AI-assisted variance review
Turn general-ledger movements into a prioritized exception queue, draft explanations, and evidence-linked review items.
One process at a time. This is a practical library for testing how AI can improve recurring accounting work while preserving review, accountability, and audit evidence.
Each working playbook breaks down the accounting task, the role AI can perform, the required inputs, the control points, and the judgment that stays with people.
Turn general-ledger movements into a prioritized exception queue, draft explanations, and evidence-linked review items.
Extract contract terms, identify accounting questions, organize authoritative guidance, and prepare a review-ready first draft.
Match entity balances, isolate timing and foreign-exchange differences, and assign unresolved items to accountable owners.
The AI proposes. The accounting owner substantiates. The Controller approves.
View the control modelLock the period, accounts, comparison basis, and materiality rules before analysis begins.
Rank unexpected movements using explicit rules and documented pattern analysis.
Draft explanations only when traceable support is available; never invent a missing driver.
Account owners resolve exceptions and the Controller signs off on material conclusions.
The goal is not autonomous accounting. It is better accounting work: faster preparation, clearer exceptions, and stronger human review.
Every factual claim traces to an approved source or is clearly marked unresolved.
AI organizes and drafts; qualified owners make and approve accounting conclusions.
Missing support and conflicting data are escalated, never silently averaged away.
Inputs, rules, changes, reviewers, and final approvals remain reproducible.
I build accounting organizations that can move quickly without losing control. My work spans global controllership, technical accounting, systems transformation, audit readiness, and the operating rhythms behind a reliable close.
Rebuild Accounting is a work in progress: a place to document, test, and improve practical ways of applying AI to accounting work. The goal is useful operating design—not AI hype.