Vendor-neutral, engineering-first patterns for trusted, AI-ready data systems.
This organization hosts open work around the Data Trust Engineering Manifesto: practical patterns, tools, and community conversation—not a product catalog or sales funnel.
| Website | datatrustmanifesto.org |
| Main repository | DataTrustEngineering — manifesto, patterns, Trust Dashboard MVP |
| Slack | Join the community |
| Discussions | GitHub Discussions |
- Manifesto — principles for certifying data systems by use case, risk, and value
- Patterns — DataOps-style practices (quality, contracts, lineage, observability, AI evals, …)
- Trust Dashboard MVP — working artifact for trust / fairness / drift style monitoring
- Site — Hugo site published at datatrustmanifesto.org
- Read the manifesto and quick start
- Star or watch DataTrustEngineering
- Join Slack or open a Discussion / issue
- Contribute patterns or tools — see CONTRIBUTING.md
Open engineering conversation. Not demos-for-hire.
Related: InfoLibrarian Corporation — Brian Brewer’s company & portfolio (classic product EOS); open work continues here under Data Trust Engineering.