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Ward and NetSuite for Home technology

Your NetSuite feed covers 50,000+ SKUs across your stores. Ward reads it for technology.

Ward and NetSuite for Home technology

A Home Improvement operator is watching 50,000+ SKUs throughout stores. Project-based purchasing, long-tail SKUs, and seasonal volatility. Ward manages the complexity of 50,000+ SKU environments with ease.

The business wants AI. You sign off on the architecture. Ward filters to what a Head of IT can act on and drops the rest.

How the connection works. Ward connects via SuiteTalk REST or SOAP APIs. Token-based authentication. Read-only access to your NetSuite instance.

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

Chat

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

Why did the spring mulch pre-build miss in the Southeast?
You · 9:42 AM
Schema Scout · routed to Merchandising Agent

I pulled the spring pre-build against the last three seasons and the weather signal for the 22 Southeast stores.

SignalFinding
seasonal_prebuildMulch on-hand hit 61% of plan the week temps broke 70°F
dc_pushDC push started 9 days after the first-warm-week trigger, LY it was 2
attach.projectSoil and edging attach fell 18% at stores that gapped on mulch

Recommend: tie the push trigger to the 10-day forecast instead of the calendar week, pre-position two truckloads at the 8 stores that gapped, and re-set the attach endcap.

9 parallel queries 4 sources cited confidence 0.88
Which stores get the pre-position first?
You · 9:43 AM
Supply Chain Agent · ranking stores
Querying seasonal_prebuild
Ask anything, Ward routes to the right agent. Cmd+K

Reporting

Pinned views built from saved data-lake queries. Every number re-derivable from its SQL.

7d13w52w
Revenue vs. plan
$57.3M
+2.9% WoW
Gross margin, seasonal
31.2%
−2.4pp
Mulch on-hand vs plan
61.4%
−18pp
Special order cycle time
11.6 d
+3.1 days
Revenue vs. plan 13 weeks actual, 6 weeks forecast, 80% interval
Actual Forecast 80% interval
% of plan 106 94 100 forecast → W−13 W−5 today +6wk
Holt-Winters + weather regressor MAPE 4.1% at 4wk Backtested 24 months Crosses plan in 3 weeks
Forecast error by horizon MAPE, 24-month backtest
1wk 2.1%
2wk 3.0%
4wk 4.1%
8wk 6.3%
13wk 8.9%
Accuracy bar for promo decisions: ≤5% at 4wk
Models in production Every forecast ships a model card
ModelHorizonMAPE
holt_winters4wk4.1%
arima_sarimax13wk8.9%
gbm_demand1wk2.1%
bayes_hiernew store11.4%

Sources

Connect external systems to the data lake.

NameTypeLast sync
epicor_pos_transactionsimport2m ago
epicor_special_ordersimport2m ago
netsuite_inventory_snapshotimport14m ago
retail_seasonal_prebuildimport1h ago
retail_sku_velocityimport1h ago
retail_pro_account_salesimport1h ago
retail_weather_dailyimport1h ago

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::*
pro-team-read-accountspermitModel::"pro_account_sales"
vendor-blockedforbidModel::"labor_*"
supply-read-orderspermitModel::"special_orders"
for Home on NetSuite data, live product demo.

What Ward delivers

  • Federated query: data stays in your warehouse. No copies, no shadow lake
  • Read-only credentials. Cedar policies enforce least-privilege per agent
  • LLM-agnostic. Anthropic, OpenAI, Gemini, Ollama. Bring your own keys
  • Every query, every model, every source logged. SIEM-ready audit output
  • VPC peering, PrivateLink, SOC 2 II. Your security review is short

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

What Ward has eyes on.

Ward reads NetSuite rather than replacing it. Sales orders, inventory, purchase orders come across on a read-only connection, get enriched with contextual data, and come back as insight cards. Your ERP is untouched.

Every one of your stores gets its own baseline. Ward tracks project basket value, seasonal accuracy, long-tail turn against it and raises only the deviations that hold up. The two that show up most in home retail are project basket identification and seasonal pre-positioning, and both are baseline problems before they are P&L problems.

Why this combination
is its own problem.

Oracle NetSuite is the system of record for most home operators of your size, which means the inputs Ward needs are already there. The gap is not data collection. It is that nobody has time to read sales orders and inventory every morning across every store.

What the first 90 days
actually look like.

  1. 01

    Week 1: read-only connection

    Ward plugs into Oracle NetSuite with read-only credentials and begins ingesting sales orders and inventory. No config changes on your side. First insight cards arrive within 48 hours.

  2. 02

    Weeks 2 to 3: calibration

    Baselines stabilize per store and per category. Ward stops flagging normal variance and starts flagging exceptions. This is the window where the false positive rate drops sharply.

  3. 03

    Weeks 4 to 12: operating rhythm

    Cards arrive on a daily cycle and get triaged like any other queue. Most teams find the volume settles into something one person clears in ten minutes. What matters is the action rate, not the alert count.

How Ward connects to Oracle NetSuite

Ward integrates with NetSuite SuiteCommerce, inventory management, and financials. Mid-market retailers get enterprise-grade insight cards.

Setup: Ward connects via SuiteTalk REST or SOAP APIs. Token-based authentication. Read-only access to your NetSuite instance.

Data Ward reads from NetSuite

Sales orders
Inventory
Purchase orders
Customer records
Financial summaries
Item fulfillment

Impact metrics with NetSuite

Inventory Accuracy
Discrepancies reconciled live
POS and fulfillment data cross-checked against NetSuite counts.
Order Fill Rate
Stockouts preempted
Demand forecasting layered onto NetSuite purchase orders.
Gross Margin
Margin erosion flagged
Pricing drift and vendor cost creep caught across financials.
Cash Conversion Cycle
Days of supply reduced
Demand-inventory alignment frees tied working capital.

Data lake enrichment

Ward enriches NetSuite data with: Sales orders, Weather & events, Customer segments, Vendor performance, Market pricing data

The business wants AI. You sign off on the architecture.

Pain points
  • ×Business sponsor already chose the vendor. You inherit the security review
  • ×Every AI vendor wants write access and a copy of the production data
  • ×Model lock-in means rewriting the stack when GPT or Claude moves again
  • ×Audit trail is an afterthought. Compliance has nothing to pull on
  • ×Data lake project keeps getting bumped for the next thing the business wants
How Ward helps
  • Federated query: data stays in your warehouse. No copies, no shadow lake
  • Read-only credentials. Cedar policies enforce least-privilege per agent
  • LLM-agnostic. Anthropic, OpenAI, Gemini, Ollama. Bring your own keys
  • Every query, every model, every source logged. SIEM-ready audit output
  • VPC peering, PrivateLink, SOC 2 II. Your security review is short

74% of enterprise AI projects stall before production. Integration debt and security review are the top two reasons. Source: Gartner

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 connects via SuiteTalk REST or SOAP APIs. Token-based authentication. Read-only access to your NetSuite instance. Data points include: Sales orders, Inventory, Purchase orders, Customer records, Financial summaries, Item fulfillment.

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

The business wants AI. You sign off on the architecture. Ward solves this with automated insight cards: Federated query: data stays in your warehouse. No copies, no shadow lake. Read-only credentials. Cedar policies enforce least-privilege per agent. LLM-agnostic. Anthropic, OpenAI, Gemini, Ollama. Bring your own keys.

Ward delivers daily insight cards covering Project basket value, Seasonal accuracy, Long-tail turn, tailored for Technology 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 stable 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.

See what Home problems Ward catches.

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

Read-only to start · your LLM keys · SOC 2 Type II underway · or book a call directly

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