The pace of change in search and discovery technology has never been faster. Generative AI is transforming how people search, ask questions, and browse. Queries are longer, more specific, and more natural-language based. Especially in ecommerce, customer behavior is shifting fast, driven by trends on TikTok, Instagram, and other social platforms. Shoppers expect smarter, more relevant experiences—instantly.

Enterprises have responded with AI tools like semantic search, recommendation engines, personalization strategies, and machine learning (ML) models for ranking. But there's a catch: all these tools, from the most basic keyword matchers to our most advanced NeuralSearch models, rely on quality data. When the product data is messy, inconsistent, or incomplete, even the most sophisticated models fall short.

That’s where data enrichment platforms like Velou can help. Velou specializes in enriching product data using computer vision and natural language processing (NLP), giving search engines like Algolia the structured, complete, and consistent data they need to truly perform.

The problem: garbage in, garbage out

Relevance is the holy grail of ecommerce search. If your users can’t find what they’re looking for, they’ll bounce. But achieving high relevance isn’t just about algorithms—it’s about the inputs.

Let’s consider a big-box retailer selling affordable baby clothes. A shopper searches for "6 month baby boy clothes." Here’s a typical product in the catalog:

{

  "title": "Blue Size 00 T-Shirt",

  "category": "Clothing > Baby",

  "tags": []

}

This product might be relevant, but the data gives the search engine little to work with. There's no mention of gender, age, or even the product type "baby boy clothing."

Neural search engines like Algolia’s might recognize that a t-shirt is clothing and even make some semantic leaps. For example:

• Partial Fix: Neuralsearch might connect "t-shirt" with "clothing."

• Limitations: The lack of age and gender tags means it’s unlikely to match "baby boy."

Poor data quality affects the ranking of results as well. Algolia features like query categorization and multi-signal ranking depend on consistent data across products—categories, product types, features. If one product is labeled "Chocolate" and another is labeled "Chocolate > Bars," they won’t be treated as part of the same concept. Learning signals get diluted.

Consider a grocery site:

{

  "title": "Velvet Smooth Chocolate",

  "category": "Chocolate > Bars"

},

{

  "title": "Chocolate Milk Drink",

  "category": "Milk"

}

A user searching for "milk chocolate" might be shown the drink first, because "milk" and "chocolate" are exact matches in that title. But that’s not what the customer wants. Algolia's query categorization could help—but if the category structure is inconsistent, the model can’t learn reliably.

Why data quality breaks

Most ecommerce data is:

• Manually entered: which means it’s inconsistent.

• Sparse or unstructured: missing fields or using free text instead of structured tags.

• Out of sync with how customers search: e.g. industry terms vs. colloquial terms.

Fashion is a prime example. As Velou’s Head of Fashion notes:

“From off-shoulder to halter to asymmetric and everything in between, there are at least 25 different necklines. Plus the words and phrases we use to describe things in fashion are always changing and evolving.”

Teams don’t always have the time or domain expertise to capture those nuances. As a result, even powerful tools like Algolia’s various AI capabilities can’t deliver their full potential.

The solution: AI-powered data enrichment

This is where Velou steps in. Velou uses advanced computer vision and NLP models to:

• Analyze product images to recognize features like sleeve type, neckline, material, and color.

• Parse descriptions to extract missing or implied data.

• Generate structured tags that are clean, normalized, and semantically meaningful.

For example, Velou could turn this:

{

  "title": "Blue Size 00 T-Shirt",

  "description": "Soft cotton t-shirt, great for warmer months."

}

into this

{

  "title": "Blue Size 00 T-Shirt",

  "description": "Soft cotton t-shirt, great for warmer months.",

  "tags": ["baby", "boy", "short sleeve", "summer", "cotton"],

  "category": "Baby > Boys > Tops"

}

Now, when a customer searches "6 month baby boy clothes," the enriched product has a much higher chance of surfacing—and ranking correctly.

Velou + Algolia: how it works

Velou integrates with Algolia in a couple of key ways:

Option 1: Pre-processing the catalog

Velou enriches product data offline or as part of a batch process. The enriched data is then pushed into Algolia’s index via API. This is ideal for teams that want to maintain control and audit enriched data before it goes live.

Option 2: Index-time enrichment

For dynamic or fast-changing catalogs, Velou can enrich data in near real-time as new products are added. Algolia ingests this structured data and uses it immediately in features like Neuralsearch, query categorization, and personalization.

In both cases, Algolia’s relevance and ML ranking models have a much better foundation to work from. Structured data improves recall, but more importantly, it improves precision.

The payoff: better AI starts with better data

So what happens when you combine enriched data with advanced search?

• Improved relevance: Products match better to long-tail and nuanced queries.

• Stronger ML models: Multi-signal ranking and query classification models have consistent, high-signal inputs.

• Better UX: Customers find what they want, faster. Bounce rates go down, conversions go up.

And it’s not just about search. Enriched data powers better faceting, filtering, recommendations, and even analytics.

Conclusion

AI is transforming ecommerce. But without the right data foundation, even the smartest models won’t deliver. As teams race to adopt neural search and ML-based ranking, it's crucial to ask: what data are we feeding these systems?

Platforms like Velou give ecommerce teams the ability to fix their product data at scale. And when paired with Algolia’s AI capabilities, that means less manual tuning, fewer surprises, and more predictable, high-performing search and discovery experiences.

Remember: AI is not magic. It’s math. And math needs good data.

Looking to explore how enriched product data could impact your search experience? Get in touch with us or learn more about Algolia and Velou's partnership? Get in touch with us.