Azimut
Every report finance or leadership needs goes through the same queue: it gets requested from IT or the data team, it waits, and by the time it arrives the question has changed. The map of where each piece of data lives sits in two people's heads, and the monthly close drags on because recurring reports get rebuilt from scratch. Azimut puts the data warehouse within reach of whoever has the question, with the permissions they already have.
What Finance and Data gains
Asking in plain language
"Sales by product line last quarter compared with the previous one" is a business question, not a SQL query. With the data warehouse connected, whoever has the question writes it in plain language and the agent translates it, runs it and returns the result. No ticket, no waiting. The data team is left with the questions that genuinely need its judgment.
Knowing what's there
In most companies, knowing which table holds the real margin or which field is the right one for the invoice date is two people's knowledge. When the agent navigates schemas, tables and relationships and answers "where is this?", that map becomes the company's. Someone joining the data team finds the right table without asking, and whoever knew it stops being the only one who can answer.
Closing sooner
Closing and periodic reporting repeat the same structure every month with new data. The agent produces the report draft with the period's data and the person responsible reviews, comments and signs off. Closing days shrink because the mechanical work comes out already done and the team's time goes into understanding variances, not building the table.
The agents you turn on
| Agent | What it does | What gets measured |
|---|---|---|
| Natural-language queries | Translates business questions into queries against the data warehouse. | Reports obtained without asking IT. |
| SQL assistant | Generates, debugs and optimizes queries aligned with the data model. | Queries resolved without the data team. |
| Data catalog | Navigates schemas, tables and relationships and answers where each piece of data is. | Time to find the right table. |
| Spend analysis | Analyzes and reconciles spend, including AI spend itself by team. | Hours spent and accuracy. |
| Close and reporting | Drafts the periodic reports. | Days to close. |
You start with natural-language queries. It's the agent seen by the most people outside the data team, the one that drains the request queue from week one, and the one that makes the value of a connected warehouse visible. The other agents build on that same connection and the same data model, so the work of connecting and describing the warehouse gets done once and serves all five.
Which systems it works with
Finance and Data agents work on your data warehouse, Snowflake or BigQuery, and on the supporting document repository where policies, closing procedures and report templates live. One important note: there's no direct ERP connector. SAP or Dynamics data is queried through the warehouse your company already replicates it into, which is how it's usually consumed for reporting anyway. Each person sees only what they could already see in the source system. The specific integrations are confirmed with your team during rollout, and only the ones tested end to end get activated.
Which model fits
Natural-language queries and periodic reporting work well with a mid-tier model: it translates the question into SQL reliably and writes a clear report at a contained cost per query. Complex analyses, reconciliations across many sources or questions that require reasoning about the data model, justify the top-capability model. In Azimut, every user picks the model per task from OpenAI, Anthropic, Google and Mistral, and the team's agents don't depend on the model they were built with.
Where to start: the baseline
Before turning on any agent, you measure how data requests work today. In Finance and Data that's two numbers: report requests reaching IT or the data team each month, and the days the accounting close takes. Both already exist, in the ticket queue and the close calendar, and get taken as they are. You also note who's already using some AI tool on their own, with what and how much the company spends on it.
Week 0. You collect report requests from the last month, the days the last close took, and current AI use across the company: how many people, with what tool, and at what cost.
Month 3. You compare those same numbers against Azimut's usage panel by user, team, model and tool, which is also the source the spend-analysis agent itself reconciles against to work out what AI costs each team.
Frequently asked questions
Does it replace my data team?
No. The data team stays the owner of the data model, quality and the hard questions. The agent resolves the simple, repeated questions that come in as tickets today. What changes is that the data map and the queries that work stay on the platform, available to the whole company.
Do I need an integration project?
No. Azimut connects to your data warehouse, Snowflake or BigQuery, and to the document repository with integrations from its catalog, which are tested before being activated. What there isn't is a direct ERP connector: SAP or Dynamics data is queried through the warehouse you already replicate it into.
Who builds the agents?
At first, Azimut together with your team: the first agent is configured during rollout on your data model and shared with the whole company. From the second month on, power users on the data and finance teams build their own, for example one per recurring report, and publish them for the rest.
Measure how long a report takes today
Request a demo and we'll come away with your finance and data team's baseline numbers.
Request a demo