Content Tagging for CRM Free-Text Data

Management Summary

HMS developed an automated content tagging solution for unstructured CRM free-text data for a company in the pharmaceutical healthcare sector. The LLM-based solution classifies the content according to predefined categories and supplements the model’s logic with rules for classes that are difficult to distinguish. The structured tags support search, grouping, and downstream analyses.

Project summary

#lifesciences

Unternehmen aus dem pharmazeutischen Healthcare-Sektor

Branche: Pharma und Healthcare
Projektstart: 09/2023
Schwerpunkt: Generative & Agentic AI
Use Case: Document & Content Intelligence

Projektziel

CRM-Freitexte klassifizieren und als strukturierte Daten für Analysen bereitstellen

Wichtigste Kernfunktionen

LLM-basierte Klassifikation entlang vordefinierter Inhaltsklassen
Ergänzende regelbasierte Logik für fachlich ähnliche Kategorien
Produktive Integration und Verarbeitung in Palantir Foundry

Tech Stack

python langchain palantir foundry gpt models

The combination of LLM control and rule-based logic was crucial for reliably distinguishing between content classes that are similar in subject matter.
Christoph Bergen
CoE Lead for GenAI at HMS

The Starting Point

The client’s CRM system contained large amounts of free-text feedback from customers and sales representatives. The content included information relevant to technical analyses and strategic evaluations, but it was not structured in a uniform manner.

The volume and heterogeneity of the texts made systematic evaluation difficult. In addition, there were categories with similar content that could not be clearly distinguished from one another based solely on simple rules. As a result, the information could only be incorporated to a limited extent into downstream analyses and decision-making processes.

Fünf HMS-Mitarbeitende aus dem Content-Tagging-Projekt im Austausch an einem Besprechungstisch.

The HMS Solution

Automated Content Tagging with LLMs

HMS developed a system that automatically processes unstructured CRM text and assigns it to predefined content classes.

Multidimensional Classification

Large language models classify texts along several domain-specific dimensions. The classification results are used to generate structured tags for further processing.

Rule-Based Control of the LLM

HMS supplemented the model-based classification with rule-based logic. This logic supports classification when dealing with heterogeneous documents and classes that are difficult to distinguish based on content.

Implementation with Azure OpenAI and LangChain

The application was developed using Azure OpenAI, GPT Models, LangChain, and Python. HMS optimized tagging performance and derived technical best practices from the implementation.

Productive Integration into Palantir Foundry

HMS integrated the system into Palantir Foundry and designed the processing for scalability. In a project comparison, the solution outperformed previously used approaches based on NLP Transformer models in terms of tagging performance.

Customer Benefits

Making CRM Free-Text Data Analytically Usable Through Content Tagging

Automated content tagging unlocks information that was previously difficult to analyze for further data use.

Structured

Making Free-Form Text Useful

Free-form text is assigned to predefined content classes and is then available as structured data.

Searchable

Find Specific Content

The tags generated make it easier to search, filter, and group CRM content by topic.

Multidimensional

Classify Content in a Differentiated Manner

Texts can be classified according to several disciplinary dimensions and used for subsequent analyses.

Well-founded

Preparing Decisions

Information from customer and sales feedback can be incorporated more systematically into evaluations and strategic decisions.

The solution thus supports both exploratory data queries and in-depth analyses of key metrics from management reports.

Our Strengths, Your Benefits

Combining LLMs and rule-based classification

HMS was responsible for the development and deployment of the content-tagging system. In this project, we combined LLM engineering with rule-based logic, performance optimization, and integration with Palantir Foundry.

What You Can Expect from HMS

In automated content tagging, model control, classification logic, and data processing must function as a cohesive system. To achieve this, HMS combines large language models with domain-specific rules and traditional software engineering.

Development of the content tagging system through to production deployment
Rule-based control for heterogeneous documents and similar content classes
Performance optimization and design for scalable processing

Christoph Bergen

CoE Lead GenAI

Sie möchten ein ähnliches Projekt umsetzen?

Sprechen Sie mit HMS darüber, wie sich Ihre Daten-, KI- oder Softwarelösung fachlich einordnen, technisch umsetzen und in Ihre bestehende Systemlandschaft integrieren lässt.

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Next Steps and Further Study

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