Most AI tools never learn
if they were right.
They fire alerts, your team acts, and nobody checks if it worked. Ward connects insight to action to outcome, and each result tunes the next cycle.
The loop that accelerates
Most analytics tools stop at the chart. Ward closes the loop. Data reads in from POS, inventory, labor, finance, ERP, marketing, and external signals. Agents query each source, insight cards surface what changed and why, teams act, and results feed back to tune the next cycle.
Unified data layer
Ward reads POS, marketing, labor scheduling, finance, inventory, ERP, and ecommerce, plus external signals like weather, events, and demographics. One unified view across stores, ecommerce, wholesale, every channel.
Audit any number on the page
Click a forecast, a margin call, a shrinkage flag. Ward shows the SQL, source tables, model, parameters, and backtest. IT sees data governance rules in a graph view, not a wall of YAML.
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.
Light and dark themes are available. Your choice is remembered per browser.
Available integrations
Ward connects to the systems you already run: ERP, POS, data platforms, BI tools, supply chain.
Where Closed Loop
does the work.
The insight types that lean on closed loop 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.
Insights surface
What changed, why it matters, what to do. Every insight arrives with the action attached.
Insights become actions
Any card becomes a tracked ticket, dispatched to a named owner by push, text, or email.
Your team responds
Cards voted up or down with reasoning. Tickets completed or rejected. Every response is a signal.
Outcomes measured
Revenue, margin, fill rate, labor cost. Did the action move its target? Measured, not assumed.
Agents get sharper
Every vote, ticket, and outcome feeds back in. Each cycle sharpens the next.
Most AI tools never learn if they were right.
Ward tracks outcomes. Every cycle sharper than the last. 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.