What Are Embeddings and How Can They Optimize Processes in Your Company?

Did you know there’s an artificial intelligence (AI) technique that helps companies understand the meaning of words and enhance internal processes? This technique is called embeddings and is transforming how AI systems process language. Though it might sound complex, embeddings allow AI systems to understand relationships between words, phrases, or even entire documents, making search and information analysis more precise and efficient. Want to learn more? Read on to discover how embeddings can make a difference in data management for your business.

What Are Embeddings?

Embeddings are numerical representations of words, phrases, or documents within a multidimensional space. Imagine each word or phrase has a specific “place” in this space. Words with similar meanings (like “cat” and “feline”) will be closer to each other, while words with different meanings will be farther apart.

This numerical representation helps AI understand human language better and handle complex tasks like semantic search and product recommendation.

Why Are Embeddings Useful?

Embeddings are especially valuable in AI applications because they allow systems to recognize words and the context in which they appear.

This is crucial for tasks where the full meaning of a phrase or document needs to be analyzed. Rather than processing only individual words, AI systems can capture relationships and meanings in whole phrases or texts.

This capability makes embeddings key to improving internal processes in customer service, information retrieval, and task automation.

Types of Embeddings and Practical Examples

Depending on the goals and type of information that needs to be analyzed, there are various types of embeddings that serve specific functions.

1. Token Embeddings

Token embeddings are representations of small text units, such as individual words, characters, or word fragments. They’re helpful for complex languages like Chinese or Japanese, where characters can have multiple meanings.

Example in Business: Imagine a customer support system receiving queries in different languages or dialects. With token embeddings, the AI system could understand and respond to questions that use variations of the same term or region-specific slang, providing more personalized and accurate support.

2. Word Embeddings

Word embeddings represent individual words in vectors and capture relationships between words efficiently. For example, the words “doctor” and “physician” might have similar vectors since they appear in related contexts.

Example in CRM: In a CRM like Daialog, word embeddings enable the system to understand related words and offer smart suggestions to users. If a sales rep searches for clients interested in “winter products,” the system might also suggest similar terms like “warm clothing” or “cold-weather footwear,” facilitating a broader and more relevant search.

3. Sentence Embeddings

Sentence embeddings represent entire sentences in a single vector, capturing the full meaning and context of the phrase. This enables AI to analyze whole sentences and understand their intent, essential in applications where the context of a sentence matters as much as individual words.

Practical Business Case: An internal search system using sentence embeddings could help employees find relevant information within company documentation. If an employee searches for “how to improve customer retention,” the system can find documents with similar sentences, like “strategies for customer loyalty,” improving the efficiency of internal resource searches.

4. Document Embeddings

Document embeddings represent entire documents (web pages, reports, or long emails) in a single vector, capturing the general meaning of the content. This is ideal for analyzing large volumes of text and extracting key themes.

Example in CRM: A CRM like Daialog could use document embeddings to analyze a series of customer emails and automatically extract common concerns, helping sales reps prepare more effective responses. For instance, if a customer sends multiple emails asking about “return policies” and “warranties,” the system can alert an agent to these topics, supporting a personalized response.

How Can Embeddings Optimize Processes?

Embeddings not only help in understanding language but also improve efficiency across various internal processes, saving companies time and resources. Here are some practical ways embeddings can be applied in business:

1. Enhanced Semantic Search: Unlike traditional search, which relies on exact keywords, semantic search lets users find information based on meaning and context. For example, a sales team looking up “partner pricing policy” might find documents related to “discounts for loyal clients” or “special pricing for affiliates.”

2. Sentiment Analysis and Feedback Monitoring: Embeddings can analyze the tone of customer conversations and feedback, helping businesses identify problems and opportunities for improvement. This is especially useful in customer service, where agents can receive alerts about potentially dissatisfied clients and act promptly to improve the relationship.

3. Automated Customer Support Responses: By analyzing frequently asked questions, an AI system based on embeddings can provide automated answers to common inquiries. This allows the customer service team to focus on complex issues, while AI handles the more frequent questions.

Workflow Example Using Embeddings

Suppose you have a sales team that receives numerous customer emails with questions about various products. Using embeddings, the CRM can automatically organize these emails by topic, identifying common questions like “international shipping” or “discount prices.” This allows the sales team to quickly filter through emails and provide faster, more accurate responses, optimizing the workflow.

Wrapping Up: Unleashing the Power of Embeddings

Embeddings are a powerful tool for understanding and processing natural language in AI applications. In a conversational CRM like Daialog, embeddings allow the system to better understand user needs, improve search precision, and enhance the customer experience. Tasks like semantic search, automated response, and sentiment analysis become more effective, contributing to greater operational efficiency and high-quality customer service.