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Your teams ask in plain language and get the answer from your data and your company's knowledge sources, without a BI ticket and the days of waiting that follow. The assistant and its AI model run in your own cloud, so your data stays inside your environment.
The assistant at a glance
In a chat, in plain language
Your data environment and knowledge sources such as Power BI, Google Docs, Slack, Guru or Pipedrive
Installed in your own cloud, AI model included, for example Azure OpenAI in your tenant
Anyone on the team, experienced or new, without BI tool training
Our AI in production elsewhere: at a global professional services group, it writes the quarterly variance commentary and hands back an estimated 13,400 hours a year.
Which portfolios are showing early stress signals this quarter?
Checks the portfolio KPIs for anomalies and answers senior people directly, without routing through the data team
Which player segments are showing early churn signals this month, and what is driving it?
Reads player behaviour, promotion returns and revenue across online and land-based channels together
Which categories drove margin loss last week, and what is the root cause?
Works through sell-through, promotions and pricing across channels before the next meeting
Which production lines are underperforming against target, and why?
Searches operational data across sites for bottlenecks and the factors behind them
A question becomes a Jira ticket, and the answer arrives days or weeks later.
A week into a campaign you see you are not tracking what matters, and the report does not exist yet.
A new BI tool often means months of training before people use it.
Each request for a new KPI, a report tweak or a custom view joins the same long queue.
Installed in your cloud
Every component, AI model included, runs in your own environment.
Connected to your sources
Your data environment and the knowledge sources your teams already use.
Asked in a chat
Plain-language questions, from experienced users and newcomers alike.
Answered from your data
The answer names its source and shows which figures moved together.
Nowhere outside your environment. Every component, the AI model included, is installed in your own cloud, for example Azure OpenAI in your tenant, and each user sees only what they can already see in the source.
Your data environment and your company's knowledge sources, such as Power BI, Google Docs, Slack, Guru or Pipedrive.
From the data and documents already in your environment. Each answer names the report or document it came from, and when the data is not there, the assistant says so.
No. It answers the routine questions in the chat, including a new KPI or a view your reports do not show, so your analysts spend their time on the harder questions.
Astral Forest. We built the finance platform of a global professional services group, where our AI now writes the quarterly variance commentary in production. Read the case study
In a 30-minute demo with Michał Dębski, co-founder, you put your own question to the assistant and leave knowing which sources it would connect to first.
