Organization purpose
Open Factory Initiative is building open-source infrastructure for factory intelligence, with a focus on transparent, governed, human-accountable manufacturing AI systems.
For Funders
Open Factory Initiative is building open-source infrastructure for factory intelligence, with a focus on transparent, governed, human-accountable manufacturing AI systems.
One-page grant brief
Public-benefit mission
The public-benefit mission is to help manufacturers, engineers, researchers, and contributors build shared infrastructure for responsible AI-assisted manufacturing without creating another closed black box.
Core project
Factory Intelligence Platform is the core open-source technical project: an early-stage Factory Intelligence Layer for connecting manufacturing systems, contextualizing operational data, detecting process drift, and supporting human-reviewed AI-assisted workflows.
Why the work matters
Manufacturing teams need transparent, interoperable, and governed infrastructure for AI-assisted workflows that preserve source traceability and human accountability.
Fundable roadmap area
Support for site-specific onboarding, local-first AI, SLM/LLM evaluation, RAG, model governance, and validation-aware documentation would help OFI develop safer, reusable patterns for adapting open factory intelligence tools to real manufacturing environments while maintaining privacy, governance, and human accountability.
Current stage
Early-stage open-source development
Type of support sought
Grant funding, technical collaboration, advisory support, and ecosystem-building support
Funding contact
For funding conversations, partnership discussions, or grant-related inquiries, please contact Open Factory Initiative through the Contact page.
Contact Open Factory Initiativehello@ofinitiative.orgSite-specific onboarding, local-first AI, and model evaluation
Site-specific onboarding will help each participating facility map its assets, lines, process signals, quality events, procedures, terminology, and governance requirements into the Factory Intelligence Platform.
A fundable roadmap workstream is site-specific onboarding, local-first AI, SLM/LLM evaluation, RAG, model governance, and validation-aware documentation. This work should remain planned research and development for human-reviewed, site-specific, governed decision support.
Any site-specific model training or adaptation work should be governed by explicit data-use agreements, privacy and security controls, human review, evaluation criteria, and adoption responsibility by the participating organization.
Data-use boundary
Public demos should use synthetic or permissioned data. Proprietary facility data should not be used for public model training without explicit authorization. Model outputs should be treated as decision support, not automatic decisions.
View roadmap12-month plan
The first year is organized around foundation, reference implementation, community engagement, and sustainability planning.
Months 1-3
Foundation and governance
- Complete nonprofit setup milestones
- Improve documentation
- Strengthen project governance
- Define contributor pathways
Months 4-6
Reference implementation
- Advance the Factory Intelligence Platform vertical slice
- Improve simulator-backed demonstrations
- Publish technical architecture resources
- Create the foundation for site-specific onboarding workflows and a Site AI Package outline
- Expand validation and testing practices
Months 7-9
Community and adoption
- Grow contributor onboarding
- Host technical working sessions
- Engage manufacturing, automation, quality, and validation professionals
- Publish implementation guides
- Gather input on facility context mapping, RAG-before-fine-tuning patterns, and human-review paths
Months 10-12
Demonstration and sustainability
- Produce public demo materials
- Document outcomes
- Prepare SLM/LLM evaluation workflow and model governance materials for synthetic or permissioned scenarios
- Prepare follow-on funding materials
- Formalize long-term roadmap and governance improvements
Budget categories
Funding discussions can be organized around high-level categories rather than fixed public dollar amounts.
Open-source software development
Technical architecture and documentation
Community building and outreach
Governance, compliance, and administration
Infrastructure, hosting, and tooling
Site-specific onboarding, local-first AI, SLM/LLM evaluation, RAG, model governance, and validation-aware documentation
Training, workshops, and educational resources
Evaluation, reporting, and sustainability planning
Hardware and test environment support, if applicable
Expected outcomes
Near-term outcomes should be concrete, public, and realistic for an early-stage open-source project.
Improved public documentation
Published reference architecture materials
Working open-source demonstration workflows
Contributor onboarding path
Governance and decision-making materials
Public roadmap and issue backlog
Site onboarding template
Site AI Package outline
Facility context mapping framework
Data-use and governance checklist
SLM/LLM evaluation workflow
Model governance documentation
Human-review and evidence-traceability documentation
Synthetic or permissioned demo scenario
Community engagement assets
Funder-ready reporting structure