How embeddings can solve your most complex enterprise data problems

If you’re looking to solve some of your most complex enterprise data problems, embeddings might just be the answer.

Traditional methods of data retrieval, which rely on rigid, keyword-based searches, are no longer enough to meet the needs of modern organizations and their customers. 

From improving search and recommendations to enhancing fraud detection and using MongoDB for AI and machine learning, these numerical representations of data that capture semantic meanings offer some incredible business benefits. 

In my previous blog I discussed why vector search is an important part of future-proofing MongoDB data strategy, why it matters and how to get started. 

In this piece, we’ll take a closer look at the role of embeddings, which vector search relies on to quickly find and rank similar items based on meaning rather than exact matches.

Why embeddings matter 

TL;DR: 

• Improve customer experience with smarter search and recommendations
• Boost operational efficiency by reducing manual data retrieval efforts
• Strengthen security with better anomaly detection
• Unlock insights from previously untapped unstructured data

Looking to use MongoDB for AI and machine learning? Embeddings allow AI systems to process unstructured data – including text, images, and audio – by converting them into structured, comparable vectors. This allows predictive models to function.

In an increasingly data-driven world, embeddings are the secret to extracting value from huge, unstructured datasets.  You may not realise it, but if your company uses AI or machine learning models, you’re already using embeddings. 

Without the right embeddings, search and recommendation systems are at risk of slowing down or becoming inaccurate at scale.

There’s also a real risk of falling behind competitors who are already providing AI-enhanced search and recommendation experiences. With the rise of AI-powered tools like ChatGPT, users now expect intuitive and context-aware interactions. Businesses that fail to adapt may struggle with customer satisfaction and retention.

Embeddings allow your teams to make the most of the power of AI without requiring deep expertise in machine learning. With the right tools and strategies, even non-technical teams can use embeddings to improve decision-making and operational efficiency.

In short, if your embeddings aren’t well optimized, you’re leaving money on the table. 

How embeddings solve business problems

Embeddings are useful for any organizations with large amounts of complex data and low latency needs. 

In short, embeddings are useful to any organization hoping to use AI in search, recommendation engines, fraud detection, and predictive analytics. But they have particular value where speed and accuracy are critical.

From anticipating customer behavior and forecasting trends to tailoring user experiences to increase sales, the right embeddings make these processes more accurate and scalable. 

Embeddings’ ability to understand similarity within datasets allows similar items, whether its content, products or services, can be accurately identified and suggested to users.

Here are some industry-specific examples:

E-commerce: Customers expect instant, relevant results in response to queries, such as suggesting products that go well with previous purchases (such as offering vacuum cleaner bags to a customer who previously bought a vacuum cleaner) or offering alternative colors of a product.

Travel and hospitality: Embeddings improve search functionality, even handling complex queries like “affordable luxury hotels near the Eiffel Tower with lots of good reviews and free parking.”

Insurance and financial services: Embeddings can match similar claims or contracts, which can streamline quote generation and fraud detection.

Healthcare and pharmaceuticals: Embeddings can analyze similarities between patient cases, symptoms, or drug interactions.

Security: Embeddings simplify data retrieval from vast document repositories, so your team can find the right information faster.

Real businesses, real benefits 

Businesses are already seeing incredible benefits from this. BrandAlley, a UK flash-sale e-commerce platform, needed to deal with complex real-world, multimodal data. It used sophisticated embedding techniques to put a recommender system in place. 

This identifies products users are most likely to purchase, before they search. This personalization brought a significant uptick in conversion rates, average order value and revenue per user as shoppers are shown the most relevant items instantly.

The results speak for themselves, as BrandAlley achieved a 77% higher conversion rate, 68% increase in average order value and 60% growth in revenue per user.

How to add embeddings to your data strategy

If search or recommendation features are central to your business, embeddings should be a key part of your strategy. They not only improve system efficiency and accuracy but also enable scalability that traditional methods cannot achieve. Without embeddings, search and recommendation systems are at risk of slowing down or becoming inaccurate at scale.

Businesses typically approach embeddings in one of two ways:

1. In-house experimentation: Companies with data science teams often start with small proof-of-concept (POC) projects using text embedding models before scaling up.
   
2. Partnering with experts: Businesses that lack in-house expertise or need faster implementation often work with third-parties to develop and integrate embedding-based solutions.
   

Regardless of the approach, embedding quality is critical. Poorly optimized embeddings lead to inaccurate results, limiting their business impact.

To implement embeddings successfully you need to consider unique embedding strategies for different data types, ensure scalability with efficient processing and retrieval techniques, align embeddings with business logic and make them accessible to non-technical users.

Third-party companies can help businesses overcome these with intuitive interfaces, infrastructure, and scalable solutions. This removes the need for deep AI expertise in your organizations, making the technology more accessible. 

Measuring success

The best way to evaluate an embedding-based system is not just through benchmark performance but through its impact on business outcomes. Tie metrics directly to revenue generation, cost savings, or efficiency improvements. If a new embedding model improves search accuracy but doesn’t lead to higher conversions or user engagement, its value is limited.

For decision-makers, key performance indicators (KPIs) for embedding-based solutions should include:

• Conversion rates: More relevant product recommendations lead to higher sales.
• Operational efficiency: Faster, more accurate searches reduce time spent on manual data retrieval.
• Customer satisfaction: AI-driven personalization keeps users engaged and returning.

Final advice for decision makers

As AI continues to evolve, expectations around search and recommendations will only increase. Embeddings have a vital role to play in meeting these changes. But, decision makers must maintain control over decision-making, using AI as an augmentation tool rather than replacing human intelligence entirely.

Those unsure where to begin should start with a clear business problem. You don’t need a fully defined technical specification – just a clear understanding of your goals. Superlinked and Studio 3T are two businesses with tools to help you at any stage of your embedding journey.