Verticals · AdTech
Your teams answer questions about spend, performance, and audience all day — and the hard part is agreeing on the terms. Which "conversion," which "active user," which attribution window, whose viewability standard. Text-to-SQL tools make that worse: they write fresh SQL and re-guess which definition you meant every run, so the same performance question returns a different number tomorrow — and no one can say which answer the campaign decision was actually based on.
The alternative today
Every option in front of a performance-analytics team either guesses which metric definition you meant or keeps several of them in play at once — and none of them make the next campaign answer one you'd defend in a QBR or to a client.
Text-to-SQL & LLM copilots
The tool writes fresh SQL from the schema and silently picks one meaning of "conversion" or one attribution window each run. Nothing surfaces the choice, and the interpretation can change between two identical questions — so a number moves and no one knows why.
Dashboards + platform exports
The ad platform, the analytics stack, and the finance sheet each define active users, conversions, and viewability their own way. Every campaign readout starts with a reconciliation fight over which number is real before anyone can talk about what it means.
The cost of waiting
Performance questions move fast — pacing, pause, reallocate. When the tool guesses the definition under time pressure, spend gets shifted on a number that meant something other than what the analyst assumed, and the mistake surfaces later.
The outcome
Instead of regenerating SQL and re-guessing your terms, Spotonix — the AI analyst that interprets before it queries — resolves the question against definitions your business already agreed, surfaces the ambiguity when a term has more than one meaning, and compiles from the plan you approve. Agree what "conversion" means once, and every campaign answer that uses it is consistent and defensible.
The who → Segments
Active users, target cohorts, and placement groups are defined once as Segments in the Context Graph — referenced everywhere instead of re-derived a different way on each dashboard and each platform export.
The how much → Calculations
Conversion, cost-per-acquisition, viewability, and the attribution window live as Calculations that carry the logic your teams agreed — the same definition behind every answer that uses them, not a fresh guess each run.
The reusable intent → Analysis Patterns
A pacing check or a channel-performance cut becomes an Analysis Pattern — the semantic glue binding Segments and Calculations into a question the team can re-run for every campaign and every review.
Governed from intent through execution
How it works
A plain-English performance question stops being a guess. Intent Algebra resolves it into a visible plan built from your own definitions, surfaces the ambiguity instead of silently choosing, and only compiles when the plan closes.
Someone asks "which campaigns converted best last week?" No SQL, no special syntax — the question the performance team actually has.
Intent Algebra binds the terms to the Segments, Calculations, and Analysis Patterns your teams approved, then shows the plan — which definition of "converted," which attribution window, which scope — before executing. When "conversion" has more than one accepted meaning, it asks which one instead of guessing. You approve it once, and that becomes the reused definition.
When the plan closes it compiles to a query, and the generated SQL is validated against the bindings you approved before it executes. It runs as the logged-in user against your warehouse — inside your environment, under your existing permissions.
Representative questions
One definition
"The ad platform, analytics, and finance each report a different conversion number. Which one do we use?"
Spotonix surfaces the competing definitions, shows the difference, and lets the team settle on one Calculation that then drives every campaign answer.
Attribution
"Is this week-over-week lift real, or did someone quietly change the attribution window?"
The plan shows which window the answer used. Change the window and it's a visible, approved choice on the record — not a silent reinterpretation between two runs.
Self-service under governance
"Can campaign managers pull their own performance numbers without inventing new metric definitions?"
They ask in plain English; Intent Algebra composes only from the definitions your teams approved, and every answer runs under existing permissions.
Consistency
"If two analysts ask for CPA by channel, do they get the same logic?"
The plan each analyst approves is visible, and its generated SQL is validated against the CPA and channel definitions your teams accepted before it runs — so the interpretation is on the record, not re-guessed between runs.
Proof & status
The mechanism behind consistent, defensible answers: how a question is resolved into a visible plan built from your definitions, and only compiled once the plan closes.
Ask a performance question and see the plan before any SQL runs — and watch the ambiguity gate ask which "conversion" you meant instead of guessing. That behavior is live today.
We're working with early design partners to validate interpret-before-execution on high-volume performance data. We're not publishing customer results or naming firms yet — if contested metric definitions and answers you can defend are the problem in front of you, talk to us about the design-partner program. Ask about the design-partner program →