Exxeta Schweiz

Exxeta Switzerland combines 15 years of industry experience with AI expertise, from strategy to production. For banks, insurers, and asset managers looking to use AI responsibly and effectively.

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Our Services

AI projects rarely fail because of the technology. They fail because use cases are not prioritized, data is unavailable, or regulatory requirements are underestimated. We help you avoid exactly that.

AI-Strategy & Use Cases

Without a clear target vision, AI remains a collection of pilot projects. We first define where AI should be used in the future, which technology and operating model best support it, and who is responsible for what within the organization. We then assess existing use cases based on business value, data availability, and regulatory effort. Anything that does not align with the target vision is not prioritized.
The result is a clear AI target vision with defined areas of action, an established technology and operating model, and clear responsibilities. This is complemented by a prioritized use case portfolio identifying the three initiatives to launch first, as well as a measurable target and baseline for each area of action.
Data & Data Platform

AI requires data that is available, reliable, and accessible. In many financial institutions, this cannot be taken for granted. Data is locked in silos, quality remains unverified, and access is restricted by regulatory requirements. We create the data foundation that AI initiatives need to succeed, from data platform architecture and data strategy to governance that ensures models are built on reliable foundations.
GenAI & Agentic AI

Traditional automation is designed for standardized, high volume processes. A significant share of day to day work falls outside this scope. For the first time, AI agents can operate beyond this boundary because they do not require a predefined workflow. Instead, they need a goal, access to data, and a way to verify their actions. We build solutions that perform reliably in production environments, not just in a proof of concept.
The result is an AI agent running in production, connected to the relevant source systems, with a defined level of autonomy and a documented audit trail, ready for handover to operations.
AI Powered IT Modernization

Legacy systems are the norm in financial services. AI accelerates the process of understanding and transforming these systems in a structured way, with traceable results and less risk than traditional migration projects.
The result is a documented map of the legacy system, including dependencies and business logic, as well as a migrated reference component with generated tests that provides a reliable basis for estimating the overall effort.
Compliance & AI Governance

The revised Swiss Data Protection Act (revDSG), the EU AI Act, and FINMA requirements raise specific questions for financial institutions: What needs to be documented before a model goes into production? Who is responsible? We help structure AI initiatives so they meet regulatory requirements. This is not a barrier. It is the foundation for operating AI sustainably over the long term.
The result is an auditable governance model with risk classification for each use case, clearly defined responsibilities, and documentation that meets internal audit requirements.

15 Years of Financial Services Expertise in Switzerland

Exxeta Switzerland has been supporting banks, insurers, and asset managers with complex transformation programs for 15 years, from front to back programs and data migrations to the modernization of critical core systems.

What has changed during this time is that AI is no longer an add on. It is transforming how systems are understood, how decisions are prepared, and how code is transformed. We have evolved with this shift and bring both sides together: the industry expertise needed to assess initiatives from a business perspective and the technical depth required to implement them.

We can also draw on the expertise of more than 1,200 colleagues across Exxeta, with specialized teams for AI, cloud, data, and IT modernization.

Examples of How We Support Our Clients

BANKING

30% Efficiency Gain With a Proprietary AI Platform
- 30% Increase in Efficiency
- 50% Lower Token Consumption
- Full Vendor Independence
There was no standardized AI platform. AI tools were used through individual providers and employee subscriptions.
There was no overview of actual usage and no control over which data was shared with external models.
There was no reliable foundation for systematically automating use cases and measuring efficiency gains.
The goal was deliberately not to rely on a solution from an established provider, but to build a proprietary platform with full flexibility and control.
Designed and built a proprietary agent harness as the platform foundation for company wide AI use.
Analyzed security, risk, and regulatory requirements and configured the platform accordingly.
Implemented and deployed the platform across the organization, including access and authorization management.
Continuously enhanced the platform based on systematically collected user feedback.
BANKING

Audit Ready AI Governance Through a Structured Risk Management Framework in Banking
- Complete AI System Inventory
- Measurable Risk Indicators
- Regulatory Readiness
The use of AI had grown across individual initiatives, but there was no central overview of which systems were running in production, who was responsible for them, or how critical they were.
Existing model risk and outsourcing policies were designed for traditional IT and did not cover AI specific risks such as bias, model drift, or unauthorized tools.
With regulatory deadlines approaching, the second line of defense needed to report on AI risks without defined indicators, thresholds, or clear responsibilities.
Conducted a structured assessment of the existing AI landscape across six risk domains, including a prioritized gap register and mapping to relevant regulatory requirements.
Established a central AI inventory with risk based classification and documented responsibilities across the three lines of defense.
Defined a control library with measurable risk indicators and escalation thresholds, embedded in a compliance approval step before deployment.
Designed governance and executive level reporting structures, including clearly defined escalation paths.
BANKING

Evidence Based Regulatory Assessments for AI Initiatives in the Financial Sector
- Source Based Knowledge Base
- Every Statement Linked to a Source
- Foundation for Further Compliance Applications
Assessing an AI initiative in the financial sector involves multiple regulatory frameworks at the same time: the EU AI Act, DORA, NIS2, and GDPR at EU level, FINMA circulars and the revised Swiss Data Protection Act (revDSG) in Switzerland, and MaRisk and BAIT in Germany. These frameworks overlap, differ in their level of legal force, and do not follow a common structure.
Determining which requirements applied in each case required manual research. This was time consuming, and the reasoning behind an assessment was difficult to reconstruct later.
A conventional AI assistant was not suitable for this purpose. Without strict source grounding, it can produce answers that sound plausible but cannot be substantiated.
Built a source based knowledge base covering the relevant frameworks in Switzerland, the EU, and Germany. Requirements were derived from primary sources, linked to controls, and classified according to whether they are legally binding, regulatory expectations, or voluntary standards.
Developed a regulatory assistance system that responds exclusively based on retrieved sources. Every statement references a requirement ID, an additional validation step verifies the citations, and unsupported content is flagged rather than presented as fact.
Implemented an automated regulatory radar to identify updates for the knowledge base, with human approval required for every proposed change.
Created audit ready deliverables, including registers, analyses, and management documentation, with a complete audit trail covering queries, tool calls, and costs.
BANKING

Measurable AI Adoption Beyond License Usage in the Financial Sector
- Measurable Adoption at Team Level
- Role Based AI Skills
- Shadow AI Made Visible
After the rollout of AI tools, adoption remained below expectations. Licenses had been assigned and pilots declared successful, yet a large share of employees continued working as before.
Usage remained superficial. Beyond basic applications, advanced capabilities such as agents, connectors, and automations were rarely used.
There was no reliable way to measure progress. Success was measured by license usage rather than changes in ways of working or actual value created.
At the same time, unapproved AI usage emerged in day to day work, without visibility into the underlying needs or how these use cases could be brought into an auditable environment.
Conducted an Adoption Readiness Assessment to establish a fact based baseline before rollout, covering AI skills, role specific capability gaps, cultural readiness, and areas of resistance.
Built role specific AI capabilities with a company wide foundation and advanced enablement where AI genuinely changes how people work, including agents, connectors, and automations.
Embedded AI adoption into leadership and culture through clear leadership signals, playbooks, and rituals that encourage experimentation and create space to discuss informed failure.
Established champion networks and continuous two way communication to surface concerns while they can still be addressed effectively.
Measured adoption, skills development, and value creation from day one, including visibility into shadow AI before it becomes a barrier.