Use case

Expense approvals without the shoebox of receipts.

Receipts arrive by photo, email, and memory. Someone retypes them, someone approves them on trust, and finance reconciles the mess quarterly. A generated expense system takes the receipt at the door, extracts the details with AI, routes by policy, and leaves an audit trail - with a human approving anything that matters.

What your generated system includes

Data model

  • Expenses: amount, merchant, category, date, receipt file
  • Employees and approval chains
  • Policies: per-category limits and rules
  • Reimbursement batches for payout runs

The flow

  • Submit by form with the receipt attached
  • AI extracts amount, merchant, and date from the receipt
  • Within policy: to the approver with everything pre-filled
  • Out of policy: flagged with the specific rule it breaks

Automations

  • Approver notified on Slack or email
  • Submitter notified of the decision
  • Weekly digest of pending approvals
  • Monthly export for payroll or accounting

Guardrails

  • AI extraction is suggestion, not truth - the approver sees the receipt
  • Low-confidence extractions route to review
  • Every approval logged: who, when, what changed
  • Policy changes preview their impact before applying

Try this exact prompt

Paste into Chromoly

Build an expense approval system. Employees submit expenses with a photo of the receipt; AI extracts the amount, merchant, and date for the approver to verify. Meals over $75 and anything over $200 route to me, the rest to team leads. Notify submitters of decisions, send me a weekly pending digest, and export approved expenses monthly as CSV for accounting.

The extraction step runs with confidence thresholds - anything the AI isn't sure about lands in a review queue instead of a ledger.

Why AI extraction plus human approval is the right split

Pure manual entry wastes hours; pure automation books errors. The generated system splits it the way the architecture principle says to: AI does the reading (extract amount, merchant, date - the part humans hate), humans do the judging (is this legitimate, within policy, correctly categorized). The approver sees the receipt and the extraction side by side, so verification takes seconds.

Finance gets the part they actually want: a clean, queryable record of every expense with its receipt attached, exportable any time.

Frequently asked questions

How accurate is the AI receipt extraction?

Good enough to save the typing, not trusted enough to skip the human - by design. Extractions come with confidence scoring; low-confidence results route to a review queue, and the approver always sees the original receipt next to the extracted values.

Does this replace tools like Expensify?

For teams that found per-seat expense tools heavy or generic - yes, that's the fit. You get your policies, your categories, your approval chain, no per-user pricing. If you need corporate-card feeds and travel booking, a dedicated expense platform may still earn its keep.

Who maintains the system after it's built?

Chromoly does - monitoring, security patches, AI model upgrades, and integration migrations are platform-owned for the lifetime of the account. Changes you request run through a dependency-aware preview showing the blast radius before anything ships.

Can I import my existing data?

Yes - export your current spreadsheet or tool to CSV and import with column mapping, or attach the spreadsheet to your prompt and Chromoly shapes the data model from it directly.

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