Merchandising wants Ward. You sign the contract.
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.
What lands on your desk
when the business buys AI.
Merchandising wants Ward. You sign the contract.
Business sponsor saw the demo. You have a week to vet a vendor you didn't pick
AI vendors price by seats and tokens. Total cost is unknowable until invoice three
Multi-year commits with auto-renew. No exit if the pilot stalls
Renewals come back 30% higher with no leverage and no benchmark
Security and DPA reviews start after the team has already committed
Source: Gartner
How Ward earns
the sign-off.
- ×Business sponsor saw the demo. You have a week to vet a vendor you didn't pick
- ×AI vendors price by seats and tokens. Total cost is unknowable until invoice three
- ×Multi-year commits with auto-renew. No exit if the pilot stalls
- ×Renewals come back 30% higher with no leverage and no benchmark
- ✓MSA, DPA, SOC 2 Type II (in progress), and architecture review available before signature
- ✓Month-to-month contracts. No multi-year lock-in. No auto-renew traps
- ✓Transparent pricing tied to scope and store count, not seats or tokens
- ✓14-day insight guarantee. If Ward doesn't deliver, month two is on us
MSA, DPA, SOC 2 Type II (in progress), and architecture review available before signature
Month-to-month contracts. No multi-year lock-in. No auto-renew traps
Transparent pricing tied to scope and store count, not seats or tokens
14-day insight guarantee. If Ward doesn't deliver, month two is on us
Active pilot with an 800-store, $300M a year fresh grocery chain. Reference calls available before signature
From signature
to running insights.
A live pilot for head of procurement hits these milestones on real data, on a fixed-fee schedule.
How the deal is structured.
No multi-year commits. No auto-renew. Pricing tied to scope, not seats. Exit any time. Every artifact you need to vet a vendor is available before signature.
30-day notice to exit. No rollover clauses buried in the MSA. Pay for the month you use.
Tier price reflects data complexity and store count. Not seats. Not tokens. Not per-query. Renewals match the scope.
If Ward doesn't produce insight cards on your data within 14 days of connect, month two is on us. Outcome-anchored, not promise-anchored.
MSA, DPA, SOC 2 II report, sub-processor list, and architecture review available before procurement signs. No back-and-forth.
Active pilot with an 800-store, $300M a year fresh grocery chain, plus design partners on file. 30-minute reference calls scheduled before signature, not after.
Cyber and tech E&O on file with an AI rider. Standard MSA indemnity. No carve-outs hidden in the schedule.
The product your
business teams will use.
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 |
|---|---|---|
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.
| 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.possnow.inventoryPipelines
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.
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::"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" | 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.
| 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.
Procurement packet: MSA, DPA, SOC 2 Type II status (in progress), architecture review, and reference contacts. Month-to-month. 14-day insight guarantee. Available before signature.
Verticals the business teams
will use Ward for.
What head of procurement
uses Ward for first.
The three insight types this role drives the most value from. Each one tailored for procurement decision-making.
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.
The KPIs Ward moves
for head of procurement.
The platform pieces
this role leans on.
Systems Ward connects to,
read-only by default.
Operator stories
and the alternatives.
Two ways to start.
Run a read-only proof on your stack, or have advisory walk you through architecture.
Playbooks for Procurement.
The procedures this team runs on Ward. Each pairs a data insight with the AI automation and automated data routing to act on it.
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.
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.
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.
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.
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.
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.
Merchandising wants Ward. You sign the contract.
Architecture review, MSA, DPA, SOC 2 II report, on the table before you sign.
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.