robert-te-ross

Thanks for the comment. We get this question often, and our most common answer is that our customers tried to solve their mapping problems using OpenAI calls. After it did not yield the expected reliability, consistency, and scalability results, they ended up onboarding to Lume. We’ve put extensive work into the overall engine (and the underlying AI) to the point where customers have seen it as a significant value add over their attempts at leveraging OpenAI directly.

Yes! Once the transformation job has been completed, you can review the mapping in the returned job payload and our Lume dashboard. You can review, edit, and deploy the mapping pipeline from the dashboard. There are two ways to fix mappings. You can edit the target schema (e.g., make a required target field nullable) or manually override our mapping by giving the correct mapping value from the source data. I have also attached a Loom video showing this workflow: https://www.loom.com/share/95e47ead923d4911b647456174142e00

In most cases, we build these transformations in a matter of seconds. Furthermore, we can detect changes from either source or destination and change the transformation accordingly, reducing maintenance burden as well.

Our system only uses LLMs at particular points of the process, so we do not expect letting users do this to have much value. However, descriptions we generate and/or take in as input for both end and start schema columns have a significant effect on the generation of your transformations. Therefore, the ability to edit these descriptions can be a powerful way to experiment with our models.

Yes, internally, we have separate models that produce tests the final data has to pass before being presented to the user. In addition, you can define your own tests on the platform, and we will ensure transformations produced will pass those tests before deployment. We also have helpful versioning and backtesting features.