The following scene is an illustration. The evaluation sheet has four columns: a data catalog, a data observability tool, the versioning feature in the lakehouse, and an AI-ready data platform. Twenty rows of capabilities. Almost every cell says yes. The committee still cannot decide, and the reason is in the rows. Each row asks whether a tool has a feature. None of them asks which question the tool answers when an AI result is challenged.
Many tools in this space are not substitutes for each other. They answer different questions, and the right purchase depends on which question your stack cannot answer today.
Key takeaways
- Compare AI-ready data tools by the question they answer, not by feature count.
- Catalogs, observability, and versioning answer whether data can be found, is healthy, and can be restored.
- Whether data meets a task’s requirements, and whether the validation still applies after a change, are separate questions.
- If only the first three are open, your existing platform may be enough.
Five questions to ask when reviewing an AI result
These five questions help separate the checks your team needs and form CUBIG’s evaluation framework. They are not a fixed sequence, and several tools may contribute to each answer.
| The question | Tool that usually answers it | When that is enough |
|---|---|---|
| 1. Can we find this data, and are we allowed to use it? | Data catalog and governance | The blocker is discovery, ownership, or access approval |
| 2. Is the pipeline healthy right now? | Data observability | Failures come from freshness, volume, or schema breaks |
| 3. Can we restore the data as it was on a given date? | Data versioning and time travel | You need rollback or point-in-time recovery |
| 4. Is this data fit for this AI task, with this model or agent, under these evaluation conditions? | Task-specific data preparation and evaluation | The same data works for one task and fails another |
| 5. Which data state did this run use, and does the result still hold after a change? | Run tracking, data versioning, and change-aware revalidation | Results must be traced, compared, and revalidated after changes |
These categories overlap. Your existing stack may already cover parts of questions 4 and 5. An experiment tracker, for example, can log the training data, parameters, and metrics of each run. Check what it can demonstrate before adding another platform, and note how much manual work it takes to connect and maintain the answer.
Questions 4 and 5 sit between data management and AI execution, which is the gap Syntitan, CUBIG’s AI-ready data platform, is built for. Syntitan prepares data for a specific task and creates a new data Version after each successful processing run. Ready for Model compares results before and after data preparation using the same model and evaluation conditions, then checks whether the agreed criteria are met.
Ready for Agent turns work materials into tasks and cases. Planned for December: connecting actual agent runs, addressing the gaps those runs reveal, revalidating the updated cases, and change-aware tracing and reuse.
Some teams face an earlier blocker: the data cannot yet be used for the task.
- The original data is too sensitive to send to an external AI as it is, but the LLM, RAG, or agent workflow still has to finish. That is the problem LLM Capsule addresses with a context-preserving data layer.
- The data needed for an AI task is restricted, scarce, or skewed, so there are not enough usable cases to train, test, or evaluate. That is the problem DTS addresses: it diagnoses the data and fills in the cases and conditions the chosen task needs.
When your existing data platform is enough
If your open questions are only 1 to 3, a separate layer is probably not your next purchase. Strengthen the catalog, add observability, or turn on versioning first.
A separate layer starts to pay off when teams need to repeat preparation and validation after changes.
- A model passed in the pilot and underperforms in production, and nobody can say whether the data or the conditions changed.
- Two teams use the same customer data for different AI tasks and keep redoing preparation without knowing what carries over.
- A source system changes, and you cannot tell which AI results need to be rechecked.
Three questions for your own team make the gap concrete. What does someone still do by hand before the AI can use the data? Which steps need a developer or a data scientist every time? What has to be redone when the data, the model, or a business rule changes? If the answers keep pointing to repeated preparation and rechecking, the gap is in questions 4 and 5.
Evidence about the data, and evidence about the AI task
A quick way to see which question is still open is to look at the evidence you can produce today. The left column shows what existing tools already establish about the data. The right column shows what a reviewer still needs for the AI task. The left column matters, but on its own it is not enough to judge whether the data fits a specific task.
| What your existing tools establish | What you still need to verify for the AI task |
|---|---|
| Data quality checks passed | The churn model met its recall target on this data version, with the same evaluation set used last quarter |
| The dataset is versioned | This run used Version 14, and here is what changed since Version 13 |
| The catalog shows lineage | After the source schema changed, three AI tasks were revalidated and one did not meet its criteria |
In a January 2025 insurance call classification test covering 64 classes, we used Syntitan to supplement classes with too few training examples. Weighted F1 rose from 58.55% to 88.55% on the same 687 original test records. These were self-measured, first-round results using labels assigned with an LLM (measurement record). The result is more useful when readers can see what changed and what stayed fixed. The comparison used the same model setup and the same original test set, with the training data supplemented between runs. To repeat it, the team also needs the data versions and training settings.
How to use this guide in an evaluation
- Start from one questioned result. Write down the last AI output someone challenged, and which of the five questions you could not answer.
- Ask for evidence, not features. Ask each vendor to show what they would produce for that question.
- Run the same task under the same conditions. Give each option the same data and the same task, then compare five things: time to finish, work people still did by hand, whether the result fits the task, work that had to be redone on the next run, and total cost. A longer feature list is not one of them.
- Test it against a change. Check whether that evidence would still be valid after the data or the model changes.
The tool that answers your unanswered question is the one to evaluate first. The others may still be useful, just not for this gap.
Read the detailed comparisons
Start with the comparison that matches your situation: Collibra vs Syntitan · Monte Carlo vs Syntitan · lakeFS vs Syntitan · Dataiku vs Syntitan · LLM Capsule vs blocking tools · DTS vs anonymization vs differential privacy.
