AI Assistant: Multi-Channel Experience | Pragmatica AG Prozessoptimierung & Digitalisierung

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Customized AI Assistants - Cross-Channel, Safe and Efficient

Interaction with customers via various channels, be it via voicebot, chat, email or the web, is developing rapidly. AI-supported assistants are opening up new ways to improve the customer experience, reduce costs and speed up processes. The seamless and regulation-compliant integration of these solutions into existing processes, systems and touchpoints, from conception to operation, is crucial for success.

With Pragmatica’s Multi Channel Experience for AI-powered assistants, we offer a structured approach that guides you from ideation to implementation and continuous optimization – tailor-made for banks, insurance companies and regulated industries.

Our Services at a Glance

Holistic project approach along the LLM life cycle

Needs Analysis & Target Definition

Technical Analysis & Data Protection

Partner Selection & RfP

Modeling & Integration

Testing & Go-Live

Operation & Continuous Optimization

AI Governance Framework

Analysis of data flows and risks associated with cloud use and FINMA-compliant protection
Protection of generative AI systems against threats such as prompt injection or model leakage
Establishment of guidelines, roles and controls for the LLM lifecycle in accordance with the EU AI Act, ISO/IEC 42001 and NIST AI RMF.

Your Advantages With Us

Experience in the implementation of AI projects for banking clients.
Focus on regulated industries: Over 20 years of project experience in banks and insurance companies – with a deep understanding of systems, data flows and regulation.
Structured technology and partner process: in-house developed LLM vendor map & RfP templates for informed decision-making.
From use case to rollout: we take on end-to-end responsibility or supplement your teams on a selective basis – flexibly, pragmatically, solution-oriented.

Introductory formats - free of charge & individual

With over 20 years of experience in project management, we successfully support Swiss financial institutions in the implementation of LLM use cases – from the design of the use case through to full implementation.

We offer comprehensive expertise in AI governance and AI risk management and are familiar with the systems used (e.g. core banking, telephony) and their interactions.

Thanks to our own LLM map with an overview of providers and implementation partners, we make it easier to select the right technology and partners. By working with leading LLM experts, we ensure a customized and smooth implementation.

See for yourself with our free, customized introductory formats and take the first step towards AI & LLM with us.

Use Case
Exploration

2-4 hours | Remote or on-site Introduction to LLMs, individual use cases, feasibility assessment

Request now free of charge

Workshop

System Landscape & Technology

2-4 hours | Remote or on-site
Technology options in the market & fit to existing architecture

Request now free of charge

workshop

AI Governance & Compliance

1-2 hours | Remote or on-site
Regulatory requirements for AI use (FINMA, DPA, EU AI Regulation)

Request now free of charge

awareness

SELECTED REFERENCES

Success Stories

Optimizing customer interaction through the use of innovative technologies at a Swiss retail bank

Initial situation

The Swiss bank was faced with the challenge of reducing the volume of incoming customer calls, which had increased significantly due to extensive changes in e-banking. As part of the measures to reduce call volume, the goal was to expand self-service solutions and optimally guide customers at the digital touch points along the customer journey.

At the same time, the Swiss bank was faced with an increase in written customer inquiries, which made an efficient processing solution essential. As a strategic response to this, the goal was defined to develop an advanced AI-supported system to increase the efficiency in processing these requests.

Our contribution

Reduced call volume: Implementing new and improving existing self-service solutions contributed significantly to achieving the goal of reducing customer call volume
More efficient processing of written customer inquiries: The development and introduction of an AI-supported tool enables the bank’s employees to process written customer inquiries faster and with high quality
Increased customer satisfaction: Customers benefit from faster and more efficient digital self-service solutions, resulting in an increase in overall customer satisfaction

Client benefit

Support for strategic projects from concept development to technical implementation and rollout
Requirements analysis, process design, solution design and project planning of service solutions and AI applications
Examination of existing digital self-service solutions in comparison to the market offerings, combined with the creation of a comprehensive report that includes targeted recommendations for action
Reduced call volume: Implementing new and improving existing self-service solutions contributed significantly to achieving the goal of reducing customer call volume
More efficient processing of written customer inquiries: The development and introduction of an AI-supported tool enables the bank’s employees to process written customer inquiries faster and with high quality
Increased customer satisfaction: Customers benefit from faster and more efficient digital self-service solutions, resulting in an increase in overall customer satisfaction

AI architecture review and development of suggestions for improvement

Initial situation

The enterprise architecture of a medium-sized bank wants an external view of the current AI solution design and a clear approach to implement AI solutions and receive recommendations for future design of the same. This is important because the bank does not have any AI experts internally, the use of the technology is new to the bank (apart from purchased black box solutions) and business demand for AI solutions is likely to increase significantly.

The aim is to support the enterprise architecture – and in particular the text assistant usecase – in the following areas:

Architecture and solution finding
Competencies
Method
documentation

Our contribution

Clear status analysis that clearly shows the current problems but also opportunities in implementing the solution
Suggested solutions to increase internal competencies and improve the resilience of data-driven requirements
Unmistakable description of why enterprise architecture should be brought more into play in order to avoid isolated solutions
Showing the current problems and opportunities in the collaboration of different teams

Client benefit

As part of the mandate, recommendations and appraisals were made on company-wide AI efforts in corporate architecture, cloud and compliance, as well as on the implementation, procedure and architecture of a specific use case.
Interview with various parties involved in the topic of AI, summary of the conversations and recommendations for organizational improvements.
Comprehensive presentation that examines both the technical aspects of the LLM-RAG approach and the costs of the various on-premise and public cloud solutions in more detail.
Moderation of workshops between Value Stream Lead, external solution provider and the internal development team with the aim of suggesting improvements for the further course of the project.