How we built Level
How we built a modern CFO firm from $0 to $1 million in revenue in six months
Finance services are known for manual work, fragmented systems, and growth that depends on adding people. We built Level around a different idea: standardize the repeatable work, make every important check auditable, and preserve human judgment for the decisions that actually need it.
The constraint was not intelligence
Owners rarely lack reports. They lack a reliable operating rhythm that turns messy records into a short list of decisions. We focused on reconciliation, exception checks, margin movements, revenue leakage, spend concentration, and monthly review because those repeat every month and benefit from consistency.
We spent $500K on AI tokens
We spent $500K on AI tokens while building and testing these systems. That does not mean every finance decision became an AI decision. The durable value came from schemas, deterministic checks, evaluation cases, failure gates, and clear boundaries around what a model may and may not do.
The 20 skills that survived
The public catalog covers the recurring work where structured evidence creates leverage: bank reconciliation, monthly variance, recurring subscriptions, idle-cash review, 13-week cash forecasting, AR integrity, duplicate billing, revenue leakage, margin trends, spend concentration, cost rationalization, vendor rules, remittance matching, customer margin, 1099 readiness, close readiness, finance charts, and monthly CFO review.
They are not twenty prompts. Each skill defines its evidence, calculation, possible explanations, limitations, and final human decision boundary.
The surprising part was where AI belonged
AI was most useful after finance judgment had been recorded, not before. We captured how experienced reviewers investigate a variance, what evidence changes the conclusion, which explanations are plausible, and when the system must stop. AI could then apply that judgment consistently while a finance professional retained the final call.
Tools make the work easier, people make it useful
By the end of the day, these are tools. AI can make a finance firm faster and easier to run, but a human with domain expertise still has to interpret the output, challenge assumptions, and talk through the decision with the owner. Software can flag an unusual margin. It cannot understand the full operating context by itself.
Now we are making the reusable layer public
We are publishing the reusable finance tools, checks, schemas, synthetic examples, and playbooks so operators and finance teams can inspect them, use them, and improve them. Start with one check, understand its assumptions, and keep a human reviewer in the loop.
Open source does not mean open client data. We publish reusable finance tools, checks, templates, and playbooks. Client records, workpapers, credentials, communications, and private configurations remain confidential.