I sit with social justice organizations, read how their money, grants, reporting, and structure actually move, and design AI tools shaped to that specific operational reality. The tools are called MIAgents (Movement Intelligence Agents): software that reads your data, surfaces what matters, and drafts work for the people already doing the job. Built for data sovereignty. Owned by the org. Shaped by your actual workflows.
18 years inside movement organizations as the finance person, the ops director, the development support worker. That operational depth is what makes the design work.
For EDs, finance directors, and program officers navigating fiscal sponsorship, dual-entity structures, and multi-year grants.
Slingshot is what one engagement produced for Catalyst Youth Collective, a fictional org with the kind of structure I work with most: C3/C4, fiscal sponsorship, mixed revenue. Two MIAgents sharing one data layer. Solar reads the grants side. Lunar reads the accounting side. Together they surface what used to require a finance director and a development director in the same room. Your organization's build would start from your structure, not this one.
Grants & revenue intelligence: lifecycle, payment schedules, compliance, funder reporting
Budget planning & tracking: line items, GL mapping, monthly cycles, actuals
Social justice organizations lose something no budget line captures. When experienced staff leave, they take accumulated wisdom with them. That is the turnover tax on movement work. The Human-AI Partnership Philosophy proposes a different path: technology that preserves institutional memory under community control. The full argument behind this philosophy lives in the writing, where each piece works out a specific part of the case.
Your data is never used to train external models. Processing happens through API providers whose terms prohibit training on your data, with network-isolated deployment available for organizations that require it. Your campaign strategies, community relationships, and organizational knowledge stay under your control. Data sovereignty is not a feature. It is the foundation.
Your organization shapes its tools. Not the reverse. Purpose-built systems that adapt to your workflows, your grant structures, your reporting cycles. The cost of building lightweight custom tools has dropped dramatically. No platform lock-in. No adapting your work around someone else's architecture. You own the code, the data, and the documentation. Any qualified developer can maintain or extend what we build together.
AI that preserves institutional knowledge instead of extracting it. Systems designed so that the wisdom of experienced staff becomes accessible to everyone, regardless of when they joined. Strategic thinking distributed across the organization, not concentrated at the top.
These three pillars form a coherent arc: Data sovereignty protects what your organization owns. Adaptive infrastructure ensures your tools serve your actual work. Collective intelligence ensures that what you know does not walk out the door with any one person.
Continue without AI tools. The risk: other actors, including those working against movement interests, are already using these capabilities, and the gap in capacity may widen.
Adopt commercial AI tools with default settings. Accept the risk. Watch your organizational knowledge feed systems you do not control, governed by terms of service that can change without notice.
Build AI tools that center data sovereignty, distribute intelligence, and preserve institutional memory under community control. The path that advances the mission while strengthening the organization.
The organizational intelligence framing: collective knowledge that walks out the door with staff turnover, systems that fragment rather than connect, technology decisions made without strategic grounding. Luis brings the practitioner view to these conversations. The goal is not implementation. It is clarity about what your organization actually needs and whether AI is the right tool for it.
Purpose-built tools are now tractable without a six-figure implementation, and I help organizations understand the full cost picture, including ongoing maintenance and infrastructure. The question is not whether you can afford to build something that fits your work. The question is whether you can afford to keep adapting your work around platforms that were never designed for it.
Book a scoping conversationThe accounting function reframed as an organizational intelligence hub. Not merely a compliance function, not just a reporting burden, but the place where the organization's full story lives. Every transaction carries meaning beyond the number. Which funders release payments early. Which grant restrictions have been negotiated before and why. What the budget history reveals about program sustainability that no narrative report captures.
Your MIAgent Solar and your MIAgent Lunar are demonstrations of what adaptive infrastructure looks like in practice. Tools built around your actual grant lifecycle and GL structure. Not the other way around. Explore them in read-only mode, or reach out for an access code to try the full interactive experience.
The capacity building gap: grantees are under-resourced on technology infrastructure and the sector has normalized that gap. Fiscal sponsors sit at a unique leverage point: shared infrastructure that can be designed to preserve individual project data boundaries, provided the fiscal sponsorship agreements and data governance policies support it. A cohort model that spreads cost while building collective knowledge across the portfolio.
Each organization retains full control of its own data. The shared infrastructure means no organization has to absorb the full cost of building what they need. This is one of the most significant untapped opportunities in movement technology.
Let's talk about your portfolioOp-eds, frameworks, and a foundational philosophy paper built from years running these systems from inside. The ideas come from the work.
I have run financial operations, managed grants, and supported development inside nonprofits at every level: from grassroots organizations navigating paired C3/C4 entities to philanthropic intermediaries managing complex grant-making programs. I have been the finance person trying to make QuickBooks speak movement language, the operations director wrestling with disconnected systems, and the development support translating community wisdom into funder metrics.
That operational depth is what makes the technology work. I am not a tech person who learned nonprofits. I am a nonprofit person who built the tools my field needed.
Movement-Aligned Intelligence. Technology that serves collective liberation rather than corporate efficiency.
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