What is AI Data Enrichment?

Core Concept

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Data Labeling: Textual data is rich with information and meaning. The function of enrichment is to extract this information as a label.

Each enrichment task (i.e., prompt) identifies a different aspect of meaning from the raw text of a conversation or document which is output as a label. We refer to these extraction tasks as "dimensions of analysis" or simply dimensions.

Video: Core - What is Data Enrichment [1:30]
09/2025

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Data Enrichment Explained

From Unstructured to Structured Data

We refer to the textual data in a conversation—the messages between a customer and a chatbot or agent—as unstructured data. Dimension Labs allows you to add structure by adding labels.

Example: Raw Conversation Data

The above table is a dataset with two columns: a unique session ID and conversation text. Within the textual data lies additional information. Using AI we can identify, extract, and add this information to the dataset as a new column. We call this process enrichment.

The Enrichment Process

Each conversation transcript is analyzed by a large language model given a specific analytical task (prompts).

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Example: Sentiment Analysis

• Task: Sentiment Analysis
• Prompt: "Identify the sentiment of this conversation."
• Output Dimension: sentiment_cluster: {positive, negative, neutral}

The AI applies labels according to the prompt instructions, which are then added as new columns in your dataset.

Result: Enriched Data with Sentiment

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Note: We prefer to use the term "data labeling" to describe the enrichment process. While the output is similar to coding text, text mining, or AI classification these terms offer a less precise description of the underlying technology.

Dynamic Enrichment

The enrichment process is more versatile and easier to customize than many existing approaches for classifying data (i.e., machine learning models). AI may run multiple out-of-the-box and/or custom analytical tasks concurrently.

Moreover, these tasks may include both predefined and open-ended or "dynamic" labeling. Here's an example of dynamic reason labeling:

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Example: Reason Analysis

• Task: Reason Analysis
• Prompt: "Detail the reason the customer engaged with support."
• Output Dimension: reason_session: {dynamic, based on conversation content}

Result: Adding Dynamic Label Enrichment

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Note: Unlike sentiment analysis with predefined categories, reason analysis generates dynamic labels based on conversation content. These organic labels can be consolidated into themes through data mapping.

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Why Generative AI Enrichment Works

Unstructured Data is Multi-Dimensional

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Theory:

Subjectivity in analyzing text is not a problem to solve but rather an insight into the nature of the data itself. A small amount of text can be dense with information, even if we don't recognize it immediately.

Any conversation or document has dimensionality. Depending on your vantage point (your company and your role in it) you may recognize different dimensions of meaning.

Consider the following examples:

The Problem: Too Messy

A single conversation is easy for a person to interpret. Thousands are not. As volume grows, it becomes:

• Challenging to detect consistent patterns across all conversations.
• Impossible to quantify why customers engage, what they feel, and how issues are resolved.

Legacy tools like boolean keyword searches or machine-learning models fall short in terms of both speed and accuracy. These tools require a massive effort to configure and rely on brittle rules that deliver low accuracy results.

In contrast, modern language models employ word embeddings to identify mathematical relationship between words and meaning based on context.

Too Many Cooks

Consider that keyword searches are incapable of accurately segmenting textual information because word usage and metaphor create endless possibilities for false positives or erroneous exclusions.

A simple search for the term "cook" will return:

• a title (seeking line cook)
• a verb (love to cook)
• a noun adjunct (cook station)
• a product (cook knife)
• slang (let him cook)
• an exclusion (culinary professionals only — no amateur cooks)

The Solution: Adding Structure

AI enrichment uses large language models (LLMs) to read each conversation the way a person would—then labels it to add structure.

Each analytical task (prompt) extracts one aspect of the text. These labels are added as new columns in your dataset (dimension). Identifying and extracting multiple dimensions of meaning from text is a powerful analytic approach that is highly customizable.

Dimension Examples:

The Benefit: Scaling Human Judgment

The enrichment process mirrors traditional human data coding, but at machine speed.

The result: human-level comprehension at enterprise scale with consistent labeling and faster turnaround.

The Innovation: Dynamic Labeling + Transformation

Discover what you didn't know.

Traditional systems can only apply predefined options. AI enrichment can generate new labels dynamically, based on what appears in the text. Use our data maps to aggregate these dynamic labels into themes.

For example: Instead of choosing from fixed categories, the AI may surface organic themes such as “bonus code confusion” or “crypto withdrawal delay.”

This enables genuine discovery—finding issues and trends you didn’t predefine.

Dynamic labeling and mapping is only possible through advanced generative transformer models (GPTs) that understand language contextually.

The Validation: Reliability and Precision

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LLMs identify patterns: Generative AI enrichment avoids issues of hallucination because it leverages the underlying pattern recognition and reasoning skills of LLMs with minimal generative output.

When used for enrichment LLMs deliver on:

• Consistency: No variation between human coders.
• Contextual Understanding: Captures nuance and meaning, not just keywords.
• Reliability: No hallucinations in recognition-based tasks.

Validation studies confirm that hallucination rates in enrichment tasks are extremely low.

Why AI Enrichment is More Reliable Than Generation

Recognition vs. Generation

• Generation Tasks (writing, creativity): Higher risk of hallucination
• Recognition Tasks (labeling, classification): Extremely low hallucination rates

Pattern-Based Analysis Large language models excel at identifying consistent patterns in text structure, making them highly reliable for enrichment tasks.

Contextual Understanding Unlike simple keyword matching, LLMs understand:

• Conversational flow and context
• Implicit meaning and sentiment
• Industry-specific terminology
• Customer intent and emotion

A Key Element of Multi-Channel Analysis

Data Enrichment is just one part of a process.

That process starts with combining heterogeneous sources of data into one universal schema for analysis. The Dimension Labs Conversational Schema (DLCS) combines different sources of messy, textual data into a consistent format designed for unstructured analysis.

This normalization and standardization is meant specifically for analyzing conversational data and harmonizing it with other sources of unstructured text, allowing for high-quality omni-channel data enrichment.

The result: Creating a "single source of truth" and the first step in creating data pipelines capable of drawing on the full universe of your customer interaction data. Enrichment is the 2nd step. The 3rd is data transformation (data mapping & knowledge graphs).

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In Summary

• Textual data holds valuable, nuanced meaning.
• AI enrichment extracts that meaning through labeled dimensions.
• Dynamic labeling reveals new, evolving patterns in customer feedback.
• LLM precision ensures accuracy and trustworthiness at scale.

Enrichment transforms unstructured conversations into structured intelligence—allowing you to see what customers mean, not just what they say.