The COALA and WASABI EU projects built an entire industrial voice-assistant framework on top of OpenVoiceOS (a non-profit open-source voice platform) and HiveMind, integrating them with their own Android UI, NLP engine, and Docker stack.
I wasn’t involved in these deployments. That’s the point: the stack is being adopted on its own merits, by teams with real industrial requirements.
WASABI Open Call
The 2nd WASABI Open Call to provide financial support to at least 10 experiments led by SMEs recently closed. This open call is designed to support AI-based digital assistance experiments involving SMEs from manufacturing.
All WASABI Open Call experiments are required to:
- run the WASABI/COALA OVOS Docker stack
- connect via HiveMind
- develop a custom OVOS Skill containing their industrial logic
The usage of OVOS/Hivemind is explained in these 2 documents from the Wasabi project:
Examples of Industrial Applications
1. Worker Guidance & Assembly Support
Experiments like TICONAI and SKITE are using OVOS skills to guide workers during complex tasks such as assembling components, validating procedures, or providing step-by-step instructions hands-free.
2. Quality Control and Error Reduction
Projects like WALLABI and HUMANENERDIA focus on providing workers with real-time instructions and checklists to prevent mistakes. Voice assistants help operators verify settings, remember safety checks, or cross-check parameters.
3. Predictive Maintenance Assistance
Experiments such as GENIUS-PM use the assistant to give maintenance techs quick access to machine health data, fault explanations, and repair steps, especially when their hands are occupied.
4. Logistics, Material Handling & Warehouse Support
VELO and AIVEA use voice to help workers locate items, confirm inventory, or check delivery tasks while moving around a shop floor.
5. Onboarding and Training
ONBOARD and AI-MODE test how new employees can be guided through tasks using voice guidance, reducing the burden on supervisors.
6. Sustainability, Waste Tracking & Resource Efficiency
VAFER integrates voice interfaces with systems that monitor recycling, material reuse, and resource flows, with hands-free reporting in factory environments.
All of these rely on OVOS and on HiveMind for routing communication between devices, Android UI, and backend systems.
What COALA/WASABI Built on Top of OVOS
Although the projects produced no open-source industrial skills, they did create several components around OVOS + HiveMind:
1. A RASA-based Domain Assistant (DA)
Earlier COALA research developed a RASA NLP pipeline trained on manufacturing conversations (about quality checks, troubleshooting, machine operation). In WASABI, this RASA engine is plugged into OVOS as a skill, handling domain-specific dialog.
2. The COALA Android App
An Android front-end for workers, connecting to OVOS through HiveMind.
Early version released here: BIBA-GmbH/Mycroft-Android
Features include:
- login via Keycloak
- text or voice chat
- UI for instructions, warnings, and notes
- HiveMind-based messaging
3. A Full Docker-Based Industrial Stack
Both projects ship a preconfigured Docker environment bundling:
- OVOS
- HiveMind
- Keycloak (user management)
- RASA NLP engine
- COALA connector services
This forms the standard industrial voice-assistant stack that all WASABI experiments must deploy.
4. An Industrial Speech Dataset
COALA published a multilingual speech dataset recorded in factories and workshops: zenodo.org/record/8268928
Why It Works for Industry
The design goals that matter on the factory floor were baked in from the start, not retrofitted: full transparency for regulated sectors, local/edge deployment with no cloud dependency, modular skills for proprietary logic, and HiveMind’s ability to distribute voice nodes across a facility.
OVOS and HiveMind source code: github.com/OpenVoiceOS · github.com/JarbasHiveMind