AI & AUTOMATION
AI belongs in processes. Not in presentations.
We help companies use artificial intelligence in a sensible, secure and measurable way – from strategy through integration into existing systems to operations. For real efficiency, better decisions and new business models.
OUR FOCUS AREAS
Concrete Application Areas,
Real Value.
We identify, develop and integrate AI solutions wherever they deliver the greatest benefit – in your processes, systems and teams.
HOW AI FITS INTO YOUR SYSTEM LANDSCAPE
From Idea to
Integrated AI Solution.
We connect AI models, your business systems and your data through scalable integration and communication layers – secure, high-performing and future-proof.
Your Business Systems
CRM · ERP · ITSM · HR
Databases · Business Applications · …
Integration & Communication Layer
APIs · Middleware · Event Streaming
Data Platforms · iPaaS · …
AI Models & Agents
LLMs · Specialised Models · Agents
RAG · Vector Databases · …
GDPR · EU AI Act · Data Governance · Access Controls · Monitoring · Transparency · Human-in-the-Loop
Intelligent Process Automation
Automating processes, not just speeding up individual tasks.
Many companies already automate individual work steps today: documents are read out, tickets are classified, data is transferred or emails are answered automatically. The real economic leverage, however, only emerges once these individual solutions become end-to-end processes.
Modern AI can understand information, prepare decisions and respond to different situations. Classic automation then ensures that defined actions are carried out reliably. Together, this creates an architecture that combines structured rules with intelligent decisions.
The decisive point here is not to create as much autonomy as possible. What matters is determining, for each process, which steps can be executed deterministically, where AI makes sensible decisions, and where a human must remain involved.
VIA X therefore first analyses existing workflows end to end. We identify media discontinuities, manual routines, waiting times and recurring decisions, and use them to develop an automation architecture that integrates into the existing system landscape.
For example, incoming customer requests can be automatically understood, categorised and enriched with existing customer data. An AI agent can gather the necessary information and prepare a proposed solution. A workflow then takes over processing in CRM, ERP or ITSM. Only exceptions or higher-risk decisions are passed on to an employee.
The goal is not “AI at any cost”, but less manual routine work, shorter turnaround times and more reliable processes.
Enterprise Search & Knowledge Work
Finding, understanding and using enterprise knowledge in the right context.
Most companies don’t lack knowledge. The problem is that this knowledge is spread across numerous systems.
Information resides in SharePoint, Teams, file systems, CRM, ERP, ticketing systems, wikis, technical documentation and personal storage. As a result, employees spend a lot of time searching for information, assessing how current it is, or asking colleagues for knowledge that already exists.
Enterprise search combined with modern language models fundamentally changes this approach. Instead of merely finding documents based on individual search terms, employees can ask questions in natural language and receive context-aware answers drawn from existing company information.
VIA X develops Retrieval-Augmented Generation architectures – RAG for short – for this purpose. Company data is unlocked, structured, indexed and connected to a language model. However, the quality of the search is not the only thing that matters.
A production-grade enterprise AI solution must also understand which user may access which information. Document permissions, roles, tenants and access concepts must therefore be preserved throughout the entire processing chain.
Currency and traceability are also crucial. Answers should not be based solely on the model’s own knowledge, but on current company information, with sources disclosed transparently.
This gives rise, for example, to internal knowledge assistants, technical support systems, intelligent contract and policy search, or copilots for service and sales staff.
Especially as the use of AI agents increases, a clean, reusable and well-governed data foundation becomes a core prerequisite for scalable enterprise AI.
AI in CRM, ERP & ITSM
AI delivers value where your business processes already happen.
An additional chatbot can be helpful. But artificial intelligence only becomes truly relevant once it is connected to a company’s operational systems.
CRM holds customer relationships and sales processes. ERP manages orders, goods, finances and master data. ITSM maps services, incidents and technical workflows. This is exactly where large volumes of data and decisions are generated every day – and with them, some of the most interesting use cases for AI.
That’s why VIA X doesn’t integrate AI as an isolated application alongside the existing IT landscape. Using APIs, middleware, events and secure integration layers, we connect models and agents directly to CRM, ERP, ITSM and other business systems.
A sales employee, for example, can automatically receive an account briefing that combines information from CRM, emails, open opportunities and past projects. In service, AI can understand and categorise tickets and deliver suitable solution proposals from the knowledge base. In ERP, documents can be checked, discrepancies detected or master data processes supported.
A clear distinction between reading, suggesting and acting is essential. Not every AI needs to independently modify postings or trigger business transactions. Depending on the level of risk, agents can simply provide information, prepare actions for approval, or carry them out themselves within clearly defined limits.
Technically, this means authentication, permissions, APIs, logging and approval logic are just as important as the model itself.
AI Agents & Copilots
From intelligent assistant to digital colleague for clearly defined tasks.
Copilots support people. Agents can additionally plan multiple steps independently, use tools and carry out tasks across different systems.
This fundamentally changes how AI is used.
A copilot can, for example, summarise a customer enquiry and suggest a reply. An agent can additionally research information in the CRM, check contract data, create a case in the ticketing system, prepare an appointment, and then inform the employee of the outcome.
The added value doesn’t come from a powerful language model alone. A production-grade agent needs clearly defined tasks, controlled tools, relevant company data and unambiguous decision boundaries.
That’s why VIA X develops specialised agents and copilots not as a universal “AI for everything”, but for specific roles and processes. These can be, for example, service, sales, research, document or operations agents.
Together with our clients, we determine the appropriate level of autonomy. Some agents provide information only. Others may prepare cases for processing. Still others can independently carry out actions within precisely defined rules.
In more complex scenarios, multiple agents can work together. One agent might research data, another evaluates it, while a third carries out the required action in a business system.
The more decision-making and operational authority an agent is given, the more important governance, identities, permissions and technical guardrails become.
Governance, Security & Compliance
Innovation needs speed. Enterprise AI also needs control.
The more deeply AI is embedded in operational processes, the more important the question becomes of what rules govern its use.
Which models may be used? Which company data may be made available to a model? Which employees may use which AI application? Which actions may an agent carry out independently? How can decisions be traced later on?
These questions cannot be left until AI is already in widespread use.
VIA X therefore treats governance as a technical and organisational part of every enterprise AI architecture. This includes identity and permission concepts as well as model and agent registers, logging, audit trails, data classification and human-in-the-loop procedures.
Regulatory requirements are also becoming more important. The EU AI Act follows a risk-based approach. Companies therefore need to know which AI systems they use, for what purpose they are used, and what regulatory requirements follow from that.
GDPR and information security remain just as relevant here. Especially with company data, it must be clear what information is passed to models, where it is processed, and whether it could, for example, be used to train a service.
Governance is not meant to prevent innovation. Good governance instead creates a defined framework within which teams can adopt AI faster and more safely.
That’s why VIA X supports companies in building guardrails that combine technical and organisational requirements: from approved models and data sources to roles, guardrails, approvals and monitoring.
Monitoring & Cost-Effectiveness
An AI solution only succeeds once its benefit becomes measurable.
A working prototype is not yet a successful business case.
In production, data, user behaviour, models and processes keep changing. Answer quality can decline, new failure patterns can emerge, and model costs can grow significantly as usage increases.
That’s why we look at AI systems not only from a technical perspective, but combine AI observability with business metrics.
At the technical level, we track response times, error rates, model and tool calls, or the quality of retrieval results, for example. With generative AI, this is complemented by evaluations that check whether answers are factually correct, relevant and sufficiently backed by existing data.
For agents, it also becomes relevant which actions were carried out, which tools were used, where an agent stopped, and when human intervention was required.
These technical metrics are then connected to the actual business process.
How much does average processing time decrease? How many cases can be completed automatically? How often do employees need to step in and correct something? What are the model costs per process? And above all: is the new solution actually more cost-effective or qualitatively better than the previous process?
This is exactly where experimental AI parts ways with production-grade enterprise AI.
Our goal is therefore not to roll out as many AI applications as possible. Together with our clients, we want to identify and scale those use cases where technology creates clear, traceable business value.
Your project with VIA X
Let's work together to find the right technology solution for your challenges. We look forward to talking with you.