Building AI products
An app or SaaS with AI at its core, planned to pay off.
AI chatbots · AI agents · Automation · Prototypes
AI development for businesses
As an AI agency, we use AI to build practical solutions that take real work off your business. From prototype to production.
5.0 from 96 reviews100+ products since 2017
The AI solutions we build for businesses
AI integration into existing software
New features where your data and users already are.
AI agents and automation
AI agents sort, check and write drafts. Your team decides.
AI chatbots and assistants
Answers from your data (RAG), with a source and a handover to people.
Vibe coding and AI prototypes
Clickable within a few days, then built properly.
-50 %Time to market
AI software development: how we build your software with AI
AI takes on the routine work in every step, people make the decisions.
- 01
Planning
AIchecks requirements for gaps and contradictions
HumanYou and our team set the scope
- 02
Design
AIcreates variants and clickable drafts
HumanOur designers choose and refine
- 03
Code
AIco-writes the code, including vibe coding
HumanDevelopers review every line
- 04
Tests
AIwrites test cases that run automatically
HumanThe team assesses every result
- 05
Reviews
AIchecks every change in advance
HumanA developer approves
Case studies: AI projects with real results
Real reviews from our clients and what we built with AI in their projects.
Checkwerk
5.0Built with AIAI-supported paper-to-digital checklist flow
A team that listens, understands, and delivers.
Working with Vitec felt like true partnership. They took time to understand our concept and shaped it into a polished web experience. Responses were quick and professional, and solutions were prioritized thoughtfully. The end result exceeded what we imagined when we started.
Sven BeuckCEO, SaHeGa Service GmbHView case study
weguide
5.0Developed with AITime to market cut by 50 % through AI-supported development
Their workflow is transparent
We are working with Vitec to develop our mobile app for our start-up. What began as an initial project has turned into a long-term partnership that helps our start-up grow every day.
Meal Planner
5.0Built with AIConfigurator-driven recipe logic: weekly plans by family, preferences and allergies
A supportive partner for first-time founders.
As a first-time founder, I needed guidance as much as execution. The collaboration helped me visualize, refine, and finally build a highly personalized meal-planning app from scratch. Changes were handled flexibly, communication stayed open, and deadlines were respected.
RMN Events
4.8Built with AIAn assistant that answers from the platform data and hands over to the team in the chat
Vitec GmbH's development process has been seamless.
Vitec delivered a scalable web and mobile platform that replaced multiple legacy systems and streamlined communication across our entire operation. Their structured approach, speed of execution, and reliability allowed us to move from idea to market without costly rework.
In 4 steps to AI in production
Understand
Which step should become easier?
Prototype
With your real data.
Integration
With tests and a handover to people.
Operation
Measure, learn, improve.
How we make AI reliable
AI can be wrong, in a chat as much as in the background. That is why, before we start, we define which data it works with, how we test it and where a person takes over.
Results from your data
Whether an answer, a classification or an analysis: the AI works with your data, and every result can be traced.
Tested before every release
Fixed test cases with a known result run with every change. That way we see whether the AI gets worse before anything goes live.
A person decides where it matters
If the AI is unsure or a lot is at stake, nothing moves on automatically: your team takes over or approves.
Your data, your rules
We choose model, hosting and data flow to match your data protection requirements. Which EU AI Act duties apply, we settle before we start.
Not every problem needs AI
AI in the product pays off where software has to weigh things up. Everything else we build the classic way, developed with AI and therefore faster.
AI in the product
When software has to weigh things up
Classic, developed with AI
When the rules are clear
Decision
AI in the productWeighs up criteria instead of fixed yes/no rules
ClassicFixed if-then rules, always the same result
Task
AI in the productTakes on decisions a person would otherwise make
ClassicData is structured, the steps are clear
Speed
AI in the productMany times faster than by hand
ClassicFinished faster through vibe coding and AI in the development process
Operation
AI in the productCosts per request, a person checks where it matters
ClassicNo ongoing AI costs
What we build AI solutions with
We choose the AI model per task. We build the AI features into the software where your data and users already are, and connect them to your knowledge and your systems through RAG and MCP.
AI models
- Claude
- Mistral AI
We choose the model per task: Claude for assistants and agents, Mistral when your data should not leave Europe.
RAG and databases
- RAG (Retrieval-Augmented Generation)
The AI answers with your company knowledge: documents and data from your databases become searchable, and the answer names its source.
MCP for your systems
- MCP (Model Context Protocol)
We build MCP servers for ERP, CRM or database, so AI assistants and agents work with your systems under clear permissions.
Automation
- n8n
Workflows between form, CRM and email, without custom code for every connection. Core logic under heavy load gets its own interface.
Software around the AI
- Python
- Node.js
- Angular
- Flutter
Python for data and models, Node.js for interfaces, Angular and Flutter for the web app and app your team uses with the AI.
Our AI tools
- Claude Code
- Cursor
They make us faster: routine code, tests and refactoring. A developer reviews every change.
Frequently asked questions about AI development
We use AI to build solutions that do real work in your business: AI chatbots, AI agents, automations and AI in your existing software. From the strategy call to production, everything comes from one team.
It depends on the data, the integration and the number of requests. In the free strategy call we clarify the scope, then you get an estimate including the running cost per request.
The model that fits your task: Claude, Mistral or open-source models. We choose by quality, cost per request and data protection, not by vendor.
Yes, that is often the fastest route, because the data and the users are already there. When your data changes, the AI follows automatically.
An AI chatbot answers questions, for example from your documents. An AI agent also carries out tasks in your systems, such as sorting requests or creating drafts, and where it matters, a person approves.
Workflows in which people read, weigh up and pass on information: sorting requests, checking documents, writing drafts. Where the rules are clear-cut, classic automation without running AI costs is enough.
You describe in plain language what an application should do, and an AI writes the code: ideal for a quick prototype. For production we then continue properly, with architecture, tests and security.
In planning, design, code, tests and reviews, AI takes on the routine work and people make the decisions. This cuts the time to market by up to 50 % (measured at weguide).
It only works with your data. When it is unsure, a person takes over, in a chat as much as in the middle of a workflow. Automated test runs catch errors before they repeat.
We choose model, hosting and data flow to match your data protection requirements; parts such as the document search can run locally. Which data the AI may see, we agree together beforehand.
For most applications mainly transparency: users must be able to tell that they are talking to an AI. High-risk areas such as recruitment we plan with the stricter duties from the start.
Where decisions by criteria tie up a lot of time from experienced people, usually yes. Where the rules are clear-cut, classic software developed with AI is often cheaper; we tell you that up front.
Yes. In the free strategy call we clarify where AI takes work off you and where classic software is enough; for larger projects there is our product strategy with a discovery phase.
For a quick test, a freelancer can be enough. If the solution is meant to go into production, with tests, data protection, maintenance and cover within the team, a partner with its own team is the safer choice, and we discuss that honestly in the strategy call.
By real case studies with reviews, by a prototype with your data before the big contract, and by someone who also tells you where AI is not worth it. That is exactly how we work: prototype first, then production, and classic software where it fits better.
Where can AI take work off you?
In the free strategy call we tell you honestly whether AI in the product is the right solution or whether classic software, developed with AI, is the better fit.
Free strategy call
Marc Roset
CEO
