Your analyst has a 4-week backlog.
Ward answers in seconds.
Most ad-hoc questions take a week. Ward queries every data source at once and returns the root cause with charts the same day. Click any number to see the SQL.
One question. Full investigation.
“Why did margin drop in the Southeast?” usually means three systems and a deck next week. Ward answers in seconds, across every connected source, with root causes, confidence intervals, and charts.
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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Every answer ships with its evidence trail
Every finding shows its work: the forecast model, the MAPE, the confidence interval, the source tables. Pin findings, share them, or drill deeper. Any insight traces back to the row.
Audit the query. Audit the math.
See exactly what Ward ran: the raw SQL, the chart spec, the result set, the forecast model and its parameters. Export any query to your BI tool or hand it to engineering.
Charters on file for every agent
Each agent runs under a written charter: scope, sources, allowed actions, owner, version. Every edit is logged with name, time, and approver. Diff two versions in the console.
Powered by your choice of LLM
OpenAI, Anthropic, Gemini, or bring your own. Ward’s router picks the model per task and you can switch any time. Forecasting math runs on classical models, not the LLM.
Where Chat
does the work.
The insight types that lean on chat 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.
Your analyst has a backlog. Ward doesn’t.
Same-day answers to questions that used to take weeks. See it on your data.
Read-only to start · your LLM keys · SOC 2 Type II underway · or book a call directly
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