NCR · Head of IT

What a Head of IT gets out of NCR

Your POS has POS transactions and item-level sales. A Head of IT needs the two-line version. Ward writes it.

NCR Voyix, read for technology

The business wants AI. You sign off on the architecture. Ward writes the finding at the altitude a Head of IT works at.

How the connection works. Ward reads NCR transaction data via API or data export. Real-time or batch, depending on your NCR configuration.

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

Reporting

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

7d13w52w
Revenue vs. forecast
$48.2M
+4.2% WoW
Gross margin %
24.1%
−3.2pp
Fill rate, fresh
83.4%
−4.1pp
Shrink, West region
2.41%
+0.8pp
Revenue vs. forecast 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
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

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::"inventory_shrinkage"
vendor-blockedforbidModel::"labor_*"
region-west-onlypermitTenant::"acme"
on NCR 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

Data Ward reads from NCR

POS transactions
Item-level sales
Tender data
Daypart summaries
Loyalty data

Why this combination
is its own problem.

NCR Voyix was built for transactions, not for technology decisions. The data is right there and it is in the wrong shape. Ward pulls from POS transactions and item-level sales and rewrites them as a decision.

What Ward has eyes on.

Ward reads straight from POS transactions, item-level sales, tender data from NCR Voyix on a read-only connection. Nothing is written back, and your POS configuration does not change. NCR stays the system of record.

What the first 90 days
actually look like.

  1. 01

    Week 1: connect

    Read-only credentials to NCR Voyix. Ward reads straight from POS transactions, item-level sales, tender data and starts building baselines. First findings land inside 48 hours, before baselines are stable, so you can see the shape of the output early.

  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 every morning 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 NCR Voyix

Ward integrates with NCR Voyix POS and Aloha for convenience and restaurant retail. Transaction-level data powers daypart analysis and impulse optimization.

Setup: Ward reads NCR transaction data via API or data export. Real-time or batch, depending on your NCR configuration.

Data Ward reads from NCR

POS transactions
Item-level sales
Tender data
Daypart summaries
Loyalty data

Impact metrics with NCR

Attach Rate
Adjacencies mapped per daypart
Impulse cross-sell patterns identified by time of day.
Daypart Revenue
Underperforming hours exposed
Traffic and weather data pinpoint revenue-light dayparts.
Shrinkage
Slow-bleed loss detected
POS anomaly patterns caught that periodic audits miss.
Basket Size
Bundle opportunities surfaced
Item-level sales mined for actionable upsell patterns.

Data lake enrichment

Ward enriches NCR data with: POS transactions, Weather & events, Loyalty data, Competitor proximity, Demographic 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

Frequently asked questions

Ward reads NCR transaction data via API or data export. Real-time or batch, depending on your NCR configuration. Data points include: POS transactions, Item-level sales, Tender data, Daypart summaries, Loyalty data.

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.

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 your stores are hiding.

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