Getting AI to return a number is one thing. Getting it to understand the business behind that number is another.

Take a simple question: why did sales drop this month? Pulling the sales figure is only the start. Which metric definition applies, which period to compare against, and which business records to bring in all shape where the analysis goes.

Once data starts serving AI, enterprises need to rethink more than how they query. They also need to look at how business knowledge is organized, how different types of data get used, and how applications fit into real work.

On October 20, MirrorShip will host the MIP launch event. Before then, here are five questions we want to put on the table.

1. We already have a database. Why do we need a data platform built for AI?

Most enterprises have databases and data warehouses in place, along with years of reports and business documents. Is adding AI just a matter of putting a natural-language interface on top, or does the way data is organized and used need to change?

Query performance still matters. Moving from retrieving data to understanding the business, though, means metric definitions, business relationships, and task context also have to be part of the analysis.

From StarRocks to MIP, the first question we want to explore is this: how can existing data infrastructure keep delivering value, and what does it need to support AI?

2. Beyond the data itself, what does AI need to know?

"Sales" can mean gross order value to one team and revenue net of refunds to another. A "drop" month over month can look very different from a drop year over year.

Data tells you what happened. Business definitions tell you what the data means. The context of the task tells you which information matters right now.

How can this information travel with the data, so people don't have to explain it again every time? That question is a good way into the thinking behind MIP.

3. What does it take to get from a business question to an application people can use?

Once someone asks a question, the system has to access data, apply business definitions, and carry out analysis around the task. How do these steps connect into something that works in practice?

More telling than an architecture diagram is the actual workflow: what needs to be prepared, how to get started, and which tasks it can handle.

At the launch, a product walkthrough and live demo will show how MIP brings these capabilities together, and how lightweight applications can be put into use.

4. When data goes beyond tables, how do you find what you actually need?

Enterprise data is no longer just business tables. It also includes text, images, and other types of content, and different data and tasks call for different ways of searching.

Keyword search and similarity search each fit different needs. How can the two work together to help teams pull useful content out of large volumes of data?

The launch will also take the discussion from business analytics to multimodal data, looking at how retrieval supports specific AI scenarios.

5. From product demo to enterprise rollout, where do you start?

After seeing what a product can do, enterprises face more concrete questions: which task to start with, which data to connect, how to organize business definitions, and how to turn that groundwork into AI applications.

Starting from a well-defined task, rather than aiming for one agent that covers the whole business, makes it easier to see the work an implementation requires.

From data onboarding and semantic modeling to application delivery, the launch will look at the methods and practices behind enterprise implementation.

Find the answers on October 20

These five questions cover both how MIP is designed and how enterprises can help AI make better use of their data.

On October 20, we'll work through them with a product introduction, a live demo, and practitioner talks, from why the product is built the way it is to how applications get deployed. Bring your own business questions and join us.

  • Date: Tuesday, October 20, 2026
  • Time: 14:00–15:50 (GMT+8)
  • Format: Online

Register on LinkedIn: https://www.linkedin.com/events/7515230959584305152/?viewAsMember=true