Solutions · Heads of Data & Analytics
You own the analytics backlog, the headcount line, and the definitions the business is supposed to trust. Hiring more analysts scales the queue, not the answers — and every ungoverned workaround widens the gap between what people ask and what your team can stand behind. Spotonix is the AI analyst that interprets before it queries: it reads a business question and resolves it into a plan you can see before SQL executes. In Copilot mode, the workflow pauses for acceptance; semantic checks validate material query bindings.
The alternative
Every path below is a rational response to the queue. None of them make the queue smaller, and two of them make the definitions harder to trust.
Hire more analysts
A new analyst takes months to ramp — not on SQL, on context: which tables matter, what a metric means, how the business actually thinks. The budget line grows; the backlog doesn't shrink. And when someone leaves, their undocumented logic leaves with them.
Ship a bigger BI backlog
You licensed the tools and built the dashboards, but adoption stays low and people still come to your team — because a fixed dashboard rarely answers the exact question in the room. The self-serve promise keeps not arriving.
Open up ungoverned self-serve
Let everyone point an LLM at the warehouse and the business interpretation lives inside each prompt and generated query. You are left reviewing outputs without a shared pre-execution representation of what the requester meant.
Cost of delay Every quarter you wait, the backlog compounds and more decisions get made on numbers no one can trace back to a definition.
The outcome
The repetitive work resolves against definitions your team already approved, so analysts spend their week on the analysis that justifies the headcount.
01
Recurring questions—the revenue breakdown, cohort read, and executive cut—arrive with the intended definitions, scope, and filters exposed before execution.
02
Segments, Calculations, and Analysis Patterns live as explicit context rather than remaining trapped in a prompt or an analyst's memory. Persistence and reuse are evaluated per workflow.
03
Self-serve exposes the Segments and Calculations selected for the question. Hosting, identity, permissions, and network boundaries are documented for the deployment instead of implied.
How it works
Intent Algebra resolves the Segments, Calculations, and Analysis Patterns in a business question into a visible plan. Where the question is ambiguous, it surfaces the ambiguity and asks — instead of guessing your business meaning.
In Copilot mode, plan acceptance happens before query generation continues. The generated SQL is then checked against material bindings in the accepted plan before execution.
Definitions and analytical relationships can be represented in the Context Graph. Where persistence or reuse is required, make it a named proof criterion for the workflow.
Representative questions
Each one resolves against accepted definitions, so the answer is consistent and traceable — no matter who asks or how they phrase it.
“What did churn among high-value customers cost us last quarter, by region?”
Segment High-value customers Calculation Churn cost, QoQ Pattern Regional breakdown
“How did the promo cohort retain versus control?”
Segment Promo cohort vs. control Calculation Retention rate Pattern Cohort comparison
“Which segments drove the margin change month over month?”
Segment Product / customer segments Calculation Margin, MoM delta Pattern Contribution to change
Proof
The differentiator is a behavior you can watch: material ambiguity, the visible analysis plan, Copilot acceptance, and validation of generated query bindings. Worked traces use the public TPC-DS schema and are labeled illustrative.