Review Results

Tukun.ai is designed for inspectable analysis, which means review is part of normal product use, not a special edge-case workflow.

What you are reviewing

You are not only reviewing whether the chart looks plausible. You are reviewing whether the answer is fit for the decision you want to make.

That means checking:

• the question interpretation
• the data scope
• the metric logic
• the result shape
• the evidence strength

The review checklist

1. Intent

Did the system answer the question you meant to ask?

Look for:

• the right metric
• the right entities
• the right filters
• the right time range

2. Result shape

Did the answer come back in the form you expected?

Examples:

• a weekly trend instead of a monthly total
• a breakdown by plan instead of one global number
• a ranked list only when you asked for ranking

3. Data scope

Did the analysis use the right source and plausible tables?

If the source context is wrong, the rest of the answer is not worth debating.

4. Metric logic

Are count, sum, average, rate, or distinct-count semantics consistent with how the team defines the metric?

This is the point where semantic modeling often becomes necessary.

5. Evidence strength

Is the answer strong enough for the decision at hand?

Use a higher bar for:

• executive reporting
• pricing or revenue decisions
• customer-facing commitments
• anything that will be operationalized across a team

When to refine instead of share

Refine the result when:

• the answer is directionally useful but not yet exact
• the metric definition is still under debate
• an important exclusion was missing
• the segmentation is incomplete
• the result depends on stale or partial data
• you uploaded a file and the first answer is still too general to trust without a narrower follow-up

When to model instead of refine

Model the logic when:

• the same ambiguity appears across multiple questions
• users keep asking for the same metric in different words
• the same exclusion rules keep being restated manually
• the team wants a definition to become shared truth

When to save

Save a result only when another person in the account could reuse it without reopening the original debate about what the metric means.

That is the threshold between exploratory output and reusable product asset.