AI Finance Operations
Sits over QuickBooks + Plaid + Stripe
Headline outcome
Real-time runway; investor updates in hours, not weeks
What the operation looked like before.
A growth-stage company with QuickBooks for accounting, Stripe for billing, Plaid for banking, and a CFO drowning in monthly close + investor-update prep. Burn rate was a slide that took two days to build. ~10% of transactions were misclassified out of QuickBooks, distorting category-level reporting. Investor updates went out late, every month.
The bottleneck
The CFO and finance lead were spending ~40% of their week on data wrangling instead of capital allocation, anomaly investigation, or fundraising.
How the system is wired.
A unified ledger sits over Plaid, Stripe, and QuickBooks. Claude Haiku re-categorizes transactions at scale and flags low-confidence rows for human review. Runway, MRR cohorts, and burn decomposition compute live from the ledger. A scenario engine runs hiring/spend what-ifs. Investor updates draft in Claude Sonnet from the live numbers, then go to a human sign-off before send.
Financial Data Hub
Plaid + Stripe + manual upload unified into one transaction ledger. AI re-categorization corrects misclassifications with Claude.
Expense Intelligence
Auto-categorization pipeline, anomaly detection with z-score analysis, subscription tracking, and receipt/invoice OCR via Veryfi.
Cash Flow & Runway
Real-time runway calculation, AI scenario modeling, Prophet-powered forecasting, and configurable danger zone alerts.
Revenue Analytics
Correct MRR/ARR calculation, cohort analysis, LTV:CAC tracking, revenue recognition, and AI-generated trend insights.
Investor Relations
Auto-generated investor updates from real data, investor CRM pipeline, data room automation, and board deck generation.
Accounts Payable/Receivable
Invoice ingestion with OCR, Slack-integrated approval workflows, bill pay scheduling, and two-way QuickBooks/Xero sync.
Tax & Compliance
Automated deadline tracking, 1099 identification, sales tax nexus monitoring, R&D credit estimation, and RAG on tax code.
AI Financial Chat
Natural language to SQL queries, context-aware answers, weekly Slack/email digests, and rendered charts from query results.
Where humans approve, review, decide.
Every consequential decision routes through a person on your team. Speed without abandoning judgement.
Sample approval surface
Reviews flagged transactions and accepts or corrects category
Flagged- Decision frequency: Daily batch
- Routes to the operator daily review queue
Finance lead
| Role | Decision | Frequency |
|---|---|---|
| Finance lead | Reviews flagged transactions and accepts or corrects category | Daily batch |
| Controller | Approves invoices above policy threshold via Slack | Per invoice |
| CFO | Edits and approves the auto-drafted investor update before it sends | Monthly |
| CFO | Signs off on scenario projections before they reach the board pack | Per scenario |
What changed for the operation.
4
Outcomes the operating team validated. Numbers below are anonymized to client policy.
Misclassification rate
~1%
down from ~10%
Time to monthly close
2 days
down from 7
Investor-update turnaround
hours
down from a week
CFO time on data wrangling
~10%
down from ~40%
We share the underlying breakdown in a discovery call under NDA.
What runs underneath.
Talk to us
Have an operation shaped like this one?
We'll start with a 2-week Discovery, fixed-fee, against your real systems and operating people. If we're not a fit we'll say so on the call.