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SAP + Home Retail: Built for Head of Procurement

Home retailers have 50,000+ SKUs and blind spots hiding in every store. Ward watches them all and delivers the findings your team doesn't have bandwidth to find. Your SAP Retail data holds answers nobody has time to extract. Ward reads it via read-only APIs.

Ward + SAP for Home Improvement

Home Improvement retailers running SAP Retail get AI-powered insight cards without custom development. Project-based purchasing, long-tail SKUs, and seasonal volatility. Ward manages the complexity of 50,000+ SKU environments with ease.

How it works: Ward reads from SAP via RFC/BAPI or OData APIs. No changes to your SAP configuration. Read-only access. Data syncs on your schedule.

Ward monitors 50,000+ SKUs across your stores and delivers automated insight cards covering Project basket value, Seasonal accuracy, Long-tail turn, and more.

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

Chat

Ask anything. Ward routes to the right agent and returns cited answers.

Why did Store 37 miss target last week?
You · 9:42 AM
Schema Scout · routed to Merchandising Agent

I pulled Store 37’s last 28 days against the chain baseline. Two root causes — both compounding.

SignalFinding
labor_efficiencyRev/labor-hour −22% vs. cluster — staffing mismatch at 11a–1p peak
inventory.freshFresh fill 83% — backroom replenishment lag at 2–4p
promo.liftBOGO crackers cannibalized Brand Y by 28% — net category +6%

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

8 parallel queries 3 sources cited confidence 0.92
Show me how to fix the staffing mismatch.
You · 9:43 AM
Labor Agent · drafting schedule diff
Querying labor_scheduling
Ask anything — Ward routes to the right agent. Cmd+K

Dashboards

Pinned views built from saved data-lake queries.

Revenue vs. forecast +4.2% WoW
Gross margin % −3.2pp
Fill rate — fresh 83%
Shrink — West region +0.8pp

Models

Browse, search, and manage data–lake model definitions for your tenant.

NameNamespaceVersion
retail_pos_transactionsretail1.0
retail_inventory_snapshotretail1.2
retail_labor_schedulingretail1.0
retail_promo_calendarretail1.1
retail_supplier_performanceretail1.0
sap_inventory_shrinkagesap1.0
ga4_daily_eventsmarketing1.0
meta_ads_ad_levelmarketing1.0

Sources

Connect external systems to the data lake.

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

Architecture

Two ways to connect. Federate against your live systems, or ingest into Ward’s data lake. Toggle below.

Your systems · read-only
SAP Retail
Snowflake
BigQuery
Shopify
Toast POS
Ward Gateway
TLS 1.3 · AES-256
Querying live · data stays put
Federated answers
SELECT * FROM sap.pos
JOIN snow.inventory
WHERE store_id = 37
→ insight cards
Ward Data Lake
→ baselined per store
TLS 1.3 in transit AES-256 at rest Read-only credentials SOC 2 II in progress VPC peering · PrivateLink

Pipelines

Move data from sources into models on a schedule.

NameSourceModelStatusSchedule
sync_sap_pos_transactionssap_pos_transactionspos_transactionsenabledhourly
sync_sap_labor_schedulingsap_labor_schedulinglabor_schedulingenableddaily
sync_sap_inventory_shrinkagesap_inventory_shrinkageinventory_shrinkageenableddaily
sync_retail_inventory_weeklyretail_inventory_weeklyinventory_weeklyenabledweekly
sync_retail_google_ads_dailyretail_google_ads_dailygoogle_ads_dailyenableddaily
sync_retail_ga4_website_dailyretail_ga4_website_dailyga4_website_dailyenableddaily

Streams

Real-time ingestion pipelines.

0events / min
0streams active
0% delivered
  • pos.txn store_037 — basket $42.18
  • inv.move dc_west → store_104
  • labor.clock store_022 shift_start
  • pos.txn store_211 — basket $19.04

Policies

Browse and manage Cedar access policies for your tenant.

TLS 1.3 AES-256 Read-only SOC 2 II
Policy IDEffectResources
merch-read-defaultpermitModel::*
finance-read-shrinkagepermitModel::"shrinkage"
vendor-blockedforbidModel::"labor_*"
region-west-onlypermitTenant::"acme"

Entities

Principals and resources referenced by Cedar policies.

Entity UIDTypeTenant
Tenant::"acme"Tenantacme
Model::"sap.pos_transactions"Modelacme
Model::"sap.inventory_shrinkage"Modelacme
Model::"sap.labor_scheduling"Modelacme
Model::"retail.toast_pos_daily"Modelacme
Model::"retail.ga4_website_daily"Modelacme

Providers

Manage LLM API keys and the model profiles that use them.

API Keys Model Profiles
NameProviderUsed byCreated
anthropic-defaultAnthropic3 profilesApr 22
openai-defaultOpenAI2 profilesApr 22
gemini-defaultGemini1 profileApr 22
ollama-onpremOllama2 profilesApr 22

LLM-agnostic. Bring your own key, route per task. No lock-in.

Settings

Manage your dashboard preferences and account.

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Account
NameAdmin
Emailadmin@acme.io
Tenantacme-retail
Live product demo — Ward analyzing retail data in real time.

What Ward delivers

  • MSA, DPA, SOC 2 II, and architecture review available before signature
  • Month-to-month contracts. No multi-year lock-in. No auto-renew traps
  • Transparent pricing tied to scope and store count, not seats or tokens
  • 14-day insight guarantee. If Ward doesn't deliver, month two is on us
  • Reference customers and a 850+ store live pilot operator you can interview

Metrics Ward monitors

Project basket value
Seasonal accuracy
Long-tail turn
Pro customer share
Attachment rate

Home challenges Ward solves

  • Project basket identification
  • Seasonal pre-positioning
  • Long-tail inventory
  • Pro vs DIY segmentation
  • Weather-driven demand

How Ward connects to SAP Retail

Ward connects to SAP Retail (S/4HANA, ECC, CAR) via standard BAPIs and IDocs. Transaction data, inventory positions, and master data flow into Ward without custom development.

Setup: Ward reads from SAP via RFC/BAPI or OData APIs. No changes to your SAP configuration. Read-only access. Data syncs on your schedule.

Data Ward reads from SAP

POS transactions
Inventory positions
Purchase orders
Material master
Vendor master
Promotion calendar

Impact metrics with SAP

Replenishment Accuracy
Gaps flagged early
Inventory positions matched to POS velocity before shelf impact.
Shrinkage
Anomalies surfaced continuously
Transaction-level detection catches what periodic audits miss.
Forecast Accuracy
External signals layered
Weather, events, and competitor data sharpen SAP demand signals.
Promo ROI
True lift isolated
Sell-through data exposes cannibalization and halo effects.

Data lake enrichment

Ward enriches SAP data with: POS transactions, Weather & events, Competitor pricing, Loyalty & CRM, Supplier fill rates

Merchandising wants Ward. You sign the contract.

Pain points
  • ×Business sponsor saw the demo. You have a week to vet a vendor you didn't pick
  • ×AI vendors price by seats and tokens. Total cost is unknowable until invoice three
  • ×Multi-year commits with auto-renew. No exit if the pilot stalls
  • ×Renewals come back 30% higher with no leverage and no benchmark
  • ×Security and DPA reviews start after the team has already committed
How Ward helps
  • MSA, DPA, SOC 2 II, and architecture review available before signature
  • Month-to-month contracts. No multi-year lock-in. No auto-renew traps
  • Transparent pricing tied to scope and store count, not seats or tokens
  • 14-day insight guarantee. If Ward doesn't deliver, month two is on us
  • Reference customers and a 850+ store live pilot operator you can interview

Enterprise SaaS spend grew 18% YoY. 53% of subscriptions are underused or duplicative. Source: Gartner

What a Ward insight card looks like

Ward · Home06:47 AM

Procurement packet: MSA, DPA, SOC 2 II report, architecture review, and reference contacts. Month-to-month. 14-day insight guarantee. Available before signature.

✓ Action recommendedHome context appliedSAP data

Home KPI impact

Seasonal Accuracy
Weather + event driven
Pre-positioning adjusted for peak season signals.
Long-Tail Turn
Dead weight separated
Which tail SKUs serve project needs vs sit idle.
Project Basket Value
Cross-sell surfaced
Project purchasing patterns drive attachment.
Inventory Carrying Cost
Capital freed
Demand forecasting reduces slow-moving overstock.

Frequently asked questions

Ward reads from SAP via RFC/BAPI or OData APIs. No changes to your SAP configuration. Read-only access. Data syncs on your schedule. Data points include: POS transactions, Inventory positions, Purchase orders, Material master, Vendor master, Promotion calendar.

Yes. Ward reads SAP data and combines it with contextual signals (weather, events, demographics) to generate Home-specific insight cards. No custom development required.

Merchandising wants Ward. You sign the contract. Ward solves this with automated insight cards: MSA, DPA, SOC 2 II, and architecture review available before signature. Month-to-month contracts. No multi-year lock-in. No auto-renew traps. Transparent pricing tied to scope and store count, not seats or tokens.

Ward delivers daily insight cards covering Project basket value, Seasonal accuracy, Long-tail turn — tailored for Procurement decision-making. Each card includes what changed, why it matters, and what to do next.

First insight cards arrive within 48 hours of data connection. Ward needs approximately 2 weeks to establish robust baselines for your specific operation.

No. Ward sits on top of your existing stack. It is the proactive intelligence layer that watches your data continuously and delivers insight cards — so your team acts on findings instead of hunting for them.

Ward
Insight
Dispatch
Feedback
Evaluate
Learn
01

Insights surface

Ward’s agents detect what changed, why it matters, and what to do about it. Every insight includes a recommended action. Not just a chart to interpret.

Real-time detection Root cause + recommendation
02

Insights become actions

Any insight card can be turned into a tracked ticket or task. Dispatched to the right person, on the right channel: mobile push, text, or email. Not every insight needs a ticket. When one does, it has an owner.

Tickets created automatically Dispatched to the right person
03

Your team responds

Insights get voted up or down with reasoning. Tickets get completed or rejected. Every response is a signal. Ward learns what worked, what missed, and why.

Vote up / down Ticket completed Reasoning attached
04

Outcomes measured

Ward evaluates real results: revenue, margin, fill rate, labor cost. Did the action actually improve the number it targeted? Measured outcomes, not assumptions.

KPI impact tracked Results vs. prediction scored
05

Agents get sharper

Every vote, every completed ticket, every measured outcome feeds back in. Ward learns from your team’s judgment and real-world results. Each cycle sharpens the next. Then it starts again.

Cycle repeats, sharper each time
$1.8T
Projected global AI market by 2030
0
×
Customer acquisition lift for data‑driven orgs
0
+
Foundation models shipped since 2022
0
Guarantees any single model stays on top

See what Home problems Ward catches.

Root causes, not just alerts. See it on your data.

Get a demo

Find out what your data has been hiding.

Tell us about your operation. We’ll show you the problems Ward catches — and the ones your current tools miss.

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