Furniture Manufacturing & Retail · 10,000+ SKUs

Ward for
Furniture retail.

Disconnected ERP, warehouse, and POS systems. Custom/configurable SKUs that break standard reporting. ERP-locked production data, long lead times, and margin erosion you don't see until quarter-end. Ward connects your internal systems and surfaces what matters.

Furniture manufacturers operate on 3–6% net margins. A single quarter of misread demand or buried inventory data costs more than most analytics budgets.Source: IBISWorld / Furniture Today
Modern furniture showroom with curated product displays
Inventory carrying cost Order-to-delivery cycle Gross margin by channel

Problems Furniture operators
find too late.

  • Disconnected ERP, warehouse, and POS systems
  • Custom/configurable SKUs that break standard reporting
  • 8–16 week lead times with no demand signal
  • Raw material cost drift invisible until P&L close
  • Channel mix shift between showroom, e-commerce, and wholesale
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
Ward analyzing Furniture retail data. Live product, real data lake.

Where Furniture operators
leave money on the table.

These are the KPIs Ward monitors. Here is what changes when someone is actually watching.

Inventory Carrying Cost
Aged stock flagged
Slow-moving SKUs identified before carrying costs compound.
Order-to-Delivery Cycle
Bottleneck visibility
Cycle time tracked by production stage against baselines.
Gross Margin
Real-time by channel
Material cost drift detected as it happens, not at P&L close.
Stockout Frequency
Advance warning
POS and e-commerce signals feed back into production.
Important caveats

Ward requires 2–3 production cycles to baseline order flow and cost patterns. ERP data quality is the single biggest variable in time-to-value.

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 Furniture operators are missing.

Ward finds the margin leaks, shrinkage patterns, and promo misfires your reports don’t surface.

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.

Step 1 of 3
What are your goals?
Step 2 of 3
About your operation
Step 3 of 3
Your contact info