Everything you need to judge the AI analyst.
Spotonix is the AI analyst that interprets before it queries. Start with a live demo, read how Intent Algebra turns a question into a visible plan, compare it to a general LLM, then follow the thinking behind it.
Watch the analyst interpret a question and show the plan you approve — before any SQL runs.
Ask a business question and watch the ambiguity gate live — see the interpretation and inspect what runs, instead of taking it on faith.
The path from a business question to an approved, reusable answer — Learns, Interprets, Compounds.
The mechanism: bind intent, ground concepts in the Context Graph, and close the plan before any SQL runs. Interpretation before execution.
How the system is built, how it compares, and the assurance behind it.
The Context Graph remembers definitions and accepted plans; governance runs from intent through execution, in your environment.
A frontier model writes SQL beautifully but does not know what your business means by "active" or "revenue." Spotonix answers from a plan you approved, checked before it runs.
Approved, reusable answers that compound — without a queue forming behind the data team.
Measure what repeated business questions cost your team — analyst time, rework, and decision delay — on your own baseline, then prove it on one recurring workflow.
The full case for interpretation before execution, with worked traces and design-partner results as they land.
Where BI is going, how analysts actually think, and why interpretation beats generation. Read the blog →
BI Works. So Why Do Only 16% Use It Regularly?
BARC reports strong benefits where BI is used, yet only 16% regular use in large enterprises. We think part of the gap is translation: from a new business question to governed analysis.
The Engine Is Not the System
Anthropic published a detailed account of how it runs self-service analytics with Claude. Read closely, it's the strongest case yet for why a model alone won't do your BI — and what has to sit around it.
See the interpret-first behavior on a real question — then on your own data.