so users can understand, troubleshoot, and adapt them—rather than relying on opaque “black box” automation.
