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NPS Score

200+

Successful AI projects

75+

Scientific publications

Overview

Project scope

Perelyn partnered with Stiegeler to accelerate customer support and enhance customer satisfaction through an AI-powered chatbot. The chatbot will leverage a comprehensive technical knowledge base and live data to provide precise answers to internal support teams.

Key Objectives & Deliverables

1

Implement a customised chatbot for querying the company's knowledge resources.

2

Enable a faster and improved customer support by internal operators.

3

Adding an external-facing customer chatbot interface on the Stiegeler website.

Stiegeler is a Germany-based telecommunications provider delivering high-speed internet, telephone, and TV services via fiber optic and DSL. They prioritise reliable connectivity for both residential and business customers, with a focus on rural areas, ensuring consistent access to essential communication technologies. Learn more about Stiegeler here.

Telecom industry & AI

The projected value of AI in the telecommunications industry is expected to reach USD 58.74 billion by 2032. AI is transforming our client’s industry by optimising network performance, enhancing customer service with intelligent chatbots, and strengthening security through advanced fraud detection. It is also driving innovation in 5G, edge computing, and IoT, reshaping how companies deliver smarter connectivity, personalised services, and more efficient operations.

Stiegeler’s goal is to maintain high-quality customer support 24/7. The challenge lies in achieving this seamlessly, as their outsourced after-hours provider lacks the deep company and technical expertise required to match the high quality and efficiency of their in-house team. To maintain excellent service around the clock, Stiegeler’s customer support team needs easy access to comprehensive knowledge and resources, ensuring that customer inquiries are addressed promptly and accurately, regardless of who is handling the support.

“At Stiegeler, we believe in empowering our team with consistent and reliable information across the board. That’s why we entrusted Perelyn with the development of a chatbot that ensures all employees are on the same page, especially in delivering accurate and timely responses in customer support. We sincerely thank Perelyn for the excellent collaboration and the innovative solution they’ve provided."

— Felix Stiegeler, Managing Director Stiegeler

Our approach

Perelyn conceptualised and implemented a full-stack, AWS-based AI chatbot solution for Stiegeler. We incorporated features such as conversational multimodal RAG, token-by-token streaming from the latest generation of LLMs, a customer-friendly frontend, and an interface to internal company data. All of these elements were specifically tailored to meet Stiegeler’s unique requirements and demands for their use case.

Industry perspective

AI-powered chatbots provide telecommunications companies with 24/7 support, instant query resolution, and scalability, reducing operational costs while enhancing customer satisfaction. They deliver personalised, consistent service, offer multilingual capabilities, and generate valuable data insights, all while seamlessly integrating with human agents to handle more complex issues.

LLM chatbots can save businesses up to 30% of costs on customer support.
However, only 20% of businesses use it.

Project phases

Phase 1
Readiness & Prototyping

Defining Chatbot Capabilities: Customising document and API querying, usage profiles, and usage analysis

Developing a minimal prototype: Showcasing the capabilities of document and API querying​.

Implementing an employee-friendly internal chatbot frontend: Building a modern React-based web frontend.

Phase 2
Implementation & Deployment

Deploying to AWS: Hosting the full-stack solution on AWS​.

Tuning the response generation: Enhancing quality through improved document retrieval and prompt fine-tuning.

Internal productionalisation: Rolling out the solution across different departments within the company.

Phase 3
Improvement & Rollout

Continuous improvement: Leveraging human feedback on chatbot results and the overall solution to drive enhancements.

Going external: Refining chatbot responses and the frontend for deployment to external customers.

Phase 4
Handover

Comprehensive training: Conducting training sessions to ensure seamless adoption.

Knowledge transfer: Delivering detailed documentation and hands-on training for future maintenance and updates.

Long-term partnership: Equipping Stiegeler with the tools and guidance for ongoing improvements, while ensuring Perelyn’s expertise is always available.

Phase 1
Vorbereitung & Prototyping

Definition der Chatbot-Funktionen: Anpassung von Dokument- und API-Abfragen, Erstellung von Nutzungsprofilen und Analyse der Nutzungsdaten.

Entwicklung eines Minimalprototyps: Demonstration der Fähigkeiten zur Dokument- und API-Abfrage.

Implementierung eines benutzerfreundlichen internen Frontends: Aufbau eines modernen, React-basierten Web-Frontends.

Phase 2
Implementierung & Bereitstellung

Bereitstellung auf AWS: Hosting der Full-Stack-Lösung in der AWS-Cloud.

Optimierung der Antwortgenerierung: Verbesserung der Qualität durch präzisere Dokumentenabrufe und Feinabstimmung der Prompts.

Interne Produktionsfreigabe: Einführung der Lösung in verschiedenen Abteilungen des Unternehmens.

Phase 3
Verbesserung & Rollout

Kontinuierliche Verbesserung: Nutzung von Feedback der Mitarbeiter zu Chatbot-Ergebnissen und der Gesamtlösung, um Optimierungen voranzutreiben.

Externe Einführung: Verfeinerung der Chatbot-Antworten und des Frontends für den Einsatz bei externen Kunden.

Phase 4
Übergabe

Umfassende Schulung: Durchführung von Trainings, um eine nahtlose Einführung der Lösung zu gewährleisten.

Wissensweitergabe: Bereitstellung detaillierter Dokumentationen und praxisnaher Schulungen für die zukünftige Wartung und Weiterentwicklung.

Langfristige Partnerschaft: Ausstattung von Stiegeler mit den notwendigen Werkzeugen und Anleitungen für kontinuierliche Verbesserungen, bei gleichzeitiger Verfügbarkeit der Expertise von Perelyn.

Need your own chatbot?

Overview

The Stiegeler Chatbot was designed to assist in answering customer queries. Perelyn conceptualised and implemented a full-stack chatbot solution for Stiegeler within their AWS environment, with a special focus on customised document processing, storage and retrieval, as well as company API querying capabilities.

The customer asks what wifi routers Stiegeler has on offer that are compatible with fiber optics connections?

Hello! Stiegeler Internet Services offers a wide range of wifi routers. The following models are compatible with fiber optics connections and are available to buy or rent on the Stiegeler website:

Fritz!Box 7690
Fritz!Box 7530 AX

Let me know if you require anything further!

Business impact & benefits

The chatbot solution enables Stiegeler to enhance customer support across their telephone and email channels, as well as their website. The results are clear: reduced turnaround times for customer queries and a higher success rate, leading to increased customer satisfaction.

Technical architecture & integration

Perelyn integrated advanced LLMs with modern methods, including text search, conversational RAG, and a dynamic front end, to deliver a smooth, user-friendly chatbot experience. The system was seamlessly integrated into the client's existing cloud infrastructure, with special emphasis placed on data protection considerations.

We aimed to empower Stiegeler with a chatbot that transforms the customer experience through quick, reliable support, creating a solution that meets their needs today and scales for tomorrow.

Max Schattauer

More on the topic

Insights

Telecom sector: How AI is Changing the Game

Artificial intelligence optimises networks, improves customer service through personalised chatbots, and enables new data-driven business models. However, challenges such as data privacy must be addressed. AI offers great potential for the industry.

Key Tech

LLM Frameworks

AWS Bedrock, Amazon Comprehend, Google VertexAI, Azure Open AI, HuggingFace Transformers, LangChain, LangSmith, LLamaIndex

Graphdatabases

Anthropic, OpenAI, AWS Bedrock

Modern Frontend

React

Backend Services

Docker, REST APIs

Cloud

AWS, ECS, CloudFront, MongoDB

Programming languages

Python, TypeScript

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