Send us a data sample. We model your metrics, run a free analysis, and show you the answers you have been waiting days for. If it holds up, we connect your warehouse, build the natural-language layer, and set up the anomaly alerts and forecasts so the answers come to you.
The data is there. In BigQuery, in Snowflake, in Postgres tables that someone built two years ago. The problem is that getting an answer requires a data analyst, a ticket, a few days of back and forth, and a spreadsheet that nobody keeps current. By the time the answer arrives, the decision has already been made by instinct.
Decision intelligence changes the loop. You define your metrics once, with the business logic that makes them trustworthy. After that, anyone with access can ask a question in plain language and get the answer in seconds, grounded in the same numbers the data team would have pulled, with the anomalies surfaced and the forecast attached.
No platform to buy, no model to train from scratch, no six-month data strategy. We start with a sample you send us and come back with real answers on your data.
Share a sample of your warehouse data and the questions your team is asking today. Invoices, orders, web events, CRM data, anything with signal.
We define the metrics layer: every KPI, its formula, and its grain. We run forecasts against your historical data and measure the error before you see anything.
You see the free analysis: real answers to your questions, anomalies flagged, forecasts with their accuracy measured on your own history. You keep it.
If it holds up, we connect the warehouse, build the natural-language layer, wire the anomaly alerts, and schedule the automated exec briefs.
Three capabilities that compound. Each is useful on its own; together they make the data warehouse the fastest person in the room.
Type a question in English the way you would ask a colleague. The system translates it into SQL against your semantic metrics layer, runs it, and returns the answer with the chart. No dashboard to learn, no ticket to file.
The system watches your key metrics continuously. When a number breaks its expected range, it fires an alert with the likely cause and the affected dimension, before anyone notices the revenue dip in the Monday review.
Demand, revenue, churn, capacity: forecasts run against your historical data, with honest confidence bands and the backtest accuracy shown upfront so you know what to trust and by how much.
Want the depth? How the semantic metrics layer prevents hallucinated numbers, how anomaly detection works, what makes a forecast trustworthy, and where this connects to your warehouse are all covered in the complete AI Analytics guide.
A plain-language question, the SQL it generated from your metrics layer, the result grounded in real numbers, and any anomaly the system detected along the way. Every number traces back to a defined metric, not a model guess.
The card on the right is illustrative. Your free analysis is built from your own data.
SELECT month, SUM(net_revenue) AS revenue FROM metrics.orders_monthly WHERE month BETWEEN '2026-01-01' AND '2026-04-01' AND region = 'EMEA' GROUP BY month ORDER BY month; -- metric: net_revenue (defined: orders.amount - refunds.amount) -- grain: monthly, region
If the answer lives in your warehouse and you are waiting more than a few minutes for it, that is the gap this fills.
| Need | The question | What it returns |
|---|---|---|
| Self-serve analytics | Why did revenue dip in March? | The answer and the chart, grounded in your defined metrics |
| Anomaly alerts | Tell me when something breaks | An alert with the likely cause and the affected dimension |
| Forecasting | What will demand be next quarter? | A forecast with a confidence band, backtested on your history |
| Exec brief | Summarize the week | A written brief, generated from your metrics, sent on schedule |
| KPI watch | Are we on track? | A live read on every target, flagged when off course |
The analysis is always free. After that you pay for the build and the ongoing operation. The exact scope is set after the free analysis, because it depends on your warehouse and the metrics you want to define.
AI Analytics is a data engineering and modeling engagement, which is the work we are built for. A few that map closely:
Multi-touch attribution built from messy event data and surfaced in plain language.
Revenue, pipeline, and cohort metrics modeled so the GTM team could ask their own questions.
Churn forecasts with confidence bands, backtested on two years of subscriber data.
Tell us your work email and where your data lives. We reply within 24 hours to set up the free analysis. You keep the result whether or not you continue.
Two fields. We do the rest.
A free analysis on your real data, the metrics modeled and backtested, the anomalies surfaced. Then we build the layer that makes every answer take seconds.
Get a free analysis