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BI · Power BI

Stockout Prediction, powered by Power BI

Know before the shelf empties. Ward runs it directly off your Power BI feed, read-only, with no changes to your BI.

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

How Ward attaches to Microsoft Power BI.

Ward connects to the same data sources Power BI uses. Or reads Power BI datasets via REST API. Your reports stay untouched.

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
Power BI REST API datasetsUnderlying SQL/Azure dataDataflow outputs

Ward sits alongside Power BI. Your dashboards visualize. Ward detects and explains what changed. No dashboard login needed for your morning brief.

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 Power BI 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

Know before the shelf empties.

Ward detects SKUs trending toward zero-on-hand and alerts your team with replenishment recommendations before customers notice.

Ward analyzes sell-through velocity, current inventory levels, lead times, and supplier reliability to predict stockouts 24-72 hours before they occur.

  • Reduce lost sales by catching gaps early
  • Automated replenishment recommendations
  • Supplier-aware lead time modeling
  • Priority ranking by revenue impact

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

23 SKUs trending toward zero-on-hand within 48 hours. Replenishment recommendation attached. Priority: dairy and produce categories.

Stockouts cost retailers $1.14 trillion in missed sales globally each year. — IHL Group

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 Power BI 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 Power BI 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 stockout prediction off Power BI looks like once it is live. Retail is where Ward is deployed, not what Ward is.

Grocery

30,000+ SKUs · stores

Ward detects organic whole milk selling well above forecast across 23 Northeast stores as a heat wave spikes smoothie demand. Current DC allocation will leave 14 stores empty by Saturday. Ward issues a stockout prediction card Thursday afternoon with a recommended emergency PO and store-level reallocation plan, and the buying team acts before the weekend rush.

The grocery build →

Convenience

3,000+ SKUs · locations

Ward detects energy drink velocity running well above normal at university-adjacent stores during homecoming weekend, an event its model picked up from local data. Standard delivery won't replenish until Monday. Ward issues stockout prediction cards for the affected stores and recommends emergency redistribution from lower-velocity suburban locations to protect weekend revenue.

The convenience build →

Fashion

15,000+ SKUs · locations

Ward detects a spring jacket selling far above plan in key sizes at urban stores while sitting in suburban locations. At current velocity, the hot sizes will stock out well before end of season. Ward recommends inter-store transfers from underperforming locations to high-velocity stores, recovering full-price sales that would otherwise become end-of-season markdowns.

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

Replenishment exceptions auto-prioritized by revenue at risk.

What they see →

Director Store Ops

Store Operations

Morning brief flags which stores need attention before opening.

What they see →

VP Merchandising

Merchandising

Category-level fill protects promo lift and basket size.

What they see →
06 · What changes

What connecting Power BI actually moves.

MeasureEffectWhy
Time to Insight Push, not pull Insight cards delivered without waiting for someone to look.
Anomaly Detection Between-refresh coverage Issues surfaced before the next scheduled Power BI review.
Decision Velocity Cause analysis included No drill-down investigation; cards carry root cause context.
Report Efficiency Ad-hoc requests reduced Proactive cards answer questions before analysts get asked.
Questions

What a technical evaluation asks first.

Ward connects to the same data sources Power BI uses. Or reads Power BI datasets via REST API. Your reports stay untouched.

Ward reads Power BI REST API datasets, Underlying SQL/Azure data and Dataflow outputs. 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 Power BI in place and holds no second copy of your data. Nothing is exported to run Stockout Prediction, 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 detects SKUs trending toward zero-on-hand and alerts your team with replenishment recommendations before customers notice. Ward analyzes sell-through velocity, current inventory levels, lead times, and supplier reliability to predict stockouts 24-72 hours before they occur.

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, Director of Store Operations and VP / Director of Merchandising. They read the output; your platform team owns the connection, the definitions and the policy that produced it.

Ward detects organic whole milk selling well above forecast across 23 Northeast stores as a heat wave spikes smoothie demand. Current DC allocation will leave 14 stores empty by Saturday. Ward issues a stockout prediction card Thursday afternoon with a recommended emergency PO and store-level reallocation plan, and the buying team acts before the weekend rush.

Explore

Stockout Prediction on another system.

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

What else Ward computes off Power BI.

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

Point Ward at your Power BI 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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