Verticals · FinTech
Your teams answer questions about exposure, revenue, and active accounts all day — and half the work is proving which definition they used and where the number came from. Text-to-SQL tools make that worse: they write fresh SQL and re-guess what "exposure" means every run, so the same question can return a different answer tomorrow and no one can show a regulator how it was produced.
The alternative today
Every option in front of a regulated analytics team either can't show its work or keeps two definitions of the same number in play — and none of them make the next answer one you'd put in front of an examiner.
Text-to-SQL & LLM copilots
The tool writes fresh SQL from the schema and infers what "exposure" or "net revenue" means each run. There's no lineage a regulator will accept, and the interpretation can quietly change between two identical questions.
Dashboards + manual reconciliation
Trading, risk, and finance each encode the same metric their own way. Every board pack and every filing starts with a reconciliation fire drill just to agree on one number before anyone can discuss what it means.
The cost of waiting
Analysts spend more time defending how a number was produced than analyzing it. Under scrutiny, the cost of proving the figure buries the value of the insight — and the backlog behind it keeps growing.
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, shows the plan before anything runs, and compiles from that plan. The interpretation is something you can read, review, and put in front of an auditor.
The who → Segments
Active accounts, counterparties, and product lines are defined once as Segments in the Context Graph — referenced everywhere instead of re-derived differently on each desk.
The how much → Calculations
Exposure, net revenue, and risk-adjusted return live as Calculations that carry the logic your teams validated — the same definition behind every answer that uses them.
The reusable intent → Analysis Patterns
A risk review or a revenue cut becomes an Analysis Pattern — the semantic glue binding Segments and Calculations into a question the business can re-run and review each cycle.
Governed from intent through execution
How it works
A plain-English question stops being a guess. Intent Algebra resolves it into a visible plan built from your own definitions, and only compiles when the plan closes.
Someone asks "what's our exposure to this counterparty this quarter?" No SQL, no special syntax — the question the business actually has.
Intent Algebra binds the terms to the Segments, Calculations, and Analysis Patterns your teams approved, then shows the plan — which definition of exposure, which scope, which filters — before executing. Ambiguity is surfaced and asked, not silently guessed.
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
"Risk, finance, and the trading desk each report 'exposure' differently. Which one is right?"
Spotonix surfaces the competing definitions, shows the difference, and lets the business settle on one Calculation that then drives every answer.
Auditability
"When someone asks how this number was produced, what do we actually show them?"
The plan the answer compiled from — the definitions, scope, and filters used — readable in plain language, not a wall of generated SQL to reverse-engineer.
Self-service under governance
"Can business users get their own answers without us losing control of the 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 people ask the same question, do they get the same logic?"
Once the plan is approved, the definitions it binds to are visible on the record, and the generated SQL is validated against them before it runs — so the interpretation isn't re-guessed between runs.
Proof & status
Ask a question and see the plan before any SQL runs — and watch the ambiguity gate ask instead of guess. That behavior is live today.
We're working with early design partners to validate interpret-before-execution in regulated finance. We're not publishing customer results or naming firms yet — if auditable, governed answers are the problem in front of you, talk to us about the design-partner program.