Ward
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Who Ward helps
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Head of IT

Director / VP · Technology

The business wants AI. You sign off on the architecture.

Business teams want the AI. You vet the implementation. Ward starts read-only, runs on policy, and hands you every artifact you need before signature: architecture, contracts, and references, all on the table.

74% of enterprise AI projects stall before production. Integration debt and security review are the top two reasons.Source: Gartner

Get a demo → Review the trust pack

IT operations and infrastructure in an enterprise data center

Grocery Fashion Home

What you inherit

What lands on your desk
when the business buys AI.

The business wants AI. You sign off on the architecture.

Business sponsor already chose the vendor. You inherit the security review

Every AI vendor wants write access and a copy of the production data

Model lock-in means rewriting the stack when GPT or Claude moves again

Audit trail is an afterthought. Compliance has nothing to pull on

Data lake project keeps getting bumped for the next thing the business wants

74% of enterprise AI projects stall before production. Integration debt and security review are the top two reasons.

Source: Gartner

How Ward earns sign-off

How Ward earns
the sign-off.

Standard vendor playbook

Demo, MSA, then a six-month security review while the business waits.

×Business sponsor already chose the vendor. You inherit the security review
×Every AI vendor wants write access and a copy of the production data
×Model lock-in means rewriting the stack when GPT or Claude moves again
×Audit trail is an afterthought. Compliance has nothing to pull on

How Ward shows up

Architecture, contracts, and references on the table before signature.

✓Federated query: data stays in your warehouse. No copies, no shadow lake
✓Read-only credentials. Cedar policies enforce least-privilege per agent
✓LLM-agnostic. Anthropic, OpenAI, Gemini, Ollama. Bring your own keys
✓Every query, every model, every source logged. SIEM-ready audit output

Federated query: data stays in your warehouse. No copies, no shadow lake

Read-only credentials. Cedar policies enforce least-privilege per agent

LLM-agnostic. Anthropic, OpenAI, Gemini, Ollama. Bring your own keys

Every query, every model, every source logged. SIEM-ready audit output

VPC peering, PrivateLink, SOC 2 II. Your security review is short

What you get and when

From signature
to running insights.

A live pilot for head of it hits these milestones on real data, on a fixed-fee schedule.

Architecture explained

What runs where. Who can touch what.

Federated query. Read-only credentials. Cedar policies enforce least-privilege. Full audit trail per query. No data leaves your warehouse. The business gets the insights. You keep production safe.

Federated query, data stays put

Ward queries Snowflake, BigQuery, Redshift, Postgres, and SAP HANA in place. No ETL into a Ward-owned database. No copy. Data residency unchanged.

Read-only by default

Service accounts are locked to SELECT. Cedar policies enforce per-agent, per-resource scope. Agents cannot mutate the data they reason over.

LLM-agnostic, BYOK

Anthropic, OpenAI, Gemini, Ollama. Bring your own keys, route per task. Switch models without rewriting the stack. No vendor lock-in at the model layer.

Full audit trail

Every query, every source, every model call, every reasoning step logged. SIEM-ready output. Compliance has a thread to pull on for any insight.

Network and tenancy

VPC peering, PrivateLink, IP allowlist. Single-tenant deploy in your AWS, Azure, or GCP if your security posture requires it.

Identity and SSO

SAML/OIDC SSO. SCIM provisioning. Role-based access tied to your IdP groups. No shadow user store. Offboarding flows through your existing process.

What gets delivered

The product your
business teams will use.

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.

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

Dashboards

Pinned views built from saved data-lake queries.

Revenue vs. forecast +4.2% WoW

Gross margin % −3.2pp

Fill rate, fresh 83%

Shrink, West region +0.8pp

Models

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

Search models…

Name	Namespace	Version
`retail_pos_transactions`	retail	1.0
`retail_inventory_snapshot`	retail	1.2
`retail_labor_scheduling`	retail	1.0
`retail_promo_calendar`	retail	1.1
`retail_supplier_performance`	retail	1.0
`sap_inventory_shrinkage`	sap	1.0
`ga4_daily_events`	marketing	1.0
`meta_ads_ad_level`	marketing	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

Toast POS

Ward Gateway

TLS 1.3 · AES-256

Querying live · data stays put

Federated answers

SELECT * FROM sap.pos

JOIN snow.inventory

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 II in progress 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_labor_scheduling`	sap_labor_scheduling	labor_scheduling	enabled	daily
`sync_sap_inventory_shrinkage`	sap_inventory_shrinkage	inventory_shrinkage	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_ga4_website_daily`	retail_ga4_website_daily	ga4_website_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::"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"`	Tenant	acme
`Model::"sap.pos_transactions"`	Model	acme
`Model::"sap.inventory_shrinkage"`	Model	acme
`Model::"sap.labor_scheduling"`	Model	acme
`Model::"retail.toast_pos_daily"`	Model	acme
`Model::"retail.ga4_website_daily"`	Model	acme

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

Live product. Read-only by default. Every panel inspectable. Click around, this is what your business teams get.

Ward · for Head of IT06:47 AM

Architecture packet: data flow diagram, Cedar policy bundle, SOC 2 II report, sub-processor list, network topology. Read-only by default. SIEM-ready logs. Available before the pilot starts.

✓ Pre-signatureTechnology context

Where Ward operates

Verticals the business teams
will use Ward for.

30,000+ SKUs

Grocery & Supermarket

Grocery · deployed read-only

15,000+ SKUs

Fashion & Apparel

Fashion · deployed read-only

50,000+ SKUs

Home Improvement

Home · deployed read-only

5,000+ SKUs

Specialty Retail

Specialty · deployed read-only

20,000+ SKUs

Pharmacy & Health

Pharmacy · deployed read-only

3,000+ SKUs

Convenience & C-Store

Convenience · deployed read-only

10,000+ SKUs

Furniture Manufacturing & Retail

Furniture · deployed read-only

Top insight types

What head of it
uses Ward for first.

The three insight types this role drives the most value from. Each one tailored for technology decision-making.

Stockout

Stockout Prediction

What supply chain runs on Ward, read-only, on your stack.

Fill Rate

Fill Rate Monitoring

Estate KPIs delivered without giving an LLM write access.

Shrinkage

Shrinkage Detection

Cause attribution on policy-scoped agents, every query logged.

All insight types

What the business
teams receive.

The insight types Ward generates for the business teams you’re approving the platform for. Every one inspectable, every one logged.

Built on Ward’s platform

The platform pieces
this role leans on.

LLM

LLM-Agnostic

Bring your own model. Anthropic, OpenAI, Bedrock, or on-prem.

LOOP

Closed Loop

Recommendations write back into your ERP, planner, or planogram.

Systems we connect to

Systems Ward connects to,
read-only by default.

Data Platform · read-only

BigQuery

Data Platform · read-only

Blue Yonder

Supply Chain · read-only

RELEX

Supply Chain · read-only

See it. Or compare it.

Operator stories
and the alternatives.

PLAY

Plays in production

How head of it teams use Ward today.

DBX

Ward vs Databricks

Why building this on top of your lakehouse stalls.

Two ways to start.

Run a read-only proof on your stack, or have advisory walk you through architecture.

Start a pilot → Review the trust pack

›

Playbooks

Playbooks for Technology.

The procedures this team runs on Ward. Each pairs a data insight with the AI automation and automated data routing to act on it.

Integration health monitor

A feed goes stale or a connector breaks. Ward catches the gap before the numbers go wrong.

All retailSnowflake · BigQuery · SAP connectorsData freshness

Access & audit review

Access is drifting from least-privilege. Ward reviews scopes and streams the diffs to your SIEM.

All retailCedar policies · SIEMAudit coverage

Model routing guardrail

Queries over-spending on premium models. Ward routes each to the cheapest model that clears the bar.

All retailWard routing layer · BYO-LLMCost per query

Data quality watch

Upstream data quality is slipping. Ward flags nulls, dupes, and drift before they reach a decision.

All retailSnowflake · BigQueryData accuracy

PII & residency guard

Sensitive data is crossing a boundary it shouldn't. Ward enforces residency and read-only scope.

All retailCedar policies · SIEMCompliance coverage

Browse all playbooks →

Ward

Insight

Dispatch

Feedback

Evaluate

Learn

The Closed Loop

Insights surface

Ward’s agents detect what changed, why it matters, and what to do about it. Every insight includes a recommended action. Not just a chart to interpret.

Real-time detection Root cause + recommendation

Insights become actions

Any insight card can be turned into a tracked ticket or task. Dispatched to the right person, on the right channel: mobile push, text, or email. Not every insight needs a ticket. When one does, it has an owner.

Tickets created automatically Dispatched to the right person

Your team responds

Insights get voted up or down with reasoning. Tickets get completed or rejected. Every response is a signal. Ward learns what worked, what missed, and why.

Vote up / down Ticket completed Reasoning attached

Outcomes measured

Ward evaluates real results: revenue, margin, fill rate, labor cost. Did the action actually improve the number it targeted? Measured outcomes, not assumptions.

KPI impact tracked Results vs. prediction scored

Agents get sharper

Every vote, every completed ticket, every measured outcome feeds back in. Ward learns from your team’s judgment and real-world results. Each cycle sharpens the next. Then it starts again.

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

Spend governance

Control AI spend by department and user.
Without skimping on the outcome.

Give every department and every user a compute budget. Ward routes each question to the cheapest model that clears the quality bar, so finance caps the spend without capping what the business gets back.

Compute budgets, this month 68% used · on pace

Merchandising

14,200 / 20,000

Supply Chain

9,800 / 15,000

Store Ops

6,100 / 10,000

Ecommerce

4,700 / 5,000

Finance

3,400 / 5,000

Per-user caps. Alerts at 80%. Hard stop or overage approval, your call. Ecommerce flagged at 94%, before it became a surprise invoice.