We automate recurring work in your business: pre-sorting inquiries, reading documents, making knowledge findable, connecting internal workflows. What it delivers, we measure up front.
Send request →Technology is rarely the problem. AI initiatives stall on unclear use cases, on missing delivery capacity, on tools no one maintains and on data-protection questions. We name these four bottlenecks before the first test and resolve them concretely.
Without a clear bottleneck and a target to hit, you can't tell afterwards whether it paid off. We pick the process with the biggest lever and define up front how success is measured: handling time, error rate, volume.
The biggest blocker is usually free time in the team. We deliver the first productive use case and transfer the know-how to your people, so you stay capable without having to hire.
AI without a link to your systems stays a gimmick, and after launch the team is often left alone with a solution no one can see into. We embed it into your existing programs, hand it over clearly documented with an operating guide and run it on request.
When customer or HR data must not leave the building, many AI tools fail right here. We build the setup on your infrastructure or with a provider your data protection team approves, and we define up front which data the model gets to see at all.
Without a clear bottleneck and a target to hit, you can't tell afterwards whether it paid off. We pick the process with the biggest lever and define up front how success is measured: handling time, error rate, volume.
The biggest blocker is usually free time in the team. We deliver the first productive use case and transfer the know-how to your people, so you stay capable without having to hire.
AI without a link to your systems stays a gimmick, and after launch the team is often left alone with a solution no one can see into. We embed it into your existing programs, hand it over clearly documented with an operating guide and run it on request.
When customer or HR data must not leave the building, many AI tools fail right here. We build the setup on your infrastructure or with a provider your data protection team approves, and we define up front which data the model gets to see at all.
Three workflows that often still run by hand. And what they look like automated, with a person at the point where a decision is needed.
In the no-obligation intake we check whether a lever like this sits in your processes. Five questions are enough to start.
Where does AI pay off in your business?
Five questions, about two minutes, right here. Vega captures the process that costs you the most time and prepares your AI-Audit call.
Vega · intake assistant · 5 questions
First check where AI pays off. Then build what holds. Three building blocks to start concretely with AI & Automation. Each with benefit, outcome and effort on your side.
You see AI potential in your business but don't yet know which first step pays off most.
Deliverables
Deliverables
You have a concrete use case in mind and need proof that it works.
Telephony, support, qualification or appointment workflows should be automated by voice.
Deliverables
Deliverables
A validated workflow, agent or automation use case should go live.
Deliverables
Deliverables
Three steps: Identify, Build, Embed. Each has a defined outcome and a documented transition. The approach is traceable and verifiable.
We find the process with the biggest lever and measure where you stand today: handling time, error rate, volume. So before we build anything, it's clear how success will be measured.
We build the first productive use case, measure it against the starting value and wire it into your programs, technically via n8n, interfaces or direct SAP connectivity.
We hand over with audit-grade documentation, including a runbook and knowledge transfer to your team. On request, operation runs on your infrastructure or with a provider of your choice.
We find the process with the biggest lever and measure where you stand today: handling time, error rate, volume. So before we build anything, it's clear how success will be measured.
We build the first productive use case, measure it against the starting value and wire it into your programs, technically via n8n, interfaces or direct SAP connectivity.
We hand over with audit-grade documentation, including a runbook and knowledge transfer to your team. On request, operation runs on your infrastructure or with a provider of your choice.
We find the process with the biggest lever and measure where you stand today: handling time, error rate, volume. So before we build anything, it's clear how success will be measured.
We build the first productive use case, measure it against the starting value and wire it into your programs, technically via n8n, interfaces or direct SAP connectivity.
We hand over with audit-grade documentation, including a runbook and knowledge transfer to your team. On request, operation runs on your infrastructure or with a provider of your choice.
In recurring manual work that ties up capacity today: lead qualification, email triage, invoice intake, or your internal knowledge helpdesk. We start with the one bottleneck where automation pays off most and deliver a productive solution. Our aim is a measurable effect in the first quarter.
Staff time that previously went into routine work becomes available again, for customers, sales, or strategy. The aim is shorter response times and fewer errors. You measure the effect against your baseline.
We settle that together before we build: which data the model sees, where it runs (on your infrastructure or with a provider your data protection team approves) and who has access. Internally we run our AI on our own hardware and know every component. We document the processing path so your data protection team can review it.
No, your call. On request we take over ongoing operation and maintenance, including monitoring, updates and adjustments to new requirements. If you prefer to take it forward yourself, we hand it over with coaching and full documentation. Your team's workload stays as large as you want it.
Yes, and deliberately no junior layer in between that you'd pay for. We start with the one use case with the biggest payoff and deliver it productively; we expand scope through clearly scoped, signed-off steps rather than headcount. You keep the same contacts throughout: the same people who build and run the solution. Both founders know every setup, and runbook and access details sit with you, so operations stay on track even if one of us is unavailable.
Before each use case we define baseline and target value together: e.g. processing time per case, ticket volume, or lead time. After go-live you see the change in the dashboard. If it doesn't deliver as expected, we adjust.
You get the reliable timeline after Discovery, not in the pitch. It depends on scope and data situation.
Every company carries different bottlenecks: manual routines, risky data, processes no one questions anymore. In a free 30-minute first call with the founders, we pinpoint the biggest levers together and tell you where the effort pays off.
