How to Evaluate Tukun.ai

Use this guide when you are evaluating Tukun.ai as a product, not just testing whether one demo prompt works.

What to evaluate

You should evaluate Tukun.ai on four dimensions:

1. Question quality

Can the product help your team ask concrete business questions and refine them quickly?

2. Reviewability

Can the people responsible for the answer inspect enough evidence to decide whether it should be trusted?

3. Reuse

Can a good answer become a reusable asset instead of dying in one conversation thread?

4. Governance fit

Can your team connect approved data sources, preserve account boundaries, and model business meaning without inventing a second operating process?

The best first use case

Choose a question that is:

• high value
• repeated often
• narrow enough to verify
• supported by one source you already trust

Good examples:

• weekly active accounts by plan
• monthly revenue by channel
• conversion rate by landing page cohort
• retention by signup month

Bad first examples:

• broad “tell me everything unusual”
• cross-functional strategy prompts with no shared definition of success
• questions that require multiple disconnected data sources

A realistic evaluation workflow

Step 1: pick one owner

One person should own the test. Too many parallel evaluators create noise before the team agrees on basic definitions.

Step 2: connect one representative source

Do not start with your biggest integration project. Start with one source that already answers an important recurring question.

Step 3: run three question types

Test:

• one straightforward descriptive question
• one segmented comparison
• one question that intentionally reveals ambiguity in your current metric definitions

Step 4: inspect the failure cases

A strong evaluation is not “the model answered something.” A strong evaluation is “we can tell why a result is safe or unsafe to reuse.”

Step 5: decide what should be operationalized

At the end of the evaluation, identify:

• which questions are immediately reusable
• which metrics need semantic cleanup
• which data gaps block broader rollout

Signs that Tukun.ai is a good fit

• your team already asks repeated business questions
• stakeholders care how an answer was produced
• you want one place to move from exploration to reusable assets
• you need semantic consistency more than you need flashy ad hoc chart generation

Signs you should narrow the rollout

• your source data is not yet trustworthy enough for repeated analysis
• no one owns metric definitions
• the team wants instant answers but does not accept a review step
• every important question currently requires cross-source federation

Recommended success criteria

End the evaluation only after you can say:

• we connected one approved source successfully
• we validated at least one recurring business question
• we know where ambiguity still exists
• we know which metrics are worth modeling next
• we know whether the team will actually reuse the outputs