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Accuracy layer

Answers are only useful if you can trust they're actually right.

Supper's accuracy layer is why ours are

Most AI data tools generate SQL on the fly and hope for the best. Supper runs every question through a semantic model built specifically for your company. That’s your metrics, definitions, and business logic before a single number reaches you.

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See how it works

How it works under the hood

Six things happen before
you see an answer.

01
Understand the question

The agent parses intent and identifies ambiguities before doing anything else. Supper knows “what's our churn this month?" means something specific at your company.

02
Select the right tables

Supper maps your question to the relevant data sources across warehouses, SaaS tools, and databases using schema metadata that it maintains continuously.

03
Apply your business logic

Your definitions come in here. That’s your ARR formula, qualified lead criteria, and customer lifecycle stages. Everything is encoded, reviewed, and owned by your data team.

04
Write & validate the query

Supper writes the SQL or Python, then checks it against known rules before running it. Queries that don't pass validation don't reach your data.

05
Run against live data

The query runs directly against your live sources. No stale exports or intermediate copies. The answer reflects what's true right now.

06
Return with audit trail

An answer is only valuable if you trust it. Every answer shows its working: the query, the sources, and the logic applied for anyone to inspect.

Business logic layer

Your definition of "revenue." Not a generic one.

Every company calculates its key metrics differently. MRR, churn, CAC, and pipeline live in spreadsheets, onboarding docs, and people's heads. Supper encodes them into the model before your team asks their first question.

Metrics defined once, applied consistently across every question, every user

Your data team reviews and approves every definition before it goes live

Definitions stay editable — as your business evolves, the model evolves with it

Semantic structure layer

A map of your entire dataworld. In plain language.

We map every table, field, and relationship in your stack so it's human- readable and AI-navigable. “cust_arr_ltm_usd” becomes "Customer ARR (last twelve months)." A question spanning Salesforce, Stripe, and your product DB just works without pre-building every join.

Covers every connected data source in a single unified model

Human-readable names and descriptions for every field

Relationships between sources mapped and maintained automatically

Intelligence layer

The query gets written. Then it gets checked.

Supper writes SQL or Python to answer your question, then runs it through a rules engine before execution. Queries that would return wrong answers, expose restricted data, or violate policy never reach your warehouse.

AI-generated queries validated before execution, not after

Access controls enforced at the query level, not just the UI

Audit trail for every query: who asked, what ran, what returned

~10

s

Average time from question to verified answer

200

K+

Business questions answered on live data

Zero

Generic SQL guesses. Every answer uses your business logic

What it powers

What the accuracy layer powers

Agent interactions

Ask any question in plain language. The accuracy layer is what makes the agent's answers trustworthy, not just fast.

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Workflows

Automated reports built on verified business logic. Scheduled delivery of numbers everyone agrees on.

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Dashboards

Live dashboards that query your data the right way, every time. No more dashboard vs. spreadsheet disagreements.

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Ship trust, not vibes

See what accurate
answers actually look like.

We'll walk through the semantic model, run a real question, and show
you the audit trail behind the answer. About 30 minutes.

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