A multi-banner CPG portfolio runs trade promotion, sell-through, and deduction data through a different retail partner system for every banner, reconciled by hand into a weekly export that finance uses to decide which deductions to dispute. By the time a mismatched deduction surfaces in that export, the dispute window with the retailer is already closing, and revenue that should have been recovered quietly becomes a write-off instead.
QuaerisAI connects to each banner’s trade and point-of-sale systems directly and resolves trade spend, sell-through, and deduction questions through one certified definition, applied the same way across every region and category. Deductions that do not match the original trade plan surface as they happen, not at the next scheduled reconciliation, giving the finance team a real window to dispute them before the write-off is final.
A CPG portfolio selling through multiple retail banners inherits a different data relationship with each one: different point-of-sale feeds, different deduction codes, different reporting cadences. Trade promotion spend gets planned centrally, but the deductions that retailers take against it get processed independently by each banner, and the two rarely reconcile automatically.
The result is a familiar cycle: deductions accumulate for weeks before anyone compiles them into a single view, and by the time finance can see which ones don’t match the original trade plan, the retailer’s window to dispute an incorrect deduction has often already closed. What should have been a recoverable revenue leak becomes a permanent write-off, not because the deduction was valid, but because nobody could see it in time to challenge it.
QuaerisAI connects directly to each banner’s trade management and point-of-sale systems and resolves trade spend, sell-through, and deduction questions through a certified semantic layer, so a deduction is evaluated against the same trade plan definition no matter which retailer processed it.
Because the platform reads data where it already lives rather than waiting for a scheduled export, a deduction that does not match its trade plan can surface within the same cycle it was taken, while there is still time to dispute it. Category and regional teams query the same governed data, each scoped to the banners and categories their role covers.
“We used to find out a deduction was wrong the same week we’d already missed the window to fight it. Now we see the mismatch before the dispute deadline, not after.”
Illustrative example, not a real customer quote
Instead of rebuilding the reconciliation process by hand for every banner, category teams work from one governed pipeline that already knows how to compare a deduction against the plan it was supposed to match.
| Metric | Before QuaerisAI | With QuaerisAI |
|---|---|---|
| Deduction visibility | Weekly batch export | Live, as deductions are taken |
| Trade spend definition | Varies by banner and analyst | One certified definition, portfolio-wide |
| Dispute window | Frequently missed | Flagged while still open |
| Sell-through reporting | Reconciled manually per banner | Certified, consistent across banners |
| Access control | Shared spreadsheets, unscoped | Role-based, enforced at query time |
| Lineage | Difficult to reconstruct | Source-traceable by default |
| Auditability | Assembled after the fact | Logged automatically per query |
| Answer speed | Days, tied to export schedule | Immediate, on demand |
QuaerisAI gives category and finance teams a governed way to ask the questions that used to wait for the next export:
Closing the visibility gap on deductions does not eliminate deductions, retailers will always take them, and many are legitimate. What it changes is whether a finance team has time to tell the difference before the dispute window closes.
The next step for most multi-banner portfolios is extending the same certified trade spend definitions into demand planning, so promotion effectiveness and deduction accuracy are evaluated from the same governed view instead of two separate processes that rarely agree.
Talk to a Quaeris solutions engineer. We'll walk through your warehouse setup, your governance requirements, and show you a live governed answer - against your own data schema if possible.
