CEO
Beth Rudden
Beth founded Bast to build trusted, explainable AI infrastructure: AI grounded in approved knowledge that can show, for every answer, exactly which source it drew from.
About
Bast's story.
Bast builds explainable healthcare AI. Data is a belonging of human experience. An answer should carry the source it came from, so a person can understand information in their own circumstances and knowledge can emerge.
How Bast got here.
Years of paid research and development with clients: researching, building, deploying, and iterating. Commercialization is underway.
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Bast AI is incorporated.
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Client pilots and the infrastructure build.
Client pilots ran alongside the infrastructure build.
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Developer tools and production client work.
Developer tools were commercialized: 33 Python libraries, 13 APIs, and 19 pipelines. Client work in production included Lurie Cancer Center, Medic Copilot, and Maryville University. At Maryville, nursing attrition was down 12%, and $1.2M was saved.
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The platform is commercialized.
Craig Hospital piloted the Bast AI platform. That pilot is where Bast learned to use ontologies as lenses on Craig's data, with the patients, administrators, and nurses who took part. BastCare did not exist then. The platform was commercialized with an admin UI and a chat release. 2 of 5 patents were accepted.
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BastCare alpha released.
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Evidence library.
The public evidence library opened, with sources and caveats beside the findings.
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BastCare pilots for care teams.
BastCare pilots launched for care teams.
Principles from nature.
Janine M. Benyus described how living systems endure. Bast uses those patterns in decisions about compute, architecture, data, and the relationship between people and machines.
Learn from nature as model, measure, and mentor.
Janine M. Benyus
Biomimicry: Innovation Inspired by Nature
- Nature fits form to function. Use the right tool for the reason at hand.
- Nature recycles everything. Reuse a verified answer before generating another.
- Nature rewards cooperation. A person and a machine each bring what the other cannot.
- Nature taps the power of limits. A trustworthy system knows what it cannot support.
Cynefin, and different ways of knowing.
Dave Snowden’s Cynefin framework sorts a situation by how cause and effect can be known. Each domain calls for a different way of knowing and acting.
- Clear. Cause and effect are visible. Sense the facts, place them in a known category, and follow an established practice.
- Complicated. Cause and effect take expertise to uncover. Sense, analyze, and respond. Several sound answers can exist.
- Complex. Cause and effect show themselves in hindsight. Probe, sense what happens, and respond. The practice emerges from the probe.
- Chaotic. Cause and effect cannot be found in time. Act to create stability, sense the effect, and respond.
Confusion, also called disorder, sits at the center. It is the state of not yet knowing which domain applies.
A way of acting that fits one domain does not fit another.
Bast’s principles page connects Bast’s approach to Dave Snowden’s insistence that action must fit the nature of the situation.
The manifesto.
Data is a belonging of human experience.
The team.
CTO
Thanh Lam
Thanh leads the architecture of Bast's verification layer — the system that sits between an organization's approved knowledge and the model's output, checking scope and source grounding before any answer ships.
Beth and Thanh work with a wider team of contributors.
What to do next.
BastCare is free to download.