IIoT-based Solutions for

Holistic Stream Data Analytics & Process Mining

in Automated Production

Products & Services

Infrastructure & Integration

for Simplified Access of Existing Low Level Process Control and Sensor Data for Structured Stream Analytics

Knowledge & Solution Transfer

for Automated and Prioritized Transfer of  Successful Analytics Services and Optimization Measures

Process Analytics & Service Toolkits

for Data Driven Training of Digital Twins under Unified View on Systems, Processes & Products

Selected Use Cases

We offer Solutions for a Wide Variety of Challenges:

Process Mining in Automated Production

Commissioning of automated production systems usually follows standardized, stability-oriented procedures. Hence, the resulting performance is limited and the potential to increase productivity often exceeds 10%. However, relevant factors (e. g. system parameters, environmental conditions) influencing the process are often unknown.

  • Your Challenge

    • Unknown Root Causes for Occurring Deviations and Performance Discrepancies
    • Selection and prioritization of appropriate optimization measures require Time and Expertise
    • With increasing System Complexity these Tasks become even more Challenging, even for Experts

  • Our Solution

    • Centralization of Data Streams for Monitoring of Complex Automation Systems
    • Generic Algorithms for Automated Training of Process Models from Discrete and Continuous Data
    • Interpretable Data Models as a Solid Basis for Targeted Application of Specific Analytics Services 
    • Complexity Reduction through a Unified View of Systems and Processes for Additional Insights

  • Benefits & Advantages

    • Profound Verification of Actual System Behavior through Interpretable Process Visualization
    • Simplified Evaluation of various KPIs e. g. for proofs of conformity for certification
    • Prioritization of Optimization Potential through detection of bottlenecks and critical processes

System and Process Benchmarking

Production processes of identical or similar lines, cells or machines often run in parallel, at the same or at a different location. Alternatively, equal processes are operated using similar systems from different manufacturers.

  • Your Challenge

    • Similar Production Systems often show Different Performance but the Reason is Unknown
    • Lack of Transparency about Relevant KPIs for the Quantitative Assessment of System Performance

  • Our Solution

    • Data Driven Training of Process Models enables for Direct and Quantifiable Comparison
    • Evaluation of Statistical Key Figures and/or Data Features for Localization of Existing Deviations
    • Detection of relevant sub-process steps and defective technical components

  • Benefits & Advantages

    • Minimized Efforts and Time for Identification of Deviations and Detection of Causalities
    • Incentives for Plant Operators through Measurable Performance Indication and Competition
    • Increased Overall Performance through Targeted Modification of Low Performing Systems
    • Validation of Existing Potential proven by the more Powerful System Counterpart

Eco Assistant for Sustainable Production

Environmental protection and sustainability are becoming increasingly important for consumers. Eco-friendly manufactured goods, therefore, drive the direction of production companies.

  • Your Challenge

    • Lack of Transparency about Initial Situation, Existing Potential and Appropriate Measures
    • Energy and Media often Account for 40% of Operating Costs
    • High Costs for the Retrofitting of Existing Systems with Sensors, e.g. for Energy Measurement

  • Our Solution

    • Control Data based Efficiency Monitoring and Energy Management
    • Overall Picture for Prioritization of Existing Optimization Potential
    • Analytics Toolbox for Transferable Testing of Successful Improvement Measures

  • Benefits & Advantages

    • Enabler for Continuous Efficiency Improvement e.g. following the PDCA circle
    • Comfortable Solution for Certification Proofs e.g. for Energy Management (ISO 50001)
    • Long-term Cost Savings through Invests for Sustainable Production

Online Process Monitoring & Anomaly Detection

Maintenance is usually planned preventively or after fixed intervals. Critical process deviations not detected in time, resulting in unexpected system failures.

  • Your Challenge

    • Critical System States are not Reliably Detected by Local Monitoring of Subcomponents
    • Increasing Complexity Prevents Continuous Monitoring of the Overall System
    • Often even Slight Process Deviations Significantly Reduce System Performance

  • Our Solution

    • Structured Processing of Heterogeneous Data Streams based on IIoT Services
    • Process Models Implemented as Digital Process Twins for Continuous Anomaly Detection
    • Standardized Access to Process Data Streams via APIs for Additional Analytics Services

  • Benefits & Advantages

    • Alerts for Slightest Process Deviations to Lead Maintenance Teams to Critical Components
    • Traceability of Product Individuals e.g. in Context with Evaluation of Process Integrity
    • Online Monitoring of relevant KPIs and Individual Data or Signal Features
    • Web-Interfaces for Visualization and/or Direct Integration to MES/ERP

Find out what's special:

How our solutions differ from previous approaches:

Complexity Reduction

Our approach provides the analysis under a unified view of machines, processes and the product.

Data Stream Fusion

For structurized and continuous acquisition of heterogeneous data streams from various interfaces.

Existing Data

We initially extract knowledge from existing machine control data and supplement sensors only where needed.

Generic Algorithms

For data driven training of interpretable digital process twins to structurize complex and heterogeneous data streams.


Our Dockerized Services are implementable on various analytics platform solutions and data infrastructures.

Portability of Services

For flexible implementation of performant data analytics infrastructures for various environments and conditions.

System Independence

Flexibility and expandibility of our solution leads to high independence from manufacturers and technical applications.

Team Fine|alyze

We combine Technical Process Knowledge with Expertise in Control Technology, Industrial Communication, Big Data Analysis and Software Development.

Who we are:

The High-Tech Startup Fine|alyze is a Spinoff Initiative from the Institute of Mechatronic Systems at the Gottfried Wilhelm Leibniz University Hannover.

Emerged from the Research Group for Integrated Systems and Machine Learning, it is now our Mission to Optimize Automated Production with Application of our Innovative and High Performant Digitalization and Data Analytics solutions.

Our Customers benefit from our Broad Technological and Methodical Expertise, Agile and Solution-Oriented Approaches as well as Extensive Experience from Research and Transfer Projects.


  • Fine|alyze @ Hafven Smart City Hub Batch#2

  • Fine|alyze @ NBank - Network Meeting for Business Angels and Investors
    Fine|alyze @ NBank - Network Meeting for Business Angels and Investors

  • Fine|alyze at the DIGITAL FUTUREcongress 2020
    Fine|alyze at the DIGITAL FUTUREcongress 2020

    Fine|alyze with a new product demo at the DIGITAL FUTUREcongress 2020 in Frankfurt, where SMEs meet digitization!

  • Fine|alyze founded a company!
    Fine|alyze founded a company!

    Fine|alyze would like to thank all friends and partners for their support so far and look forward to further cooperation !!

  • Fine|alyze is Part of Batch#2 in the Hafven Smart City Hub
    Fine|alyze is Part of Batch#2 in the Hafven Smart City Hub

  • Fine|alyze is part of the 1st Tetramax Entrepreneurial TTX
    Fine|alyze is part of the 1st Tetramax Entrepreneurial TTX

  • Fine|alyze on Public Transport Passenger TV during #HM19
    Fine|alyze on Public Transport Passenger TV during #HM19

  • Fine|alyze wins @ FUTUR X PitchBoXing 2019.1
    Fine|alyze wins @ FUTUR X PitchBoXing 2019.1

  • Fine|alyze wins Second Prize @ the InnoEU Global Innovation Summit – Startup Pitch Competition
    Fine|alyze wins Second Prize @ the InnoEU Global Innovation Summit – Startup Pitch Competition

  • Futur X PitchBoXing

  • Green Startup Special Program
    Green Startup Special Program

    Fine|alyze has been selected for Funding within the new Green Startup Special Program focusing on digitization of the German Federal Environmental Foundation (DBU - Deutsche Bundesstiftung Umwelt)

  • Hafven Smart City Hub – Startup Ceremony Batch#2

    Meet us personally

    at Fairs, Meetups, Workshops and Pitch & Networking Events:

    Twenty2X (09:30)  

    @ Hall 7 - Booth A28/3

    Hannover Messegelände, 30521 Hannover

    more info: Twenty2X

    March 17, 2020
    DIGITAL FUTUREcongress 2020 #Frankfurt (08:15)  


    @ Stand S10, Messe Frankfurt Venue GmbH

    Ludwig-Erhard-Anlage 1, 60327 Frankfurt am Main

    Free Tickets Available

    more info: DIGITAL FUTUREcongress

    February 18, 2020
    Startupday Hannover (15:00)  

    @ VentureVilla Accelerator GmbH

    Walderseestraße 7, 30163 Hannover

    more info: VentureVilla

    February 12, 2020
    Robotik-Meetup #6 (16:00)  

    @ Roboterfabrik

    Appelstr. 11, 30167 Hannover

    more Info: XING / Meetup

    February 11, 2020
    Hafven Accelerator - Startup Ceremony #Batch4 (18:00)  

    more info: Eventbrite

    January 16, 2020
    StartUp MeetUp #15 (18:30)  

    more info: Hafven

    December 10, 2019

    We appreciate the Support of

    • mzh
    • imes
    • hafven
    • nbank
    • starting business
    • leibniz universität hannover
    • acatech - Plattform Lernende Systeme
    • AMA - Applied Machine learning Academy
    • Tetramax -TTX
    • venture villa
    • l3s Research Center
    • DBU
    • H2020
    • VDMA
    • I4MS

      • Walderseestraße 7, 30163 Hannover, Germany

      • +49 511 762 4121

      • info@finealyze.com

      • http://www.finealyze.com


      I agree to my personal data being stored and used according to the privacy policy and terms of use declarations.



      This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 761349.

      This project receives funding by the German Federal Environmental Foundation (DBU - Deutsche Bundesstiftung Umwelt) under the Green Start-up special program.