Using Generative AI in SaaS Data Migration

At ClonePartner, we’re always eager to adopt cutting-edge technology. Recently, we explored how AI could support our work in migrations, custom integrations, and data synchronization. While it’s a bold claim to say that AI can do everything, we can confidently say that AI excels at specific tasks. In this blog post, we’re diving into how we leverage AI for data migrations.

Where Generative AI Shines in Data Migration

Broadly speaking, AI is especially helpful for automating repetitive tasks. Here are some categories of work where AI provides significant value:

• Grunt Work: Automating tedious processes, allowing our engineers to focus on higher-level decisions.

  • If this is X, do Y: Setting conditions and rules.

  • Fill this for me: Completing data fields or formats.

  • This is the first iteration, finish the rest: Scaling up what has been manually set in motion.

  • Find the differences between Set A and Set B: Spotting discrepancies between datasets.

These tasks are time-consuming when performed manually, but AI handles them with impressive efficiency.

Zero to One vs. One to Ten: Where AI Fits In

The real power of AI in data migration lies in what we call the One to Ten stage. Here’s what that means:

• Zero to One: This is the foundational stage—setting up data pipelines, designing migration processes, and making strategic decisions. These tasks require the expertise of experienced engineers who can make informed design choices and address the complex “how” of approaching a problem.

• One to Ten: Once the framework is established, replicating that work and scaling it up is where AI truly shines. The repetitive nature of this process makes it ideal for automation. For example, what might take a human five days to accomplish, AI can complete in just one day. That’s a phenomenal boost in efficiency, right?

We want to share some specific use cases where AI has been incredibly effective for us at ClonePartner. These scenarios have been tried and tested by our engineering team, and we’ve seen firsthand how generative AI can supercharge our data migration workflows.

Real Use Case: Finding Duplicates and Correcting Names for a Migration to Gorgias

One of our clients needed to clean up their customer data before migrating it to Gorgias. The challenge was twofold: finding duplicates and correcting the names to ensure consistency.

Detecting duplicates wasn't as straightforward as spotting the same name appearing twice. We needed to catch cases like “St. Germain Paul” vs. “Saint Germain Paul”—names that are functionally identical but formatted differently.

This is where AI did a phenomenal job. We used it to scan and clean up 22,000 records in a matter of hours—an amount of work that would have otherwise taken a full week if done manually. Moreover, we’re confident that no human approach could have caught every potential duplicate with such accuracy. This level of data cleaning could certainly be coded by hand, but an engineer would need to think of every possible variation and explicitly program it in. The AI, on the other hand? It understood our intent with just a single prompt.

• LLM used: GPT-4o

The results speak for themselves. By leveraging AI, we not only saved significant time but also ensured a higher degree of accuracy.

AI: A 10x Aid for the Migration Process

We’re just scratching the surface of what’s possible. As we continue to use AI in data migration, we’ll update this blog post with more examples of how generative AI proves to be a 10x aid to our team.

Stay tuned—more insights are coming!