Any LLM you choose,
routed per task and audited per query.
Today’s leading model may not lead next quarter. Ward runs on OpenAI, Anthropic, Gemini, or your own, routed automatically and logged on every call.
The right model for every task
Not every question needs the most expensive model. Ward’s router sends each query to the right LLM: fast models for lookups, reasoning models for root cause. Forecasting numbers come from classical models, not the LLM. The LLM frames the answer and cites the math.
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.
Light and dark themes are available. Your choice is remembered per browser.
Audit any query, any answer
The reasoning graph shows every step: which tables were queried, what was computed, which forecast model ran, which LLM answered. Click any number to walk it back to the row. Hand the trail to compliance, no matter which model powered the analysis.
Charters on file. Changes on the record.
Every agent ships with a written charter: scope, sources, allowed actions, owner, version. Diff two versions side by side. Every edit is logged with name, time, ticket, and approver. Roll back in one click. Export to your SIEM.
Cyber liability covered
Cyber and tech E&O policy on file, AI rider included. Certificate of insurance to procurement in a day. Coverage names data breach, regulatory response, and AI-specific liability.
Built for the teams
that catch problems first.
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.
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.
Your AI vendor shouldn’t lock in your model.
Ward works with any LLM. Switch anytime. See it on your data.
Read-only to start · your LLM keys · SOC 2 Type II underway · or book a call directly
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.