Generative AI development · Munich

Generative AI development company in Munich

GDPR-first, fully documented and hosted in the EU, with a senior team working your morning hours.

Clarigo Infotech is a generative AI development company that builds GenAI products for businesses: copilots for your team, content, image and document generation, fine-tuned models and private LLMs that run on your own cloud. We connect models to your data with retrieval and guardrails, so outputs are grounded and safe to use. A focused GenAI product usually goes live in 25 to 30 days.

German companies most often hire us for enterprise software, manufacturing and Industry 4.0 solutions, and GDPR-compliant AI.

Share

10+

Years of delivery

600+

Projects delivered

20+

Countries served

25-30

Days to a live MVP

Get a free consultation

Tell us what you need. A senior engineer replies within one working day.

No obligation. We reply within one working day.

What matters to Munich teams

Privacy, quality and clear documentation

European teams tell us they want a partner who treats data protection and quality as the starting point, not an add-on. That is how we work.

01

GDPR by design

Data minimisation, consent, EU hosting and a signed data processing agreement before any personal data is touched.

02

Documentation you can audit

Written specifications, architecture notes, test reports and handover documents, so your team or auditors can follow every decision.

03

Accessible by default

Interfaces built to WCAG guidelines, helping you meet the European Accessibility Act for digital products and services.

04

Predictable contracts

Fixed scope and price, clear milestones and acceptance criteria, and full ownership of the code and IP.

What we do

What our generative AI development covers

Copilots and assistants

In-app assistants that draft, summarise, answer and take actions for your staff or customers, grounded in your own data.

Content generation

Product descriptions, marketing copy, reports and emails generated in your brand voice, with review steps before anything is published.

Image and media generation

Product imagery, design variations and visual assets generated from prompts or templates, built into your existing workflow.

Document generation

Proposals, contracts, summaries and structured reports drafted from your templates and records, ready for human sign-off.

Fine-tuning

Models fine-tuned on your examples when prompting and retrieval are not enough, with evaluation to prove the gain.

Private LLMs

Open-source models such as Llama, Mistral or Qwen hosted on your own cloud, so sensitive data never leaves your environment.

In depth

How we approach Generative AI development

Prompting, retrieval or fine-tuning

Most GenAI products work best with good prompts plus retrieval from your own documents. We only recommend fine-tuning when you have enough quality examples and a clear gap that retrieval cannot close, because it adds training and maintenance cost.

Choosing the right model

We compare GPT, Claude, Gemini and open-source models on your real tasks for quality, speed and cost before committing. The architecture keeps the model swappable, so you can switch providers as prices and capabilities change.

Guardrails and review

Generated output goes through checks for format, tone, facts against your sources and sensitive content. Where accuracy matters, we add a human approval step and keep a log of every generation.

Private by design

We use API providers configured not to train on your data, or host open-source models on your own cloud. For healthcare work, our HIPAA-trained team builds to HIPAA requirements.

Working with us from Munich

Built for Munich teams

Time-zone overlap

Central European and Indian working days overlap by three and a half to four and a half hours, so your team is online for stand-ups and calls every morning your time.

The GDPR and the German Federal Data Protection Act (BDSG)

We design to the GDPR and BDSG: data minimisation, clear consent, access controls, encryption, data hosted in Germany or the EU, and a data processing agreement (AVV) signed before work starts.

Billing

Invoices in euros (EUR) or US dollars. Typical senior rates are $25 to $49 (USD) per hour, or a fixed price for a defined scope.

Sectors · Munich

Where we help Munich businesses most

Munich's enterprise and industrial companies work with us on Industry 4.0 dashboards, enterprise software and AI, with detailed specifications and documentation.

Automotive

Carmakers and suppliers shift to software-defined vehicles. We start with one costly process and automate it end to end.

Industrial manufacturing

Mittelstand firms adopt Industry 4.0 and predictive maintenance. We build AI agents, assistants and automation around these workflows.

Mechanical engineering

Machine builders add IoT portals and digital services. AI here usually means faster reporting, fewer manual steps and better customer replies.

Chemicals and pharma

Process industries automate lab, quality and compliance workflows. We start with one costly process and automate it end to end.

Munich time · Berlin

Your working day with us

Our team's hours, in Munich time

05:30–15:00

Monday to Friday

Overlap with your working day

09:00–15:00

About 6 hours every day

Daily stand-up

09:30

Munich time, plus written updates every evening

Local setup

Local platforms and setup

Messaging channels

WhatsAppEmailLinkedIn

Languages

GermanEnglish

Local payments

PayPalSEPA Direct DebitKlarna

Data hosting

AWS Europe (Frankfurt), Google Cloud europe-west3 (Frankfurt) and europe-west10 (Berlin), in your own cloud account, so data stays where your rules require.

Process

How it works

Week 1: discovery and design

A free call to understand your goal, then a written scope, a fixed quote and a clickable design you can test.

Weeks 2-3: build and integrate

Senior engineers build in short sprints, connect your systems and demo working software every week.

Week 4: test and launch

We test with real data, set up monitoring and deploy to your own cloud account, then go live.

After launch: support and grow

The same team stays on for support, improvements and new features, with guaranteed response times.

FAQ

Generative AI development company: questions from Munich

Do you work with companies in Munich?+

Yes. Clarigo Infotech works with companies across Munich, including Munich, Augsburg, Ingolstadt, as part of clients in 20+ countries. German companies most often hire us for enterprise software, manufacturing and Industry 4.0 solutions, and GDPR-compliant AI. We work remotely from India with one in-house team.

What time-zone overlap do we get from Munich?+

Central European and Indian working days overlap by three and a half to four and a half hours, so your team is online for stand-ups and calls every morning your time.

How do you handle the GDPR and the German Federal Data Protection Act (BDSG)?+

We design to the GDPR and BDSG: data minimisation, clear consent, access controls, encryption, data hosted in Germany or the EU, and a data processing agreement (AVV) signed before work starts.

Which currency do you bill in, and what does it cost?+

We bill in euros (EUR) or US dollars. Senior engineers typically cost $25 to $49 (USD) per hour, and fixed-scope projects such as an MVP in 25 to 30 days are quoted after a free consultation.

What time are your meetings in Munich time?+

Our team works 05:30–15:00 Munich time, so we overlap with your day from 09:00–15:00. Daily stand-ups are usually at 09:30 your time.

Can you build in German and English?+

Yes. We build interfaces and content in German and English, and test them with native speakers before launch.

Where will our data be hosted?+

Usually in AWS Europe (Frankfurt), Google Cloud europe-west3 (Frankfurt) and europe-west10 (Berlin), or wherever your data rules require. Everything runs in your own cloud account, so you stay in control.

How long does it take to build a GenAI app?+

A focused generative AI product for one use case typically goes live in 25 to 30 days. Fine-tuning or private model hosting can add time, which we agree in the scope.

Do we need to fine-tune a model?+

Usually not at first. Prompting with retrieval from your documents covers most needs. We recommend fine-tuning only when testing shows a clear benefit.