Data Teams
Reduce the request backlog, stay in control. This is a self-service data platform where data teams stay the owners of every certified definition while business users self-serve inside the boundaries the team sets.
Get guided setupWrite the rules once, let business users self-serve inside them
Data teams built for QuaerisAI define the semantic layer once: certified metrics, business logic, and access policies. From that point forward, business users ask questions directly in plain language, a text-to-analytics interaction, and get answers grounded in those definitions, without a new ticket for every request. The certified definitions themselves function like a governed data catalog: one place where a metric’s meaning is decided, instead of scattered across spreadsheets and tribal knowledge.
The data team keeps ownership of every definition and every access policy. Self-serve does not mean less control, it means the control is enforced automatically at query time instead of manually on every individual request.
Benefits
This agent framing helps data teams shift from ticket processing to strategy, without losing control of definitions.
- Time efficiency: Reduce time spent processing ad-hoc requests that self-serve can now answer.
- Consistent definitions: Every self-serve answer draws from the same certified definitions the team owns.
- Traceable answers: Every query is logged with its prompt, definition, and result.
- Governed access: Business users only see what their role permits, enforced automatically.
- Team capacity: Time freed from the request queue goes toward infrastructure and strategy work.
- Consistent methodology: The same certified logic applies whether a request comes through a ticket or self-serve.
Problem addressed
Data teams are frequently a ticket-processing function by default: every ad-hoc question, however small, becomes a request in a queue. That leaves little time for the infrastructure, modeling, and strategy work that actually requires a data team’s specific expertise.
Self-serve access without governance creates a different problem: dangerous slicing, mismatched metrics, and inconsistent answers. QuaerisAI’s approach lets a data team write governance rules once and have every self-serve answer respect them automatically.
What the agent does
- Lets business users ask questions directly in plain language
- Resolves every answer through certified definitions the data team owns
- Enforces access policies automatically at query time
- Logs every query with its definition, source, and result
- Reduces the ad-hoc request queue without reducing data team control
Why do this with AI
The bottleneck in most data teams is not analytical capability, it is the sheer volume of repetitive ad-hoc requests competing with higher-value work. An agent grounded in certified definitions can absorb the repetitive volume while the data team keeps full ownership of what those definitions actually mean.
Instead of processing the same category of request repeatedly, a data team can write the definition once and let self-serve handle every future instance of that question.
Who this agent is for
- Reduce time spent processing repetitive ad-hoc requests
- Keep ownership of certified definitions while enabling self-serve
- Enforce access policies automatically instead of manually per request
- Free up capacity for infrastructure, modeling, and strategy work
- Maintain a full audit record of every self-serve question asked
How it works
The agent resolves each question through the same governed pipeline as every other QuaerisAI agent: identity and permissions are checked first, the question is mapped to certified definitions, a governed query runs against the source data, and the result is returned with a full audit record attached.