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ERP · Oracle

Your Oracle feed, read as pricing

Your Oracle data is already good enough for pricing. Ward ingests it and hands you findings with the cause attached.

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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 Oracle Retail.

Ward reads from Oracle Retail via REST APIs or direct database views. Compatible with Oracle Cloud and on-premise deployments.

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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
Sales auditInventory positionsAllocationReplenishmentDemand forecastsPrice management

Ward integrates with Oracle Retail Merchandising (RMFCS), Oracle Retail Demand Forecasting, and Oracle Retail Analytics. Full stack visibility.

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 Oracle 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

Price on elasticity you can measure.

Ward monitors price elasticity shifts in real time and recommends adjustments that protect margin without sacrificing volume.

Ward continuously measures price elasticity by category, tracks competitive pricing signals, and models the margin-volume tradeoff.

  • Real-time elasticity measurement
  • Category-level price sensitivity
  • Competitive price monitoring
  • Margin-volume tradeoff modeling

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

Dairy category showing -1.4 elasticity this week vs -0.8 baseline. Consumers responding to price changes 75% more than normal.

A 1% improvement in pricing yields an 11% profit improvement, on average. — McKinsey

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 Oracle 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 Oracle 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 price optimization off Oracle looks like once it is live. Retail is where Ward is deployed, not what Ward is.

Grocery

30,000+ SKUs · stores

A national chain drops private-label bread prices in your market. Ward detects the shift within 24 hours and models impact: nearby stores show a traffic decline among bread buyers who also carry full baskets. Ward recommends matching on the highest-velocity bread SKUs while raising prices on complementary deli items where elasticity is low, recovering traffic with a net-positive margin result.

The grocery build →

Convenience

3,000+ SKUs · locations

Ward segments 3,000 SKUs into price-awareness tiers: KVIs where customers compare, moderate-awareness items, and low-awareness categories like automotive and seasonal. Ward recommends holding KVI prices while implementing small increases on low-awareness items. Pilot stores show zero volume decline on adjusted items with meaningful weekly margin gains.

The convenience build →

Fashion

15,000+ SKUs · locations

Mid-season, Ward identifies dozens of styles selling below plan and segments them by severity: some need immediate deep markdown, others need moderate discounts, and a group should hold price because they're trending toward natural clearance. This tiered approach recovers significant margin versus the standard blanket-markdown playbook.

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 Merchandising

Merchandising

Elasticity shifts caught in real time. Margin protected without volume loss.

What they see →

CFO

Finance

Where price is leaving money on the table, and where moves would burn volume.

What they see →

Head of E-Com

E-Commerce

Competitive price tracking with elasticity measured per channel.

What they see →
06 · What changes

What connecting Oracle actually moves.

MeasureEffectWhy
Fill Rate Allocation gaps caught Replenishment outputs checked against actual shelf conditions per store.
Demand Forecast Accuracy Accuracy gap closed External signals enrich Oracle forecasts where they drift.
Markdown Waste Slow movers caught early Triggers shallower markdowns before inventory ages out.
Inventory Carrying Cost Overstock freed up Demand-aligned inventory releases locked working capital.
Questions

What a technical evaluation asks first.

Ward reads from Oracle Retail via REST APIs or direct database views. Compatible with Oracle Cloud and on-premise deployments.

Ward reads Sales audit, Inventory positions, Allocation, Replenishment, Demand forecasts and Price management. 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 Oracle in place and holds no second copy of your data. Nothing is exported to run Price Optimization, 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 monitors price elasticity shifts in real time and recommends adjustments that protect margin without sacrificing volume. Ward continuously measures price elasticity by category, tracks competitive pricing signals, and models the margin-volume tradeoff.

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 Merchandising, CFO / VP Finance and Head of E-Commerce / Digital. They read the output; your platform team owns the connection, the definitions and the policy that produced it.

A national chain drops private-label bread prices in your market. Ward detects the shift within 24 hours and models impact: nearby stores show a traffic decline among bread buyers who also carry full baskets. Ward recommends matching on the highest-velocity bread SKUs while raising prices on complementary deli items where elasticity is low, recovering traffic with a net-positive margin result.

Explore

Price Optimization on another system.

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

What else Ward computes off Oracle.

Stockout Prediction Shrinkage Detection Demand Forecasting Assortment Planning Promo Effectiveness Customer Behavior Fill Rate Monitoring
Oracle connector → Pricing overview → All 540+ connectors → Every pairing →

Point Ward at your Oracle 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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