Product

See what Ward actually looks like.

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

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

app.getward.ai

The Sources list in Ward, showing five connected systems with their type: two Parquet exports, an HTTP API, and two API connectors, above a New source button. — **Sources.** Every external system the lake reads, in one list, with the connector each one came in through.

Ward’s Sync page: four pipelines, one running mid-flight with 900,000 rows fetched, two succeeded at 7,868,333 and 2,056 rows, and one partial, each with its own Run control. — **Sync.** Live status for every pipeline in one place, and a Run button next to each one.

The Documentation tab of the store_sales_daily model: owner, tags, summary, a field-level description, and usage notes recording that net_sales is gross of customer returns and traffic is an estimate. — **Documentation.** What the metric means, stored next to the metric. This is the semantic layer, and the agents read it.

Ward’s settled answer: all six stores clustered between 31.2 and 31.7 per cent against a 32 per cent margin target, then the cause at Corpus Christi, where Electronics runs at 12 per cent margin with $389K of promotional lines sold below cost carrying $162K in discounts, followed by a suggested next question. — **The answer.** Six stores, one cause, and the follow-up worth asking. One question, seven round-trips, 87 seconds.

Ward’s tool inspector open on the query behind a figure, showing the two rows it returned and the JSON arguments that produced them: city equals Corpus Christi, department equals Electronics, business_date between 1 and 31 December 2025. — **Tool inspector.** Every number opens into the call that produced it: arguments, rows, raw result.

Ward’s Policies list for the Acme tenant: eight Cedar policies, seven permitting and one forbidding, each with an inline edit control. — **Policies.** Access is eight Cedar policies you can read in a sitting, not a permissions matrix nobody opens.

Ward’s Providers page: OpenAI, Anthropic, Google, Azure OpenAI, Groq, OpenRouter, xAI, Mistral and Ollama, each showing how many models it exposes and whether it has been configured. — **Providers.** Nine providers, your keys. On this tenant only Ollama is configured, running local.

Ward · connecting a POS export 63s · one take

Describe the export in a sentence. Ward drafts the source, you apply it, the pipeline runs, and the model joins the graph. 63 seconds, unedited, one take. Only the login screen is cut.

Ward · one question about December margin 139s · one take

One question, seven round-trips, every query inspectable on the way past. The answer settles at 87 seconds. 139 seconds, unedited, one take, real time. Nothing is sped up.

43Prebuilt retail playbooks

24 moForecasts backtested

1 viewChat, charts & reasoning

100%Answers cited to SQL

SolutionWhat you open every morning

One operating view.
Every answer shows its work.

The screen your team starts the day on.

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.

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.

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.

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.

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.

ActionInside the product