The World's Community Foundation
Community foundations coordinate the resources, needs, and people in their local ecosystems. Ryndel Labs empowers community foundations with data and modeling tools that pinpoint systemic failures and efficiently deploy resources into those gaps. We aim to weave all the local networks together to serve as the community foundation for the world.
In effect, studying and solving the world's inefficiencies at scale.
What We See
The world is stuck in a local maxima.
Organizations locally optimize with minimal sharing of resources, information, and trust between organizations. With minimal higher-level routing & orchestrating of the system.
This is because the systems are set up to remain siloed, to the benefit of the few at the expense of the network.
The result is the world's dependence on serendipity.
We believe serendipity is inefficiency. Our work is to systematically manufacture what the world currently waits to stumble into.
We observed this by being in rooms where capital and resources move, ranging from governmental to community and economic institutions, where we found commonality in their pitfalls.
The federal funding cuts forced a reckoning with efficiency that the nonprofit sector has been able to defer for decades.
The cost of modeling networks at scale has dropped by orders of magnitude thanks to the breakthroughs in transformer architecture, which made language-heavy institutional data tractable to process for the first time.
The broader political and technological reset is producing the most rapid change the world has seen in a long time, making the inefficiencies we have been talking about scale in comparison.
Community foundations coordinate the resources, needs, and people in their local ecosystems. Ryndel Labs empowers community foundations with data and modeling tools that pinpoint systemic failures and efficiently deploy resources into those gaps. We aim to weave all the local networks together to serve as the community foundation for the world.
In effect, studying and solving the world's inefficiencies at scale.
Ryndel Labs provides
Mapped ~75k US organizations
Two beta partners
We are in active development with two community foundation-like orgs. Using these learnings and data to enforce the scalable product being built.
Three failures. Every time.
Nobody is responsible for the whole
Every actor focuses on themselves without anyone owning the space as a whole. The predictable self-serving incentives cause for no restructuring of the system. No one measures how much more effective the connections/partnerships and overall system could be and are only able to partially do so when entities have time and resources left over. This means it effectively never happens. This also leads to many partnerships to be due to serendipity, which means many other helpful connections could have formed but didn't.
The incentives reward fragmentation
Institutions measure internal efficiency without accounting for whole-ecosystem efficiency. Funders are rewarded for dollars out and visibility, not for outcomes. Organizations compete for capital with the organizations they should be collaborating with. Funding outlives the problem it was allocated for and gets stuck without a way to wind down or allocate to the world's new needs. And no one is able to be the first mover and pay first toward obtaining a better equilibrium, because the first mover pays for it alone. The ego is built into the world and only rewards fragmentation.
The complexity exceeds any single coordinator’s bandwidth
Even with the right incentives and someone to coordinate the whole system, the complexity is too much. Coordination means matching thousands of actors across dozens of sectors, each with its own vocabulary, each holding a piece of the relational knowledge in people's heads and each with informal ties. Trust is slow and expensive to verify at scale. Context does not transfer cheaply. Hence, this is incredibly expensive to fix (if using legacy methods) causing communities to stay stuck at a local maxima.
“Serendipity is inefficiency.”
One quality map.
Many use cases
Our platform enables high-quality hyperlocal data to scale
Our platform maps community networks by enabling scalable hyperlocal data collection for community foundations/facilitators. This high-quality, lower-cost alternative enables the network to scale in densely inside and across many communities. Then by stitching the individual networks into one, it enables our platform to map, connect, and share info at a much higher quantity for all entities.
Mapping: understanding all the entities needs/resources and their operational procedure plus history to see how those needs/resources change under different scenarios. From this, predicting the gaps/inefficiencies in the networks
Sharing: shocks often impact many communities, so by using our mapping to measure the effectiveness of communities set up to shocks, it allows us to share timely information to enable other communities to be set up correctly and, in turn mitigate the community impact of shocks
Connecting: providing partnership suggestions (to individuals who will actually take action) to facilitate the solutions to the gaps
Built where no one else will go.
Hyperlocal entity data is the hardest data to collect. It is undigitized, heterogeneous, politically complex, and requires genuine local institutional trust, not just public scraping. This is structural difficulty at scale is exactly why our work is able to do what other networks have not.
Community foundations are the perfect entry because of their local trust, relationships, and historic communal knowledge that make effective mapping and coordination possible. This trust is imperative as the connector doesn't hold all of the information about its community; the community itself holds it, distributed across every organization and person inside. Trust enables the distributed self reporting necessary for building a truly quality communal map. Specifically every community is unique in its structure, culture, and attributes meaning scaled mapping must be personalized. By having the trust component in each community handle the last mile personalization of data collection and mapping it enables us to effectively scale with the nuance the problem deserves.
Our mapping also directly decreases their costs for the community foundations while increasing mapping and connection quality from having much larger scale quality data. It immediately has a measurable impact on the community members and their partnership effectiveness. This mapping also acts as a single source of truth which enables much more effective communication when working to fill community gaps (potentially for fundraising). Lastly this scaled mapping enables the underlying inefficiency model to do something special.
Communities are where every network intersects.
A community is not a silo. It is the ground floor where venture capital, government, universities, philanthropy, nonprofits, and local enterprise all touch. Map what is happening inside one community and you are mapping a cross section of every network that runs through it.
Philanthropy connectors are our first partners because they already hold the trust and the relationships that make real coordination possible. What the connector has is legitimacy. What we bring is the network layer that turns legitimacy into structure. Together we surface the connections that already should have happened, and the ones that will become critical as the community changes.
The value compounds with scale. One mapped community is useful. Ten connected communities change what is possible for every nonprofit, funder, researcher, and operator inside them.
Most of the conversation in capital allocation focuses on the allocators. We are building for everyone on the other side of the table too.
A nonprofit looking for a funder whose thesis fits their work
A founder looking for capital aligned with their specific problem rather than their zip code
A researcher trying to reach the business that can take her work out of the lab
An operator in an underserved market who has been invisible to every standardized database
Our job is to make people findable to the people who should be looking for them. Coordination runs in both directions.
The dataset is one thing. Three readers look at it and see three different resolutions. The shape of what they find is what each of them came looking for.
The dataset is of better quality and scale than is applicable to all different entities and use cases.
Mapping is the input.
The model is the product.
Model locally refines and shares across networks
Coordination failure has a repeatable structure.
The model is built on our platform's high-quality community data that persists over time and scales easily. Specifically, our model enables us to identify both current and future resource/needs gaps in a local ecosystem, then identify the partnerships that would be best suited to fill them. The data enables local network model refinement, which is shared across model deployments. Because every community is unique, making direct generalization of model weights impossible, it can still learn how community networks fundamentally work. This fundamental model nature is then generalizable and can be used to predict a system's output given any inputs, systematically identify recurring inefficiencies within an ecosystem, and understand the perfect solutions to these inefficiencies/systems stuck in a local maxima. We are not building a system to find people or store relationships. We are building a system that models relationships and proactively uses them to generate actionable collaborations between institutions. Every initiative we generate, successful or failed, feeds back into the underlying data. The model gets sharper with use. The dataset gets more valuable with every deployment.
The model's theoretical grounding
- —Asset-Based Community Development — the taxonomy for what gets mapped.
- —Burt's structural hole analysis & Barabási's network science — the math for finding the gaps.
- —Pfeffer & Salancik's resource dependence theory — why the gaps persist.
- —Banerjee & Duflo's causal inference — for measuring whether the connections we facilitate actually produce impact.
The long-term ambition is something closer to a research institution in the tradition of Santa Fe Institute or Bell Labs, but more directly connected to capital allocators (being a Fund of Fund). Sector-agnostic research on system inefficiency, subsidized by the allocators who benefit from the findings, feeding the model that directs our own deployments and initiatives. Every iteration of the map sharpens the model. Every deployment generates new data. The model becomes more accurate with every community mapped and every action taken.
The same three structural failures appear in every broken system we have studied, in different combinations and severities. If they are enumerable and measurable, then coordination failure is not intractable. It is a pattern problem. Pattern problems have pattern solutions that generalize across domains without reinvention.
Traditional
Ryndel
One map. Every slice trains it further.
Every mapped community adds density to a single shared layer of hyperlocal relational data. The cohorts who read the map read it through different lenses, and every lens produces feedback the model learns from. Hover a cohort to see its slice.
Every slice draws from the same underlying community data, and every slice builds on top of it. Both the raw data and the model that reads it get denser with use. What makes the map valuable to one cohort is what makes it valuable to all of them. The value compounds across cohorts rather than dividing between them. It is also what makes the map hard to replicate: you cannot slice data that was never collected.
Every community looks different from the inside. Coordination failure looks the same. The same patterns keep showing up: capital that cannot see the operator, a resource and a need in the same place that never meet, an ecosystem one introduction away from a better state. Enough communities in, and the model starts reading those patterns directly. It knows which inefficiencies repeat, which local maxima everyone is stuck in, and where the next move actually moves things.
Understanding how
systems break.
Ryndel Labs is built as an interdisciplinary research and development institution in the tradition of the Santa Fe Institute and Bell Labs, but it is directly applicable to the large-scale applications of our platform. Studying complex systems and network theory across many applications, we have found insight from biology, economics, pure mathematics, physics, and many more, because of how networks and inefficiencies generalize. This enables us to have a set of specific inefficiency application R&D (which partially generalizes across inefficiencies) and deeper non-specific application R&D, both of which build our underlying world inefficiency model.
Below is our active and future surfaced (not yet pursued) research. If your work belongs here, or if you want to join a project, reach out.
Steps to multi-
generational impact
For-profit for scale. Mission at the core.
Our goal is to make the largest, longest-lasting impact possible on the world, measured in real outcomes across real communities. That goal dictates the structure.
A nonprofit can do meaningful work but cannot compound at the speed the problem requires. A standard for-profit can scale but usually drifts from the mission under the pressure of quarterly capital. We are building Ryndel as a for-profit institution because scale is the only thing that matches the size of what we are fixing, with the values, incentives, and governance designed from the start to hold mission in place as the institution grows.
The focus is mapping and modeling. Everything else is downstream of that. As the model finds inefficiencies worth filling, we stand up the funds, initiatives, and partnerships that fill them. Sometimes that is a coalition of existing funders. Sometimes it is a standalone fund built around a specific hole. Sometimes it is a direct initiative we operate ourselves. Every deployment feeds new data back into the model. Scale and mission are the same loop.
Curiosity · Impact ·
Structural scale
We are foundationally curious people, drawn to the deep systems-level connections running between every field and harnessing it for making the largest impact possible.
We have spent our lives taking that curiosity into building things, weaving disparate entities into coherent systems that can carry themselves. Ryndel is us pointing that same instinct at the outcome we care more than anything about: the largest sustained impact we are capable of making on the world, compounding for centuries after we are gone. We have built it so that the work can be done with meraki.
Ryan has spent his entire life studying how the world works at the system level, then diving into the specifics and watching how the seemingly unconnected threads converge. Then, applying these concepts to make a sustainable change in the real world. This can be seen from his 14 years in the startup ecosystem with numerous companies/inventions, 2 years in VC, and 13 years of public speaking (including his stint on the TEDx stage on neurodiversity and reframing struggle).
He is driven by a refusal to accept that broken systems have to stay broken, and sees dysfunction as an opportunity yet to be solved.
Thirteen years of cross-domain research across renewable energy, biomedical engineering, and fintech. Presented at national conferences. Published in the Library of Congress. Built and scaled thirty robotics education initiatives across nine countries, reaching over seven thousand people, including twenty five hundred children with a particular focus on girls.
Hoshita works at the intersection of design thinking and systems research. Every domain she has worked in had a different vocabulary for the same underlying coordination problem. Ryndel is the structural answer.
We are enthralled by the world's deep interconnectedness and by how we can apply it to enable others and create genuinely sustainable change that outlives us.
The right people
make this work.
We are building something designed to outlast any single founder, fund cycle, or market condition. If you recognize yourself below, reach out directly. We respond personally to every message.
Tell us what you are working on.
We'll usually respond within 48 hours. Then, potentially set up a conversation to discuss whether there's a fit.
The entry point for the map. Phase 1 cannot exist without foundation partners willing to map their ecosystem. If you are a community foundation or philanthropy connector, we want to talk to you first.
Usually within 48 hours. We respond personally.
A conversation to understand what you are working on and where the overlap might be.