−50 %*
Inspection effort
KnowFab · Leipzig
Explainable AI for engineering, quality, and production
We make industrial decisions faster, more robust, and more economical without replacing existing workflows.
The objective is data-driven decision support with transparent results in day-to-day operations.
Focus on industrial AI
Hybrid approach combining knowledge graphs and neural networks
Step-by-step integration into existing production workflows
Proven potential from real-world projects
−90 %*
Engineering effort
100 %*
Process transparency
−30 %*
Energy costs
* Potential figures derived from completed project deployments. Results depend on process maturity, data quality, and integration scope.
Trust our network
With partners across education, consulting, technology, and delivery
What makes us different
More data. More context. More robust decisions.
Our hybrid approach processes process, quality, and context data together instead of isolated single signals. That creates artificial intelligence that can capture more relationships and deliver technically robust statements.
Solutions
Three building blocks in progress
Standardized products for scalability, complemented by project work for fast practical adoption.
KnowFab Design
KnowFab Design
Digital support for planning and evaluating production and joining processes.
Entry product with low implementation effort
KnowFab JoinTech
KnowFab JoinTech
Analysis and monitoring of live production processes to reduce scrap and rework.
Usage-based licensing model
Project business
Custom Projects
Tailored AI solutions to build references and continuously improve products.
Long-term focus on scalable product business
Where we start
Where industrial decisions need more context
KnowFab becomes relevant when process data already exists, decisions still require too much manual coordination, and results need to remain technically interpretable. The strongest value usually appears in engineering, quality, and production where data is available, but relationships are still evaluated too slowly, too separately, or without enough technical confidence. That is why our focus is not generic automation, but technically grounded decisions that help people in day-to-day operations.
Engineering
When rulesets, expert knowledge, and design data need to be combined, KnowFab helps teams handle planning and evaluation tasks faster and with more consistency.
Quality
When deviations occur, teams need more than an alert. They need a technically interpretable basis for root-cause analysis, prioritization, and robust corrective action.
Production
When processes drift, scrap risk increases, or stability declines, KnowFab helps turn process, quality, and context data into an operational decision basis.
How an entry project works
From use case to robust deployment
A meaningful entry point does not start with a generic AI promise. It starts with a clearly defined technical problem. Together, we review which data already exists, where real decisions are made today, and what level of transparency will be required once the solution reaches operations.
From there, we prioritize a starting point that is both technically relevant and operationally realistic. This creates pilot projects, product building blocks, or integrations that do not stop at demo mode but fit real workflows and can be accepted by engineering, QA, and production teams.
What we focus on at the start
- Clear target metric instead of open-ended innovation search
- Assessment of existing data quality and interfaces
- Traceable results for domain experts and operations
- Step-by-step expansion from pilot to product or integration module
Newsroom
Latest updates
Short updates on development, pilot initiatives, partnerships, and technical framing.