Platform · Query and chat

Ask in English. Read the SQL.

Ward answers in a sentence and shows the query underneath it. If the SQL is wrong you can see that it is wrong, which is the only way a data team ever trusts one of these.

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What it guarantees

Natural language over the semantic layer, with the generated SQL always visible.

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Every answer ships with the SQL that produced it.

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Resolves through the semantic layer, so the numbers match the dashboard.

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Ambiguous questions come back as a question, not a guess.

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Ask from the chat panel, or pin the answer to a dashboard.

In the console

What this looks like when you actually use it.

Add a source

Sources, then + New source. Pick your warehouse, lake, ERP or POS and give Ward a read-only credential scoped to the schemas you name. Ward reads the schema and shows you what it found before anything else happens.

Review what Ward AI drafted

Ward profiles what landed and drafts the cleaning and conforming rules. They arrive as a proposal, not as a running pipeline. You read the diff and approve it, the same way you would review a colleague's work.

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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.

A question mid-answer in Ward chat, asking which stores missed their margin target last month and what is driving it at Corpus Christi, with the reasoning trace showing 7.6 seconds of thought, models listed in 8 milliseconds, and two models inspected in 16 milliseconds. — Reasoning. You watch it choose the models before it writes a word of the answer.

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.

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 has read a plain-English description of a POS export and returned three suggested form values, each with its own Apply button: the source name, a description, and the file path. — Lake assistant. Ward AI drafts the connection from a sentence. Nothing binds until someone hits Apply.

The pos_transactions_csv source after creation, showing its name, type and description, with the assistant transcript that produced it still open on the right. — Source created. From one sentence to a live source, with the exchange that built it kept alongside.

The Inputs and Outputs tab of a Ward pipeline, mapping the pos_transactions_parquet source to the pos_transactions model, with updated_at set as the high-water-mark column for incremental syncs. — Pipeline. Source in, model out, and the column that makes the next sync incremental instead of total.

Full quickstart guide →

The rest of the platform

Five more primitives. Adopt them in any order.

Warehouse

Live in hours, not weeks.

A running warehouse built from the systems you already have, without a migration window.

Connectors

540 sources. Read-only by default.

Managed ingestion from warehouses, lakes, SaaS APIs, databases and flat files.

Semantic layer

One definition of revenue. Enforced.

Metrics, dimensions and grain defined once and enforced everywhere, including in what agents are allowed to compute.

Agent runtime

The warden of your data and your agents.

A governed runtime for building agentic logic against your own data, with every action scoped and logged.

Governance

The answers your security review needs.

Row-level policy, scoped credentials, and an audit log that covers humans and agents equally.

Start with one source.

Deploying is free. The whole console, every connector, and nothing to license.

Get a demo → Take the 3-minute assessment

Read-only to start · your LLM keys · SOC 2 Type II underway · or book a call directly

Get started

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.

Step 1 of 3

What are your goals?

Reduce stockouts Cut shrinkage Optimize pricing Improve demand forecasting Better promo ROI Understand customer behavior

Step 2 of 3

About your operation

Retail vertical

Number of stores

Step 3 of 3

Your contact info

Full name

Work email

Company

Phone (optional)