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Commerce · BigCommerce

BigCommerce plus Ward: demand forecasting

Ward reads orders, products & variants, customers from BigCommerce, then cross-references them with weather, events, and demographics to produce demand cards.

Request access BigCommerce connector reference
Read-only by default No copy of your data Ward AI drafts, you approve
01 · The connection

How Ward attaches to BigCommerce.

Ward connects via BigCommerce REST API with OAuth. Webhooks for real-time order and inventory events.

app.getward.ai Live demo
Acme Retail @Merchandising: VP Analyst claude-sonnet default
A

AI Insights

Agents run against your baselines overnight. These are what they flagged without being asked.

3 flagged 8 queries run 3 sources swept 04:00, acme-retail
Act now labor_efficiency 0.94

Rev/labor-hour −22% vs. cluster, staffing mismatch at 11a–1p peak

Schema Scout · routed to Merchandising Agent Pin Ask Ward Investigate
Review inventory.fresh 0.89

Fresh fill 83%, backroom replenishment lag at 2–4p

Promo Agent · routed via Merchandising Pin Ask Ward Investigate
Watch promo.lift 0.81

BOGO crackers cannibalized Brand Y by 28%, net category +6%

Margin Agent · routed via Finance Pin Ask Ward Investigate
Recommended

Re-baseline Store 37 schedule against true peak, raise replen window to 1p, and review the BOGO before next cycle.

Sources

Connect external systems. Ward AI maps the schema and drafts the cleaning rules.

Search sources…
NameTypeLast sync
sap_pos_transactionsimport2m ago
sap_inventory_shrinkageimport2m ago
sap_labor_schedulingimport14m ago
retail_inventory_weeklyimport1h ago
retail_google_ads_dailyimport1h ago
retail_meta_ads_dailyimport1h ago
retail_ga4_website_dailyimport1h ago

Pipelines

Move data from sources into models on a schedule. Describe the sync; Ward AI builds it.

NameSourceModelStatusSchedule
sync_sap_pos_transactionssap_pos_transactionspos_transactionsenabledhourly
sync_sap_inventory_shrinkagesap_inventory_shrinkageinventory_shrinkageenableddaily
sync_sap_labor_schedulingsap_labor_schedulinglabor_schedulingenableddaily
sync_retail_inventory_weeklyretail_inventory_weeklyinventory_weeklyenabledweekly
sync_retail_google_ads_dailyretail_google_ads_dailygoogle_ads_dailyenableddaily
sync_retail_meta_ads_dailyretail_meta_ads_dailymeta_ads_dailyenableddaily
Sources, then what Ward AI drafted from them, then what it computed. Click between the panels.
What Ward reads
OrdersProducts & variantsCustomersInventoryPromotionsStorefront analytics

Ward connects to BigCommerce for omnichannel retailers running headless or traditional storefronts. Orders, catalog, and customer data drive insight cards.

Scope

Credentials are read-only and locked to the objects you name. Ward cannot reach a table you did not grant it, and write access is opt-in per object rather than implied by the connection.

Residency

Ward queries BigCommerce in place and keeps no second copy. Your region, your retention policy, and no staging warehouse in the middle.

Drift

Every connector carries a schema contract. A source that changes shape fails the contract and stops the pipeline, rather than quietly writing wrong rows until someone notices the numbers moved.

02 · What it computes

See demand before it arrives.

Ward combines historical patterns, weather data, local events, and economic signals to forecast demand at the store-SKU-day level.

Ward builds store-level demand models incorporating seasonality, weather forecasts, promotional calendars, local events, and macroeconomic indicators.

  • Store-SKU-day level precision
  • Weather-driven adjustment
  • Event and holiday modeling
  • Automatic reorder point recalculation

Definitions resolve through the semantic layer, so a number here reconciles with the dashboard your team already publishes instead of disagreeing with it quietly. How the layer works →

Sample output

72-hour heat wave predicted for Dhaka region. Historical model suggests +18% on beverages, +12% on ice cream. Pre-position recommended.

Improving forecast accuracy by 10% can cut inventory costs by 5% on a typical chain. — Gartner

03 · Scope and audit

What the agent may read, and what it did last Tuesday.

Agents on your own data fail the security review, not the demo. The model is never the problem. The problem is that nobody can say what the agent is allowed to reach, what it already did, or what happens when it is wrong.

01

The agent sees the BigCommerce objects you declared on the source and nothing else. Scope is enforced at query time, not in the client.

02

Read-only until you grant a write action explicitly, per object. A grant leaves a record naming the agent, the caller and the rows it touched.

03

One audit log covers every person and every agent in the tenant, so a reviewer answers one question against one system rather than correlating two.

Governance and policy →
How a scope gets set

Describe the job in plain English. Ward AI proposes the agent and the exact permissions it would need against your BigCommerce objects, and the proposal sits there until a person approves it. Nothing runs on a draft.

Every finding it returns carries the SQL that produced it. If the query is wrong you can see that it is wrong, which is the only way a data team ever trusts one of these.

04 · Where it runs

The same pipeline, in three operating contexts.

What demand forecasting off BigCommerce looks like once it is live. Retail is where Ward is deployed, not what Ward is.

Grocery

30,000+ SKUs · stores

Ward detects a hurricane tracking toward your Florida market five days out and maps the predictable surge sequence: water and batteries first, then canned goods and bread, then cleanup supplies post-event. Ward issues phased demand adjustment cards store by store based on distance from projected landfall, avoiding both panic stockouts and post-storm overstock write-offs.

The grocery build →

Convenience

3,000+ SKUs · locations

A highway on-ramp closure reroutes commuters past some of your stores and away from others. Within 48 hours, Ward detects the shift: stores on the new route are depleting morning coffee and breakfast by mid-morning while stores that lost traffic are over-ordering and generating waste. Ward issues demand adjustment cards for all affected locations with revised quantities for the construction period.

The convenience build →

Fashion

15,000+ SKUs · locations

The buying team is finalizing quantities for hundreds of new fall styles with no sell-through history. Ward maps each to attribute clusters from prior seasons and adjusts for current trend velocity. The result is store-cluster-level buy recommendations that materially reduce first-allocation error, meaning fewer stockouts on winners and less dead inventory on misses.

The fashion build →
05 · Who consumes it

You stand it up. They read the output.

Your platform team owns the connection, the definitions and the policy. These are the teams that consume what comes out of it.

VP Supply Chain

Supply Chain

Store-SKU-day forecasts that fold in weather, events, and macro signals.

What they see →

VP Merchandising

Merchandising

Promo and seasonal lift factored into category-level planning.

What they see →

CFO

Finance

Forecast accuracy improvements compound into working capital savings.

What they see →
06 · What changes

What connecting BigCommerce actually moves.

MeasureEffectWhy
Sell-Through Rate Velocity tracked live Slow movers flagged early enough to reallocate inventory.
Customer LTV Churn risk identified Cohort analysis surfaces lapsing buyers and re-engagement timing.
Conversion Rate Buyer vs browser split Patterns that convert separated from those that just browse.
Inventory Turnover Reorder cadence optimized Demand signals calibrate reorder points across the catalog.
Questions

What a technical evaluation asks first.

Ward connects via BigCommerce REST API with OAuth. Webhooks for real-time order and inventory events.

Ward reads Orders, Products & variants, Customers, Inventory, Promotions and Storefront analytics. Credentials are read-only and scoped to the objects you name, so Ward cannot reach a table you did not grant it.

No. Ward queries BigCommerce in place and holds no second copy of your data. Nothing is exported to run Demand Forecasting, and your residency and retention policy are unchanged by connecting Ward.

Every connector carries a schema contract. When a source changes shape the contract fails loudly and the pipeline stops, rather than writing wrong rows into your warehouse for a week before anyone notices the numbers moved.

Ward combines historical patterns, weather data, local events, and economic signals to forecast demand at the store-SKU-day level. Ward builds store-level demand models incorporating seasonality, weather forecasts, promotional calendars, local events, and macroeconomic indicators.

Yes. Every figure Ward reports carries the query that produced it, resolved through the semantic layer, so it reconciles with the dashboard your team already publishes rather than disagreeing with it quietly.

Only what you declared. An agent is scoped to named tables and cannot reach past them, it is read-only unless you grant a write action explicitly, and every query, tool call and result is logged against the agent identity that made it.

Typically VP / Director of Supply Chain, VP / Director of Merchandising and CFO / VP Finance. They read the output; your platform team owns the connection, the definitions and the policy that produced it.

Ward detects a hurricane tracking toward your Florida market five days out and maps the predictable surge sequence: water and batteries first, then canned goods and bread, then cleanup supplies post-event. Ward issues phased demand adjustment cards store by store based on distance from projected landfall, avoiding both panic stockouts and post-storm overstock write-offs.

Explore

Demand Forecasting on another system.

SAP Oracle Shopify NetSuite Snowflake BigQuery Blue Yonder RELEX Looker Power BI Tableau NCR Lightspeed Epicor

What else Ward computes off BigCommerce.

Stockout Prediction Shrinkage Detection Price Optimization Assortment Planning Promo Effectiveness Customer Behavior Fill Rate Monitoring
BigCommerce connector → Demand overview → All 540+ connectors → Every pairing →

Point Ward at your BigCommerce and see what comes back.

Deploying is free. You pay 5% of the model compute it uses, and nothing else. No call required to find out whether this works on your data.

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