Deciding between building an internal team or hiring a specialized solution to structure your product data?

At some point, every e-commerce or marketplace business faces an unavoidable question:
How do we structure product data in a reliable and scalable way?
That’s when the dilemma begins: build an in-house team or rely on a specialized solution like Koya?

1. Building a data science team in-house

In theory, creating an internal team focused on data science and machine learning might seem like the most straightforward option. But in practice, it hides a steep and expensive learning curve. There’s a high cost involved in hiring qualified professionals, plus the time it takes for the team to understand the nuances of your operation and deliver the expected results.
This is especially critical in the digital space, where mistakes translate directly into lost sales, returns, and customer distrust.

The real question is: Is this a core area of the business worth allocating time and resources to, at the expense of other priorities?

2. Building an operational team in-house

Some companies choose to address the problem with a more manual, operational team. This approach may work in the short term, but it doesn’t scale. Each analyst interprets the data differently, employee turnover tends to be high, and the results are inconsistent.
If product data is a one-time issue, this might be enough, but if the business is growing, and so is the variety of products, inconsistencies will become a major issue. Eventually, the team will have to grow significantly just to keep up with volume.

3. Outsourcing manual data work

Another common path is to outsource this operational work to third-party vendors that use offshore labor. However, this model often delivers even lower quality than in-house operational teams. Language barriers, lack of standardization, and virtually no accumulated learning create major quality and scalability problems.

Why accuracy matters when handling product data

When it comes to product data, accuracy is the most critical variable.

With the rise of LLMs like ChatGPT, many teams ask:
"Why not just use a generic AI tool to extract or generate product attributes?"

The answer is simple:
Generic models typically deliver 60–70% accuracy, and that’s just not good enough for e-commerce.
This means 3 out of 10 attributes will be wrong — and worse, the model won’t tell you which ones. It may confidently “hallucinate” incorrect data, like stating that an iPhone has more memory than it actually does.
That leads to a chain of problems that directly affect your bottom line and your customers’ trust.

At Koya, our focus is to solve exactly that, with precision. We use proprietary models, trained with industry-specific rules and ongoing validation. This allows us to deliver product data with 95%+ accuracy, even in complex categories. That’s what we do today for verticals like auto parts, CPGs, cannabis, construction materials, and sports equipment, among others — and we’re constantly expanding.

If your catalog has thousands or even millions of products, this isn’t a problem you can solve with a script or a well-crafted prompt. It’s a structural challenge, and that’s exactly why I created Koya.