Product

See what Ward actually looks like.

Five dashboards open, still no answer to “why.” This is the screen that gives it.

Get a demo → Take the 3-minute assessment

Read-only to start · your LLM keys, no lock-in · SOC 2 Type II underway

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

AI Insights

Agents run against your baselines overnight. These are what they flagged without being asked.

3 flagged 8 queries run 3 sources swept 04:00, acme-retail
Act now labor_efficiency 0.94

Rev/labor-hour −22% vs. cluster, staffing mismatch at 11a–1p peak

Schema Scout · routed to Merchandising Agent Pin Ask Ward Investigate
Review inventory.fresh 0.89

Fresh fill 83%, backroom replenishment lag at 2–4p

Promo Agent · routed via Merchandising Pin Ask Ward Investigate
Watch promo.lift 0.81

BOGO crackers cannibalized Brand Y by 28%, net category +6%

Margin Agent · routed via Finance Pin Ask Ward Investigate
Recommended

Re-baseline Store 37 schedule against true peak, raise replen window to 1p, and review the BOGO before next cycle.

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%

Models

Browse, search, and manage data–lake model definitions for your tenant.

Search models…
NameNamespaceVersion
sap_pos_transactionssap1.0
sap_inventory_shrinkagesap1.2
sap_labor_schedulingsap1.0
retail_inventory_weeklyretail1.1
retail_google_ads_dailyretail1.0
retail_meta_ads_dailyretail1.0
retail_ga4_website_dailyretail1.0

Sources

Connect external systems to the data lake.

Search sources…
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

Architecture

Two ways to connect. Federate against your live systems, or ingest into Ward’s data lake. Toggle below.

Your systems · read-only
SAP Retail
Snowflake
BigQuery
Shopify
NCR Voyix
Ward Gateway
TLS 1.3 · AES-256
Querying live · data stays put
Federated answers
SELECT * FROM sap.pos
JOIN retail.inventory_weekly
WHERE store_id = 37
→ insight cards
Ward Data Lake
→ baselined per store
TLS 1.3 in transit AES-256 at rest Read-only credentials SOC 2 Type II underway VPC peering · PrivateLink

Pipelines

Move data from sources into models on a schedule.

NameSourceModelStatusSchedule
sync_sap_pos_transactionssap_pos_transactionspos_transactionsenabledhourly
sync_sap_inventory_shrinkagesap_inventory_shrinkageinventory_shrinkageenableddaily
sync_sap_labor_schedulingsap_labor_schedulinglabor_schedulingenableddaily
sync_retail_inventory_weeklyretail_inventory_weeklyinventory_weeklyenabledweekly
sync_retail_google_ads_dailyretail_google_ads_dailygoogle_ads_dailyenableddaily
sync_retail_meta_ads_dailyretail_meta_ads_dailymeta_ads_dailyenableddaily

Streams

Real-time ingestion pipelines.

0events / min
0streams active
0% delivered
  • pos.txn store_037, basket $42.18
  • inv.move dc_west → store_104
  • labor.clock store_022 shift_start
  • pos.txn store_211, basket $19.04

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"

Entities

Principals and resources referenced by Cedar policies.

Search UIDs…
Entity UIDTypeTenant
Tenant::"acme-retail"Tenantacme-retail
Model::"sap.pos_transactions"Modelacme-retail
Model::"sap.inventory_shrinkage"Modelacme-retail
Model::"sap.labor_scheduling"Modelacme-retail
Model::"retail.inventory_weekly"Modelacme-retail
Model::"retail.google_ads_daily"Modelacme-retail

Providers

Manage LLM API keys and the model profiles that use them.

API Keys Model Profiles
NameProviderUsed byCreated
anthropic-defaultAnthropic3 profilesApr 22
openai-defaultOpenAI2 profilesApr 22
gemini-defaultGemini1 profileApr 22
ollama-onpremOllama2 profilesApr 22

LLM-agnostic. Bring your own key, route per task. No lock-in.

Settings

Manage your dashboard preferences and account.

Appearance
Theme • Light ° Dark

Light and dark themes are available. Your choice is remembered per browser.

Account
NameAdmin
Emailadmin@acme.io
Tenantacme-retail
Click any panel, Chat, Insights, Reporting, Architecture, Policies, to explore the live demo.
43Prebuilt retail playbooks
24 moForecasts backtested
1 viewChat, charts & reasoning
100%Answers cited to SQL
A
SolutionWhat you open every morning

One operating view.
Every answer shows its work.

The screen your team starts the day on.

01
One operating view
Chat, charts, cards, and the reasoning graph in one screen. Build a chart from a sentence, click it for the SQL.
02
Agents you define, on retail playbooks
One agent per job: shrink, sales, supply, loss prevention, labor, store ops. Each runs a written charter, every edit logged by name.
03
Every answer shows its work
The reasoning graph shows every step, table, and model that answered. Forecasts carry a model card and a MAPE.
B
ActionThe four layers

What you are actually deploying.

Ward is four layers over the stack you already run. Each one has a boundary, and each one can be evaluated on its own.

L1
Retail observability
Reads POS, ERP, WMS, labor, finance, and supplier feeds. Scores every anomaly against that store’s own baseline.
Boundary: reads only. Nothing leaves your warehouse.
L2
AI orchestration
Routes across Anthropic, OpenAI, Gemini, and Ollama on your keys, to the cheapest model that clears your accuracy bar.
Boundary: the LLM frames answers. Classical models produce the numbers.
L3
Workflow automation
43 playbooks that write into SAP, Oracle Retail, Blue Yonder, Relex, and Manhattan.
Boundary: every write gates on an approver you named, logged before and after.
L4
Security and data governance
Federated query, read-only accounts, Cedar policy per agent, and a JSONL audit stream to your SIEM.
Boundary: your identity stack and residency rules govern, unchanged.
C
ActionInside the product

Retail analytics, agents,
and a reasoning trail on every answer.

Dashboards built by chat

Ask in plain English; Ward writes the query and pins the chart. No BI ticket.

Agents you define

Prebuilt retail playbooks for sales, shrink, supply, loss prevention, labor, store ops, each with an auditable charter.

Backtested forecasts

ARIMA, Holt-Winters, Bayesian, GBM, scored on 24 months with MAPE attached.

Live reasoning graph

See which tables ran, what was computed, and which model answered, step by step.

Cited SQL, every time

Click any number and re-derive it from the query and source rows behind it.

Audit-ready trail

Every step signed by name, time, and approver. Hand it to compliance as-is.

The problems are in your data right now.

See what Ward finds in your actual POS, inventory, and finance feeds.

Get a demo → Take the 3-minute assessment

Read-only to start · your LLM keys · SOC 2 Type II underway · or book a call directly

Get started

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