Your P&L surprises come from the store floor, not the market.
Your finance team has the data. They don’t have the bandwidth to find what’s buried in it. Ward delivers the findings, with root causes attached. Click any number to see the SQL.
What finance finds out
too late.
Your P&L surprises come from the store floor, not the market.
Margin erosion is discovered at month-end close, not in real time
Inventory carrying costs are a black box
Working capital tied up in slow-moving stock nobody is watching
Same-store sales comps lack decomposition into actionable drivers
Capex decisions for store remodels lack unit-economics evidence
Source: IHL Group
Insight cards for
cfo.
- ×Margin erosion is discovered at month-end close, not in real time
- ×Inventory carrying costs are a black box
- ×Working capital tied up in slow-moving stock nobody is watching
- ×Same-store sales comps lack decomposition into actionable drivers
- ✓GMROI tracking by category with weekly insight cards
- ✓Inventory carrying cost alerts when capital efficiency drops
- ✓Working capital optimization recommendations based on turnover trends
- ✓SSS decomposition into traffic, conversion, and basket components
GMROI tracking by category with weekly insight cards
Inventory carrying cost alerts when capital efficiency drops
Working capital optimization recommendations based on turnover trends
SSS decomposition into traffic, conversion, and basket components
Store-level unit economics cards for capex prioritization
From signature
to running insights.
A live pilot for cfo hits these milestones on real data, on a fixed-fee schedule.
This is what Ward
delivers to you.
Chat
Ask anything. Ward routes to the right agent and returns cited answers.
I pulled Store 37’s last 28 days against the chain baseline. Two root causes, both compounding.
| Signal | Finding |
|---|---|
labor_efficiency | Rev/labor-hour −22% vs. cluster, staffing mismatch at 11a–1p peak |
inventory.fresh | Fresh fill 83%, backroom replenishment lag at 2–4p |
promo.lift | BOGO 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.
labor_scheduling…
Dashboards
Pinned views built from saved data-lake queries.
Models
Browse, search, and manage data–lake model definitions for your tenant.
| Name | Namespace | Version |
|---|---|---|
retail_pos_transactions | retail | 1.0 |
retail_inventory_snapshot | retail | 1.2 |
retail_labor_scheduling | retail | 1.0 |
retail_promo_calendar | retail | 1.1 |
retail_supplier_performance | retail | 1.0 |
sap_inventory_shrinkage | sap | 1.0 |
ga4_daily_events | marketing | 1.0 |
meta_ads_ad_level | marketing | 1.0 |
Sources
Connect external systems to the data lake.
| Name | Type | Last sync |
|---|---|---|
sap_pos_transactions | import | 2m ago |
sap_inventory_shrinkage | import | 2m ago |
sap_labor_scheduling | import | 14m ago |
retail_inventory_weekly | import | 1h ago |
retail_google_ads_daily | import | 1h ago |
retail_meta_ads_daily | import | 1h ago |
retail_ga4_website_daily | import | 1h ago |
Architecture
Two ways to connect. Federate against your live systems, or ingest into Ward’s data lake. Toggle below.
sap.possnow.inventoryPipelines
Move data from sources into models on a schedule.
| Name | Source | Model | Status | Schedule |
|---|---|---|---|---|
sync_sap_pos_transactions | sap_pos_transactions | pos_transactions | enabled | hourly |
sync_sap_labor_scheduling | sap_labor_scheduling | labor_scheduling | enabled | daily |
sync_sap_inventory_shrinkage | sap_inventory_shrinkage | inventory_shrinkage | enabled | daily |
sync_retail_inventory_weekly | retail_inventory_weekly | inventory_weekly | enabled | weekly |
sync_retail_google_ads_daily | retail_google_ads_daily | google_ads_daily | enabled | daily |
sync_retail_ga4_website_daily | retail_ga4_website_daily | ga4_website_daily | enabled | daily |
Streams
Real-time ingestion pipelines.
pos.txnstore_037, basket $42.18inv.movedc_west → store_104labor.clockstore_022 shift_startpos.txnstore_211, basket $19.04
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-default | permit | Model::* |
finance-read-shrinkage | permit | Model::"shrinkage" |
vendor-blocked | forbid | Model::"labor_*" |
region-west-only | permit | Tenant::"acme" |
Entities
Principals and resources referenced by Cedar policies.
| Entity UID | Type | Tenant |
|---|---|---|
Tenant::"acme" | Tenant | acme |
Model::"sap.pos_transactions" | Model | acme |
Model::"sap.inventory_shrinkage" | Model | acme |
Model::"sap.labor_scheduling" | Model | acme |
Model::"retail.toast_pos_daily" | Model | acme |
Model::"retail.ga4_website_daily" | Model | acme |
Providers
Manage LLM API keys and the model profiles that use them.
| Name | Provider | Used by | Created |
|---|---|---|---|
anthropic-default | Anthropic | 3 profiles | Apr 22 |
openai-default | OpenAI | 2 profiles | Apr 22 |
gemini-default | Gemini | 1 profile | Apr 22 |
ollama-onprem | Ollama | 2 profiles | Apr 22 |
LLM-agnostic. Bring your own key, route per task. No lock-in.
Settings
Manage your dashboard preferences and account.
Light and dark themes are available. Your choice is remembered per browser.
GMROI dropped from $3.10 to $2.65 in frozen foods this quarter. Cause: 3 new SKUs added in Q2 are turning at 40% below category average. Recommend rationalization review.
Outcomes for cfo.
Without putting IT on the hook.
The fastest way to kill a retail AI deal: an agent with write access to production data and no audit trail.
Ward starts read-only, runs on policy, and logs every query. Your security review is short. Your data team isn’t carrying the risk. Cyber and tech E&O on file with an AI rider.
The blind spots that cost
cfo the most.
KPIs that erode quietly when nobody’s watching. Flip to see what Ward does about each one.
CFO
across retail verticals.
What cfo
uses Ward for first.
The three insight types this role drives the most value from. Each one tailored for finance decision-making.
Insight cards for
cfo.
Every insight type, tailored for finance decision-making.
The KPIs Ward moves
for cfo.
The platform pieces
this role leans on.
Integrations for
finance.
Operator stories
and the alternatives.
Two ways to start.
Run a fixed-fee pilot on your data, or talk to advisory about a broader engagement.
Playbooks for Finance.
The procedures this team runs on Ward. Each pairs a data insight with the AI automation and automated data routing to act on it.
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.
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.
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.
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.
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.
Control AI spend by department and user.
Without skimping on the outcome.
Give every department and every user a compute budget. Ward routes each question to the cheapest model that clears the quality bar, so finance caps the spend without capping what the business gets back.
Your P&L surprises come from the store floor, not the market.
See what Ward finds for Finance leaders, with root causes and recommended actions.
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.