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Closed-Loop Intelligence

Product · Closed-Loop Intelligence

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.

See the loop in action →

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.

Connect your full business stack

POS, marketing, labor, finance, inventory, ERP, ecommerce, plus weather, demographics, and events.

TLS 1.3 encrypted

Ward learns your business

Baselines per domain. Forecasts on ARIMA, Holt-Winters, Bayesian hierarchical, and GBM, backtested on your last 24 months.

First insights in 48 hours · Multi-LLM

Insights arrive. You act. Ward learns.

What changed, why, and what to do. Actions dispatch to a named person, and the outcome tunes the next cycle.

Always-on decision intelligence

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.

app.getward.ai Live demo

Acme Retail @Merchandising: VP Analyst claude-sonnet default

Chat

Ask anything. Ward routes to the right agent and returns cited answers.

Today

Why did Store 37 miss target?

3 turns · live

Q4 promo cannibalization

5 turns · 1h ago

Margin compression, private label

4 turns · 3h ago

Earlier

Shrinkage drift, West region

2 turns · yesterday

DC West fill-rate slip vs SLA

7 turns · 2d ago

Stockout root cause, SKU 8821

6 turns · last week

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.

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.

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

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

Model	Horizon	MAPE
`holt_winters`	4wk	4.1%
`arima_sarimax`	13wk	8.9%
`gbm_demand`	1wk	2.1%
`bayes_hier`	new store	11.4%

Models

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

Search models…

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.

Search sources…

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.

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.

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.

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 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.

Search UIDs…

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.

API Keys Model Profiles

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.

Appearance

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Account

NameAdmin

Emailadmin@acme.io

Tenantacme-retail

Every query inspectable. Drill-down keeps full context. One unified data layer.

Available integrations

Ward connects to the systems you already run: ERP, POS, data platforms, BI tools, supply chain.

Fixes powered by Closed Loop

Where Closed Loop
does the work.

The insight types that lean on closed loop to deliver value.

Stockout

Stockout Prediction

Know before the shelf empties.

Pricing

Price Optimization

Price on elasticity you can measure.

Demand

Demand Forecasting

See demand before it arrives.

Assortment

Assortment Planning

Stock what sells. Cut what doesn't.

WHO

Roles that depend on it

Built for the teams
that catch problems first.

KPI

Metrics it moves

The KPIs that change
when this runs.

Fill Rate

On-Shelf Availability

Ward monitors fill rate at the store-category-hour level and triggers insight cards when it drops below your threshold.

ITR

Inventory Turnover Ratio

Ward flags when turn slows below vertical benchmark, signaling overstock.

GMROI

Gross Margin Return on Inventory

Ward ranks categories by GMROI to identify inventory tying up capital.

MD%

Markdown Rate

Ward tracks markdown timing vs sell-through velocity.

See it. Or compare it.

Operator stories
and the alternatives.

PLAY

Plays in production

How operators run closed loop on Ward today.

DBX

Ward vs Databricks

Why building this on top of your lakehouse stalls.

Two ways to start.

Run a fixed-fee pilot on your data, or talk to advisory about a broader engagement.

Get a demo → Talk to advisory

Ward

Insight

Dispatch

Feedback

Evaluate

Learn

The Closed Loop

Insights surface

What changed, why it matters, what to do. Every insight arrives with the action attached.

Real-time detection Root cause + recommendation

Insights become actions

Any card becomes a tracked ticket, dispatched to a named owner by push, text, or email.

Tickets created automatically Dispatched to the right person

Your team responds

Cards voted up or down with reasoning. Tickets completed or rejected. Every response is a signal.

Vote up / down Ticket completed Reasoning attached

Outcomes measured

Revenue, margin, fill rate, labor cost. Did the action move its target? Measured, not assumed.

KPI impact tracked Results vs. prediction scored

Agents get sharper

Every vote, ticket, and outcome feeds back in. Each cycle sharpens the next.

Cycle repeats, sharper each time

$1.8T

Projected global AI market by 2030

Customer acquisition lift for data‑driven orgs

Foundation models shipped since 2022

Guarantees any single model stays on top