Welcome toMau
Demo for Fake SLBModeler
From prompt to DCF
Builds, maintains, and optimizes planning models from natural language intent — so your team focuses on business impact, not manual setup.
Scribe
Institutional memory
Records every assumption, change, and the reason behind it — so when the board asks “why did we use 8% decline?” six months later, the answer is already in the model.
Reviewer
QA
Checks the model the way a senior partner would — catching weak assumptions, flagging errors, and reconciling against the reserves report before it reaches the board.


