Reconcile a media invoice against the insertion order and delivery
A line-by-line check of a vendor invoice against the IO and ad server data, with discrepancies, makegoods, and a dispute note.
The prompt
Help me reconcile a media vendor invoice before we pay it and bill the client.
Insertion order terms: [PASTE OR SUMMARIZE - placements, flight dates, rate type (CPM, CPV, flat), booked units, net cost, guarantees such as viewability or audience]
Vendor invoice: [PASTE LINE ITEMS]
Third-party delivery data: [AD SERVER OR VERIFICATION REPORT - impressions, clicks, views, viewability, IVT]
Discrepancy threshold in our terms: [e.g. "we pay on ad server counts if the gap is over 10%"]
Agency commission and billing basis: [e.g. "bill client gross, 15% commission"]
For each line, check:
- Billed units vs booked units vs third-party delivered units.
- Rate billed vs rate on the IO.
- Delivery outside the flight dates or on placements not booked.
- Under-delivery that should trigger a makegood or credit.
- Over-delivery billed beyond the IO cap.
- Invalid traffic or non-viewable impressions billed against a guarantee.
- Math errors in net, gross, commission, and taxes.
Return:
1. A reconciliation table: line | booked | vendor-reported | third-party | variance % | billable amount | action (pay / short-pay / dispute / request makegood).
2. Total invoice vs total amount we should pay.
3. A short, factual email to the vendor listing each discrepancy and what we are asking for.
4. A note for the client bill explaining any credits or adjustments.
Show your working on every number. Do not assume figures that are not in my data; mark missing items [NEED DATA].
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Finance & Accounting prompts
What to fill in
- PASTE LINE ITEMS
- e.g. "we pay on ad server counts if the gap is over 10%"
- e.g. "bill client gross, 15% commission"
- NEED DATA
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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