Autonomous AI discovery built for omnichannel retail.
Continuous AI discovery across your channel, SKU, store, and loyalty data. Every finding ships with the underlying queries, the cohort definition, and the cost of the run. The cohort-level merchandise questions that never make it past the weekly KPI deck, answered before your next trading review.
Built for Chief Merchants, Heads of Stores, VPs of Merchandising, and Heads of Data at DTC brands, brick-and-digital chains, and true omnichannel retailers.
The cohort-level merchandise questions that never make it past the weekly KPI deck, running overnight against your data.
Your team owns the assortment, the channel mix, the store roster, the loyalty program. The agent runs unattended against your channel, SKU, store, and loyalty data — and ships findings into a queue your team triages. Every claim carries the underlying queries and the data-validated number, not the POS-reported or BI-dashboard-reported one.
See how the agent reads channel × SKU × storeTotal sales look fine. The cohort underneath is where the margin actually moved.
Three benchmarks every merchant and stores leader recognizes. Each points to the same gap: your data already holds the signals — channel margin drift, inventory turn anomalies, loyalty cohort behavior — but they only surface when somebody asks the right question. Most weeks, nobody does.
Three findings the agent surfaced, paired with the recommendation it shipped.
Three shapes the merchant team sees every quarter: a single-channel margin cliff the aggregate concealed, a cross-system inventory turn anomaly the demand dashboard couldn't see, and a loyalty cohort drift the aggregate visit frequency averaged into a flat line. Same auditable trail every time — severity, the underlying queries, the cohort definition, the cost of the run.
Point the agent at your channel and store data for a week. Triage what it finds Monday morning.
Five surfaces the team already owns, one continuous discovery.
Same engine, same data, five categories of work your team is already doing — or wants to do, if the queue ever clears. Pick the surface that matters this quarter.
The channel that's quietly losing margin, on the same SKUs you sell everywhere else.
Contribution margin per unit across direct.com, marketplaces, stores, and any other channel in your stack — on the same SKUs, in the same window. The agent strips out mix and pricing effects, then surfaces the fee, fulfillment, and return-handling differences that move margin. Every number is checked twice against your data before it ships. The deep dive lives in how the agent decides what to investigate.
- Contribution margin decomposition by channel and SKU
- Fee stack and fulfillment surcharge drift detection
- Returns-rate gap by channel and product category
- Promo lift vs. promo cost by channel
- Marketplace ad spend vs. organic contribution
Built for retailers who want a cohort analyst running overnight, not three dashboards to reconcile.
Three things every finding from DecisionBox carries. None of them changes how your team works in your POS, your merchandise planning tool, your loyalty platform, or whatever data tools and BI you already standardized on.
Sees what nobody had time to query for
The agent picks which cohorts, channels, SKUs, stores, and loyalty tiers to investigate from your channel, SKU, store, and loyalty data. Findings ship into a queue your team triages in the morning. The cohort-level merchandise question stops sitting in the data waiting for the next QBR.
Every number you'd defend at the trading review
The underlying queries the agent ran, the cohort definition, the data window, the cost of the run, the validation checks. Your team re-runs anything, extends anything, overrides anything. No black boxes, no POS-reported or BI-dashboard-reported numbers the data can't validate, no "trust the AI."
Reads your data, not just the POS and the merchandise planning tool
Transactions plus inventory, fulfillment, loyalty, and promo activity — read together. The patterns the POS can't see (because it only sees one channel's transactions) and the patterns the merchandise planning tool can't see (because it works on forecasts, not actuals) surface when your data is read end-to-end. Snowflake, BigQuery, Databricks, Redshift, PostgreSQL. Open source, AGPL v3. The agent meets you on the stack you already run.