Automating end to end means bringing several components together.
Agents, integration, language models, data, no single tool covers a whole process. On this page: which components we use and how we secure them.
Not every process needs AI.
Many workflows have run reliably on rule-based automation for years, and should stay that way. How much judgement is needed is decided by the process, not by the technology.
Clear rules, no exceptions
Structured data, unambiguous decision paths. A robot works through the steps, cheaply and reliably over years.
- Reconcile data between systems
- Generate and distribute reports
- Maintain master data
No LLM involved, so no token cost and a workflow that stays unchanged for years.
Rules plus reading documents
The workflow is clear, the incoming data is not. AI reads and structures it, then the rules decide as before.
- Extract and classify documents
- Turn free text into fields
- Match documents to cases
One LLM call per document, then deterministic code decides. An agent would solve the same task in a loop of many calls.
Understand context, decide
The case cannot be captured in rules. An agent weighs it up, draws on company knowledge and refers exceptions to a person.
- Assess deviations
- Research across several sources
- Choose the approach case by case
Many calls per case, in exchange for judgement where rules no longer reach.
In practice we combine all three levels inside one process: AI reads the document, rules check the standard case, and an agent steps in only where judgement is required.
The four building blocks, built out.
Deciding, knowing, connecting, validating. What we actually build each block with.
Agentic AI
Agents that plan, decide and act. Built on Azure AI Foundry, Claude or the UiPath Platform, depending on the use case.
RAG & data platform
Microsoft Fabric or Snowflake for structured data, a vector index and RAG for documents. Every answer with a source citation.
MCP · API · RPA
We always take the cleanest route available into the target system.
- 01MCP when the agent should query for itself
- 02API at high transaction volume
- 03RPA when no interface exists
Validation apps
Interfaces built with vibe coding that present the case and log the human decision.
We choose the language model per use case.
Gateway, data layer and orchestration stay the same. Only the model changes, depending on what the case demands.
Governance, integration, scale.
When data must stay in your own tenant and integration with M365 or Dynamics matters.
Reasoning depth, text quality.
When complex documents and multi-step decisions are involved, such as contracts or audit reports.
Full data sovereignty, no cloud requirement.
When no data may leave your own data centre, an open model, self-hosted.
Efficiency & token cost.
When volume and token cost matter and governance allows it, reviewed per use case.
One door to every system, instead of ten interfaces.
An agent needs data from several systems. Instead of wiring it to each one, it asks the gateway, which knows where the answer sits.
- knows every connected source
- checks who may see what
- returns the answer with its origin
Without a gateway every agent would have to be wired to every system separately. With it, each source is connected once and every new agent uses it straight away.
What this means for you: our Areas of Expertise →
The gateway is built for read queries. For high-volume transactional steps, e.g. posting an invoice, triggering a purchase order, the AI agent instead communicates directly via API with the ERP or business application, including write access and error handling.
One agent per task, not one for everything.
A single agent meant to do everything becomes unreliable and impossible to audit. We cut processes into clearly scoped tasks and give each its own agent. An orchestrator distributes the work and brings the results together.
Extracts data from a document or mail and checks it for completeness.
Matches against master data and rules and decides the routine case.
Writes to the target system, via API or RPA, and reports the result back.
When something goes wrong you can see which agent did it, instead of interrogating a black box.
A small, cheap model for extraction, a strong one for deciding. That cuts cost and runtime.
A new step means a new agent on the orchestrator, not a new version of everything.
Where your data stays.
The first question in every project. Here is the answer, before you have to ask.
Data residency
Everything runs in your Azure tenant or in Swiss data centres. We hold no customer data in systems of our own.
No model training
Your data trains no language model, neither ours nor a vendor's. Contractually assured.
Access & roles
Every agent runs on its own service account with minimal rights. No agent sees more than its process needs.
Traceability
Every decision is logged with timestamp, source and reasoning. Audit-ready from day one.
We walk through the setup with your IT and security teams before the project starts. For particularly sensitive data we run the model in your own data centre on request, with no cloud calls at all.
What happens when something goes wrong.
No process runs smoothly every time. What matters is what happens next.
The agent is unsure
Below a defined confidence threshold the agent does not decide on its own, it hands the case to a human along with its assessment.
A system is unreachable
The transaction stays in the queue and is retried automatically. Nothing is lost, nothing is posted twice.
Monitoring
Throughput, error rate and escalations are visible at any time. The system raises an alert before anyone has to report it.
Support
Defined response times and a named contact. Not a ticket system your case disappears into.
After an incident the process resumes where it stopped. You have one point of contact for the whole flow, even when the cause sits in a third-party system.
What we build on.
We work with established platforms rather than building from scratch where building from scratch is not needed.
Partner status and certifications exist where we deliver continuously. The platform choice follows the use case, not the partner agreement.
Deeper into the architecture?
Questions about our approach? Reach out, we'd love to talk.