Net17 Solutions for Munich
AI Development Company for Munich Teams
We build automation and software for Munich manufacturers, industrial technology firms, and SaaS teams who need systems that respect how engineering-led businesses actually run.
Why Munich businesses build software and automation
Munich is an engineering town before it is anything else. The region carries a deep base of manufacturing, automotive suppliers, industrial technology firms, and a Mittelstand that has quietly led its niches for generations. Add a strong research presence and a maturing software scene, and you get companies that respect technical work and expect a partner to speak the same language. Conversations here often start on the factory floor or in a product engineering meeting, not in marketing.
The problems tend to be operational and detailed. A supplier wants production data out of machines and into a system where planners can actually use it. A product team wants to turn years of field knowledge into software a customer can run. An industrial firm wants quality and maintenance records to stop living in separate spreadsheets that nobody trusts at the end of the month. These are not greenfield fantasies. They are real processes that have grown complicated and now need structure.
There is a cultural expectation of rigor that we genuinely appreciate. Munich teams ask hard questions about reliability, data accuracy, and what happens when something fails. They are right to. Software that sits next to physical production or that customers depend on cannot be casual about edge cases. We would rather spend an extra week in discovery getting the model of the process right than ship something that looks good in a demo and breaks during a real shift.
Integration with existing machinery and enterprise systems is almost always in scope. Plenty of value here is hidden inside connecting an ERP, a set of machines, and the people who plan around them. The companies we work with are not chasing novelty. They want workflow automation in Munich operations that removes the dull, error-prone steps and gives their engineers and planners better information to act on.
What we build for Munich teams
Four areas of work, shaped by an engineering-led market. Most projects combine product work with the integration and automation that make it useful day to day.
AI Automation
Removing the repetitive, error-prone steps in operational and production-adjacent workflows.
Workflow automation
We model planning, approval, and quality workflows so handoffs happen automatically and the status of any job is visible without a phone call.
Internal process automation
Routine data entry, report generation, and cross-system updates run on their own, with people pulled in only when something genuinely needs a decision.
AI copilots
Assistants trained on your technical documentation and procedures, helping staff find the right answer in manuals and records instead of asking a colleague.
CRM automation
Sales and service data stays consistent across tools, and follow-up tasks trigger from real activity rather than someone remembering to log them.
Data automation
Pipelines that gather machine, ERP, and quality data into one reliable place, so planners and engineers work from numbers they can trust.
SaaS Development
Turning hard-won domain knowledge into a product. SaaS development in Munich often means productizing something a firm has done internally for years.
MVP development
A tight first release that puts the core idea in front of real users quickly, so you validate demand before committing to a full build.
SaaS architecture
Multi-tenant foundations with sane access control and a data model that can carry industrial complexity without collapsing under it.
Product engineering
Delivery across the stack with the testing discipline an engineering audience expects, because your customers will notice if it is sloppy.
Scaling SaaS platforms
As adoption grows, we handle performance and reliability so the platform stays steady when more sites and users come on board.
Custom Software Development
Systems built around your exact process when standard tools cannot encode the way your operation runs.
Internal tools
Purpose-built tools for planning, quality, and maintenance that replace spreadsheets and enforce the rules your process depends on.
Web applications
Applications for operations, partners, or customers, built to handle real data volumes and the reliability your work demands.
Dashboards
Production and operational dashboards fed from live sources, giving planners and managers a current picture instead of yesterday's export.
Integrations
Connecting ERP systems, machine data, and internal databases through stable interfaces that survive vendor updates.
AI Integration
Adding intelligence to operational systems, applied where it measurably helps rather than for its own sake.
LLM integrations
Language models applied to technical search, summarization, and classification across your documents and records, with private deployment where needed.
Automation pipelines
Chained flows for extraction, validation, and routing, with human checkpoints at the steps where a wrong answer would be costly.
Business process optimization
We measure where time and rework accumulate on the floor and in the office, then redesign around automation instead of papering over the cracks.
How an engagement runs
A clear path from first conversation to a working system, with a review point you can hold us to at each stage.
- 01
Discovery
We sit with the people who actually run the process, map the workflow end to end, and agree on what a good outcome looks like before any code is written.
- 02
Architecture
We decide where data lives, which systems connect, and where automation or an AI model genuinely helps. Boundaries and trade-offs are documented in plain language.
- 03
Development
Work ships in short sprints with regular demos. You see progress on real screens, not status decks, and you can redirect priorities between iterations.
- 04
QA
We test against the scenarios your team described in discovery, including the awkward edge cases. Automated checks cover the paths that must never break.
- 05
Deployment
Release happens through a staged pipeline with monitoring in place. We plan rollouts so a launch is a quiet event, not a fire drill.
- 06
Optimization
After go-live we watch real usage, tune the parts that matter, and prioritize the next round of work based on evidence rather than guesswork.
Why teams choose to work with us
We are a small, senior engineering studio, and that shows up in how an engagement feels day to day.
Lean execution
You talk directly to the engineers doing the work. Requirements do not get diluted passing through layers of account management.
Fast iteration
Regular demos on real screens let you steer the build while changes are still inexpensive, which matters when the process is complex.
Engineering focus
We obsess over the durable parts: data models that match reality, architecture that holds up, and code an engineering team can respect.
Practical automation
We automate the steps that waste time and keep people in control where judgment is required. If automation is the wrong call, we say so.
Long-term support
We stay on after launch for fixes, updates, and the next phase, so the system keeps pace with the business rather than freezing in time.
Sectors we know in Munich
We have spent the most time on these areas, so you spend less time explaining the fundamentals of your operation.
Manufacturing
Production planning tools, quality and maintenance tracking, and machine data integration that give the floor and the office a shared, current view.
SaaS
Product engineering for teams building software for industrial and B2B customers who expect reliability and will test it hard.
Industrial Technology
Custom systems and integrations that connect equipment, ERP, and operational data into workflows people actually use to make decisions.
How a typical engagement takes shape
- Challenge
- An industrial supplier tracked quality inspections and maintenance logs across several spreadsheets and a paper trail. By month end, reconciling them was slow and the numbers were rarely trusted, which made planning harder than it needed to be.
- Approach
- We built an internal tool that captured inspections and maintenance events at the source, validated entries against defined rules, and integrated with the ERP so records stayed consistent. A dashboard gave planners a live view rather than a month-end reconstruction.
- Outcome
- Records moved from scattered files into one system the team trusted, and the monthly reconciliation effort shrank considerably. Planners worked from current data instead of stale exports. Exact figures vary by site and are confirmed during discovery.
Common questions from Munich clients
It scales with the number of systems involved and how variable your process is. A single workflow is a contained fixed-scope project; a program across several connected processes is larger and usually phased. We provide a written, phased estimate after discovery rather than a headline figure.
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Written by Inderpal Singh, Engagement Lead, Net17 Solutions. Last updated 1 June 2026.