Oracle · Head of IT

What a Head of IT gets out of Oracle

Oracle data, read for technology. Ward traces what moved and what to do, every morning.

Ward and Oracle for Head of IT / VP Technology

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

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 Oracle 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 Oracle

Sales audit
Inventory positions
Allocation
Replenishment
Demand forecasts
Price management

Why this combination
is its own problem.

A Head of IT rarely logs into Oracle. They read what someone else pulled out of it, two days later. Ward removes the two days and the someone else.

What Ward has eyes on.

The connection to Oracle Retail is read-only and runs on your schedule. Ward reads sales audit, inventory positions, and the rest of the feed, then joins it with external context your ERP does not carry: weather, local events, competitor pricing.

What the first 90 days
actually look like.

  1. 01

    Week 1: read-only connection

    Ward reads from Oracle Retail with read-only credentials and begins ingesting sales audit and inventory positions. No config changes on your side. First 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: steady state

    Ward returns daily cards each day, each with root cause and what to do about it. Volume settles at a level a single person can read over coffee. The measure of success is not how many insight cards arrive, it is how many get acted on.

How Ward connects to Oracle Retail

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

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

Data Ward reads from Oracle

Sales audit
Inventory positions
Allocation
Replenishment
Demand forecasts
Price management

Impact metrics with Oracle

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.

Data lake enrichment

Ward enriches Oracle data with: Sales audit data, Weather & events, Competitor pricing, Demographic data, Supplier scorecards

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 from Oracle Retail via REST APIs or direct database views. Compatible with Oracle Cloud and on-premise deployments. Data points include: Sales audit, Inventory positions, Allocation, Replenishment, Demand forecasts, Price management.

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