CommonsPARSE

CommonsPARSE is a Canadian infrastructure project that turns public and institutional records into structured, machine-readable data. It helps regional networks, including nonprofits, charities, and civic partners, build smarter tools for coordination, planning, and service delivery. All data is handled within a locally governed and privacy-conscious framework. 

Organizations already use data from places like StatsCan, municipal open data portals, and federal reports, alongside their own filings and internal records. CommonsPARSE takes that same data and makes it machine-readable, structured, and ready for automation. This means faster grant writing, easier compliance tracking, smarter planning, and better tools built on data they already trust. 

CommonsPARSE is managed by PublicBenefit under formal MOUs and Shared Service Agreements with participating organizations. These agreements outline clear opt-in and opt-out rights, maintain contributor ownership of data, and include terms for indemnity, use restrictions, and responsible stewardship.

Most of the data used is publicly available and accessed through collaboratively maintained APIs. Any private or sensitive data is only included with explicit consent and is not shared beyond agreed terms.

All infrastructure is Canadian-hosted to ensure data residency. CommonsPARSE operates in compliance with applicable privacy laws, including PIPEDA and FIPPA where relevant, and follows sector best practices for ethical data use.

CommonsPARSE includes a mix of public, institutional, and voluntarily contributed data. This ranges from government filings, open datasets, and public reports to internal documents like board minutes or program descriptions, shared under agreement. All data is cleaned, structured, and annotated for responsible use in AI systems and public-good applications. Personally identifiable information and private case-level data are never included. 

CommonsPARSE is responsible by design because it is both economically efficient and locally adaptable. The infrastructure is maintained by a professional team, so individual networks don’t need to build or manage their own AI tools. This shared approach reduces duplication, lowers costs, and ensures technical quality.

At the same time, the tools and data models are customizable. They can be adapted to fit the needs of different regional, community, or cultural clusters, allowing for local relevance without sacrificing reliability or governance.