Every card is a problem
you would have found too late.
Margin erosion, promo conflicts, shrinkage spikes, staffing mismatches. Ward catches each one and explains what changed, why, and what to do next. Click any number to see the SQL.
What changed. Why. What to do next.
Dashboards show what happened, not why or what to do about it. Each insight card surfaces one anomaly, explains the root cause, quantifies the impact, and recommends a next step. They arrive automatically, no login required.
The math behind every number
Forecasts run on ARIMA, Holt-Winters, Bayesian hierarchical, and gradient-boosted residuals. Anomalies use z-score against a 90-day rolling baseline. Each card carries the model card, the MAPE, and the confidence interval. Click the number to see the SQL, source tables, and parameters.
The agents behind every card
Define an agent for any job, each on a prebuilt, retail-specific playbook: shrink, store sales, supplier scoring, loss prevention, labor, store ops. Each runs under a written charter: scope, sources, allowed actions, owner, version. Diff two versions side by side. Every edit is logged with name, time, and approver.
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 |
|---|---|---|
sap_pos_transactions | sap | 1.0 |
sap_inventory_shrinkage | sap | 1.2 |
sap_labor_scheduling | sap | 1.0 |
retail_inventory_weekly | retail | 1.1 |
retail_google_ads_daily | retail | 1.0 |
retail_meta_ads_daily | retail | 1.0 |
retail_ga4_website_daily | retail | 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.posretail.inventory_weeklyPipelines
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_inventory_shrinkage | sap_inventory_shrinkage | inventory_shrinkage | enabled | daily |
sync_sap_labor_scheduling | sap_labor_scheduling | labor_scheduling | 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_meta_ads_daily | retail_meta_ads_daily | meta_ads_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::"inventory_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-retail" | Tenant | acme-retail |
Model::"sap.pos_transactions" | Model | acme-retail |
Model::"sap.inventory_shrinkage" | Model | acme-retail |
Model::"sap.labor_scheduling" | Model | acme-retail |
Model::"retail.inventory_weekly" | Model | acme-retail |
Model::"retail.google_ads_daily" | Model | acme-retail |
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.
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Cards by insight type
Ward generates cards across eight core domains, each tuned to a specific operational signal.
Where Insight Cards
does the work.
The insight types that lean on insight cards to deliver value.
Built for the teams
that catch problems first.
The KPIs that change
when this runs.
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.
Every card is a problem your team would have found too late.
See what Ward catches on your actual data.
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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.