Visual inspection

Visual inspection solution that automates end-of-line quality control in industrial environments through the usage of machine learning.

CUSTOMER
Wenglor | Deevio
SECTOR

AI & industrial automation

FOCUS
Computer Vision
·
Automation
Wenglor | Deevio
Prozess

01

Research &
Interviews

We analysed market needs, customer problems and developed a business model.

02

Feature scope
& wireframes

Defining high-priority functions in collaboration with stakeholders and creating wireframes

03

MVP design
& development

Design of a user-friendly interface, and development of the backend.

04

Entwicklung & Markteinführung

Introduction of the MVPs with pilot customers whose feedback was used for product refinement and function validation.

ProCess

01

Recherche &
Business Model

Wir analysierten Marktbedürfnisse, Kundenprobleme und entwickelten ein Geschäftsmodell.

02

Feature Scope
& Wireframes

Definition von High-Priority Funktionen in Zusammenarbeit mit Stakeholdern und Erstellung von Wireframes

03

MVP Design
& Entwicklung

Design einer benutzerfreundlichen Oberfläche, und Entwicklung  des Backends.

04

Rollout
& Kundenfeedback

Einführung des MVPs mit Pilotkunden, deren Feedback zur Produktverfeinerung und Funktionsvalidierung genutzt wurde.

ProCESS

01

Initial research

User and stakeholder research through the use of personas, user journey maps, and interviews.

02

Business modeling

Definition of value creation, business model and planning.

03

Scope & prototyping

Defining the feature set for the MVP, specifying the functionality, and collecting feedback

04

Technical feasibility

Technical feasibility, cost assessment and implementation planning

PROCESS

01

User interviews

Conducting interviews with experts to identify pain points in manual workflows

02

Prototyping

Design of the first prototypes to validate the concept

03

Usability testing

Conducting interviews to collect feedback and identify issues

04

Implementation

Develop an MVP and publish it for the first user to collect feedback

Challenges

Organizations face challenges managing and automating complex data processes and workflows, often struggling with manual methods. In addition, visual inspection tasks are often performed manually, which can result in inefficiencies and inaccuracies that affect quality control.

✕ Manual visual inspections

Visual inspection tasks are typically performed manually, which can be time-consuming and prone to errors.

✕ Inefficiencies and inaccuracies

✕ Inconsistent product quality

Inaccurate and inefficient inspections result in variability and inconsistency in product quality.

Visual inspection solution that automates end-of-line quality control in industrial environments through the usage of machine learning.

Solution

Data automation and management:

Deevio makes it easy to integrate, analyze, and automate complex data processes. It automates repetitive tasks, improves efficiency, and minimizes errors while seamlessly integrating with existing systems.

Advanced visual inspection

Deevio uses AI-powered computer vision algorithms to optimize quality control. It detects defects and abnormalities with high accuracy, which provides more reliable results than manual inspections.

Customizable workflows

The platform offers flexible solutions that can be adapted to industry-specific requirements. Workflows can be individually designed to optimize data management and inspection processes.

User-friendly interface

An intuitive user interface makes it easy to set up and manage data and inspection tasks, allowing teams to become productive quickly and without extensive training

Finding the right starting point to address relevant problems in industry was a challenge that triebwerk.ai helped us with. It gave us the right direction to build a successful business and product.

Damian Heimel

Damian Heimel

CEO @Deevio