Runcible is not a first-time-founder AI wrapper company.

The team combines four forms of execution capacity rarely found together:

Execution Credibility for Institutional AI

1. Founder-method capacity — the original Natural Law, operational epistemology, decidability, and adjudication framework behind Runcible.
2. Enterprise execution capacity — prior company formation, acquisition, integration, operations, investor relations, and successful exits in the $100M range.
3. Institutional domain capacity — medicine, law, compliance, behavioral science, legal systems, and organizational transformation.
4. Protocol production capacity — a trained global team capable of translating domain knowledge into Runcible protocols, test cases, diagnostics, and Decidability Records.

The company has been built by people who have worked together across ten to twenty years, with experience serving startups, mid-market companies, Fortune-scale enterprises, and Microsoft-platform ecosystems.

The team’s execution history includes:

• Built, bought, scaled, and sold companies in the $100M range.
• Growth through both organic execution and acquisitions.
• Clients spanning startups, mid-market companies, and Fortune 100 / Fortune 400 enterprises.
• INC 500 recognition three times.
• Recognition in Forrester Wave.
• Ranked among top global independent agencies by customer satisfaction.
• Deep experience in technology, operations, behavioral economics, legal systems, platform execution, and strategic transformation.

Runcible requires a team that can do more than build software.

It requires a team that can convert a research method into protocols, protocols into runtime systems, runtime systems into institutional workflows, and institutional workflows into defensible AI-mediated work.

That is the team we have assembled.

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Executive Team

B. E. Curt Doolittle

Founder and CEO

Curt Doolittle is the founder of Runcible and the primary author of the Natural Law, operational epistemology, decidability, and adjudication framework underlying the platform.

He is a philosopher, social scientist, technologist, and serial founder with experience across technology, law, institutional systems, and research.

Curt has founded, built, acquired, and sold multiple technology and consulting companies, including companies serving Fortune-scale clients and the Microsoft ecosystem. His prior work includes early research in AI, legal AI, episodic memory, phonetic tokenization, enterprise consulting, Microsoft-platform integration, and large-scale business transformation.

Relevant prior companies and projects include:

• Excel Data
• Redmond Technology Partners
• Data Dimensions
• Ascentium
• Natural Law Institute
• Reality by Chanting / Oversing
• Runcible AI

Curt’s role at Runcible is to preserve the integrity of the method, drive the theoretical architecture, direct protocol design, and ensure that Runcible remains focused on its core mission:

converting AI-generated hypotheses into testable, reviewable, certifiable, and actionable institutional work.

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Bradley H. Werrell, D.O.

President and Co-Author

Dr. Bradley Werrell is President of Runcible and co-author of the methodology and volumes.

He has worked with Curt Doolittle for five years, helping convert the theoretical work behind Runcible into communicable, computable, and operational form using LLM technology.

Brad is a practicing physician and is pioneering the application of Runcible in medical and healthcare contexts. He is also a Senior Fellow at the Natural Law Institute, an amateur historian, and author of work on the evolution of thought culminating in Natural Law.

At Runcible, Brad is responsible for external representation of the company, medical-domain application, and helping translate the method into forms legible to investors, institutions, and professional users.

His role is especially important because Runcible must bridge theory, medicine, institutional decision-making, and AI-mediated reasoning.

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Eric Adams

Chief Operating Officer

Operations, Finance, Legal

Eric Adams is a senior technology executive with more than 25 years of experience guiding companies through growth, transformation, operational execution, investor relations, finance, legal coordination, and successful sale.

He has served as COO across multiple domestic and international growth companies brought to successful outcomes in the $100M range, including work connected to Lante, Ascentium, Razorfish, Filter, and Merkle.

Eric’s experience spans technology consulting, business intelligence, executive coaching, M&A strategy, corporate transformation, and full-lifecycle program management.

He has worked with startups, mid-market innovators, and Fortune-scale enterprises across financial services, telecom, technology, education, and consumer markets, including major initiatives for companies such as Microsoft, AT&T, Starbucks, and MasterCard.

At Runcible, Eric leads company operations and helps build the execution infrastructure required to move Runcible from founder-financed proof to institutional-scale deployment.

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Moritz Bierling

EVP, Outreach, Operations, and Platform Execution

Moritz Bierling is a technology evangelist, startup consultant, and Senior Fellow at the Natural Law Institute.

He works at the intersection of technology, governance, communication, institutional design, and market adoption.

Moritz specializes in translating complex ideas into teachable, market-facing, and operationally useful forms. His work includes developing, teaching, and promoting statecraft, leadership formation, and institutional adaptation.

At Runcible, Moritz focuses on outreach, sales, advocacy, platform execution, media relationships, customer relationships, and ensuring that Runcible’s solutions align with market needs.

His role is to help convert Runcible’s technical and philosophical depth into institutional adoption.

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Francis Zhou

Chief Product and Program Officer

Francis Zhou is a senior product and program leader with Microsoft experience across quality assurance, engineering, operating systems, mobile platforms, cloud services, and customer-facing product management.

Francis combines technical depth with customer interaction — a rare combination necessary for Runcible’s product path.

His background includes:

• software development
• quality assurance
• mobile client platforms
• cloud services
• human-device interaction
• product requirements
• engineering coordination
• customer problem translation

At Runcible, Francis is responsible for product and program execution: converting institutional needs into product requirements, aligning engineering with customer workflows, and ensuring that Runcible’s runtime, protocols, and platform surfaces become usable products rather than merely correct systems.

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Luke Weinhagen

Chief Compliance Officer

Luke Weinhagen is a philosopher, author, senior consultant, software developer, delivery manager, and former P&L owner with more than 20 years of experience in technology consulting and business transformation.

His technical experience includes API development, digital commerce, software architecture, system architecture, and leading domestic and international teams from discovery through deployment and training.

Luke is also the author, consultant, and presenter of “Core Human Competence,” a program focused on demonstrated agency, cooperation, self-confidence, and practical competence.

As a Senior Fellow at the Natural Law Institute, Luke works on the science of cooperation and the institutional reforms necessary for scaling civilization under modern technological conditions.

At Runcible, Luke is responsible for maintaining the truthfulness, ethics, morality, possibility, and legal legitimacy of Runcible outputs.

His function is to help ensure that Runcible does not merely produce useful AI output, but institutionally warrantable output.

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Noah Revoy

EVP, Training

Noah Revoy is a psychologist, author, behavioral trainer, and private practitioner focused on increasing individual agency rather than therapeutic correction.

His work centers on behavioral modification, resistance to manipulation, autonomy, agency, and human adaptation under institutional and technological pressure.

Noah’s expertise is directly relevant to Runcible because AI systems must be trained away from passive assistance, social conformity, and normativity, and toward excellence, agency, judgment, and constraint.

His background in manipulation and behavioral constraint makes him especially valuable in the development of protocols addressing hallucination, persuasion, user vulnerability, social influence, and institutional misuse.

At Runcible, Noah is responsible for LLM training strategy and the development of protocols that help people use AI within personal, social, and institutional orders.

His long-term objective is behavioral adaptation and skill improvement in organizations at scale.

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Brandon Hayes

EVP, Strategy and Legal Systems

Brandon Hayes is EVP of Strategy and Legal Systems, President of the Natural Law Institute, and a co-founder with Curt Doolittle of NLI and Runcible.

He is responsible for legal systems, legal strategy, policy formation, and legal-domain application of the Runcible method.

At Runcible, Brandon maintains the adjudicability of legal outputs and will help lead justice-related expansion and implementation.

He currently uses Runcible in active legal contexts, including amicus briefs and court-facing legal argumentation. His work focuses on improving the means by which courts and institutions reason, decide, and record their judgments.

Brandon’s role is central to Runcible’s legal and governmental applications because Runcible’s core process — claim, evidence, adversarial test, judgment, record — is deeply aligned with legal adjudication.

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Training and Protocol Team

Runcible also includes a globally distributed training and protocol team of approximately twelve specialists fluent in the methodology and its application.

Most members are niche authors, researchers, analysts, or practitioners working across psychology, philosophy, law, politics, mathematics, agriculture, and social theory.

This group represents one of Runcible’s most important scarce assets.

The training is not easily reproduced. It requires mastery of Natural Law, operational epistemology, demonstrated interests, reciprocity, testifiability, falsification, adjudication, and the conversion of domain claims into protocols, tests, and Decidability Records.

Representative contributors include:

• Bryan Brey — Natural Law, behavioral economics
• Martin Stepan — Natural Law
• Michael Surrago — foundations of mathematics
• Jacob Zohny — Natural Law
• Josh Reider — agriculture and testifiability
• Robert Roe — Natural Law
• Rob McMullan — Natural Law and politics

The company expects to relocate or consolidate key contributors where necessary for IP protection, security, operational coherence, and continuity of production.

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Hiring and Buildout Plan

Runcible should be understood as operating more like a platform division than a narrow software startup.

The company requires:

1. Technical development staff for Oversing and Runcible runtime hardening.
2. Operations and test teams for validation, QA, security, deployment, and enterprise readiness.
3. Protocol production teams to research, analyze, produce, test, and implement domain protocols.
4. Domain analysts combining industry specialization with library-science-style classification and corpus discipline.
5. Customer implementation teams to support third-party integrators, consultants, and enterprise adoption.
6. Training and support teams to produce documentation, videos, guided workflows, trained AI assistants, and implementation materials.

The operational model is clear:

• analysts produce protocols
• domain specialists validate them
• Runcible tests and hardens them
• negative testing teams falsify them
• implementation teams package them
• customers deploy them inside institutional workflows

This is how Runcible scales across domains without becoming a custom consulting firm.

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Why This Team Matters

Runcible is difficult because it combines several hard problems:

• philosophy into operational method
• language into computable claims
• AI output into adjudicated records
• institutional rules into protocols
• protocols into runtime systems
• runtime systems into enterprise workflows
• enterprise workflows into defensible action

A normal AI team can build assistants.

A normal enterprise software team can build workflow systems.

A normal compliance team can apply rules.

A normal data team can curate datasets.

Runcible requires all of these capacities together, under a method that very few people have been trained to understand.

That is why the trained team matters.

That is why securing the team matters.

That is why the financing is not merely a hiring plan, but an asset-protection and execution plan.

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Implementation Advantage

The combination of Runcible, Oversing, protocol training, and business-transformation experience creates a durable implementation advantage.

• Runcible qualifies AI-mediated work.
• Oversing organizes institutional work.
• The protocol team converts domain knowledge into operational tests.
• The enterprise team packages the system for customers, integrators, consultants, support, training, and implementation.

Once implemented, the system becomes difficult to dislodge because it embeds itself into:

• roles
• workflows
• records
• protocols
• permissions
• accountability structures
• audit trails
• customer training
• institutional memory

This is not merely software adoption.

It is institutional operating-system adoption for AI-mediated work.

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Closing Statement

Runcible’s team is built for the transition from AI assistance to governed institutional action.

The founder-method team created the adjudication framework.

The enterprise team knows how to scale companies, products, operations, customers, and exits.

The domain team connects the method to medicine, law, compliance, behavioral science, and institutional systems.

The protocol team converts the method into reusable, testable, falsifiable, and deployable institutional machinery.

Together, the team is structured to build the adjudication layer for the AI age.

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Runcible is the current commercial expression of a decades-long strategy: make law, judgment, and institutional action computable; build the platform to institutionalize it; then use AI to apply it at scale.

From Computable Law to Institutional AI

Runcible did not begin as an AI product. It began as a long-term strategy for legal and institutional reform.

The original target was not chat, productivity, automation, or even artificial intelligence by itself. The target was law: how to make legal reasoning, institutional judgment, and public decision-making more truthful, more testable, more reciprocal, and more adaptive to reality.

That required a harder problem first: making law computable.

Law is not merely a set of rules. It is a system for deciding claims, evidence, authority, responsibility, liability, and permissible action. To make law computable, it was necessary to reduce truth, reciprocity, possibility, authority, and liability into operational terms that could be tested, recorded, challenged, revised, and applied across institutions.

That work became the foundation of Runcible.

The Original Problem: Law Could Not Adapt Fast Enough

Across business, politics, law, and government, the same failure appeared repeatedly: institutions could not measure reality fast enough, decide truthfully enough, or adapt lawfully enough.

Organizations preserved procedures but lost judgment. Governments accumulated rules but lost contact with reality. Public discourse rewarded persuasion over truth. Institutions acted without sufficient evidence, authority, reciprocity, or liability accounting.

The problem was not merely political. It was epistemic, legal, and institutional.

If law could not become more computable, institutions would continue to rely on discretion, rhetoric, ideology, delay, and conflict. Reform required a better grammar of judgment.

Why Research Came First

The first requirement was research.

Before a platform could be built, the underlying science had to exist. Philosophy, law, morality, economics, and cooperation had to be reduced into operational and testable first principles. Claims had to become testable. Duties had to become explicit. Authority had to become bounded. Liability had to become recordable. Judgment had to become more decidable.

That work could not easily be done inside normal academic structures. It was adversarial, cross-disciplinary, long-horizon, and institutionally inconvenient. So Curt Doolittle built and exited companies in order to fund the research independently.

In 2012, that research was formalized through the Natural Law Institute.

Natural Law Institute: The Research Arm

The Natural Law Institute was created to produce the science.

Its purpose was to reduce law, philosophy, morality, economics, and cooperation into a science of decidability: a method for determining whether a claim, action, policy, argument, or institutional decision can be tested, bounded, warranted, and acted upon.

The output of NLI is the research corpus: books, definitions, protocols, reductions, and first principles.

This was the sunk cost that made Runcible possible. The difficult work was not simply software development. The difficult work was reducing the problem of legal and institutional judgment into something that software could eventually execute.

Why a Platform Was Necessary

Research was necessary, but research alone could not change institutional behavior.

Legal reform requires institutions. Institutions require workflows. Workflows require roles, permissions, state, records, evidence, review, escalation, and accountability. A theory of computable law had to be embodied in software capable of operating inside organizations.

That led to Reality by Chanting and Oversing.

Reality by Chanting / Oversing: The Institutional Platform

Reality by Chanting was created to build the application platform for institutionalizing the research.

That work became Oversing: an institutional software platform for management, projects, workflows, collaboration, memory, approvals, records, accounting, and governed execution. Oversing was designed as the work surface where human and machine participants could act inside institutional roles rather than isolated conversations or disconnected documents.

Oversing supplied the institutional environment. But it still needed an intelligence layer capable of participating in that environment under rules.

Why AI Was Necessary

Computable law also required intelligence.

Institutions operate in language. Legal reasoning, policy interpretation, compliance review, claims analysis, medical authorization, procurement, audit, and public administration all depend on language. Rules are linguistic. Evidence is linguistic. Authority is linguistic. Records are linguistic. Disputes are linguistic.

Earlier software could store records and execute workflows, but it could not yet process institutional language with enough semantic range. The missing piece was machine intelligence capable of generating, interpreting, comparing, and revising candidate language at scale.

Curt’s early work in the 1980s on episodic memory reductions and sequence-based inference pointed toward this requirement, but the hardware and architectures were not ready. The transformer revolution changed the timing. After Attention Is All You Need and the practical arrival of GPT-4-class systems, the missing infrastructure finally existed.

But foundation models introduced a new problem: they could generate language, but they could not qualify it for institutional action.

That became Runcible’s opening.

Runcible: The AI Qualification Layer

Runcible AI Inc. was created to connect the research and the platform to modern foundation models.

Foundation models generate candidate language. Institutions cannot act on candidate language until it is qualified. Runcible supplies the qualification layer: the semantic compiler, protocol runtime, adjudication system, and Decidability Record engine that determines whether AI-mediated work can become admissible institutional action.

Runcible does not compete to build the largest model. It makes model output institutionally usable.

It translates language into operational claims, tests those claims under universal and institutional protocols, emits diagnostics, supports revision, and records what qualified, failed, escalated, or remained undecidable.

This is how the long arc closes:

Research made decidability explicit.
Oversing made institutional execution possible.
Runcible makes AI participation qualified.

The Three Organizations

The three organizations exist because the problem required three different functions.

Natural Law Institute produces the research: the science of decidability, testifiability, reciprocity, authority, and liability.

Reality by Chanting / Oversing produces the platform: the institutional work surface where roles, workflows, state, records, approvals, and memory can be managed.

Runcible AI Inc. produces the intelligence and qualification layer: the system that determines whether AI-mediated work can be tested, bounded, authorized, recorded, and admitted into institutional action.

The sequence is not accidental.

Research → Platform → AI Qualification → Institutional Reform

The Strategic Arc

The strategy has evolved with knowledge and technology, but the target has remained consistent.

In the 1980s, the intelligence problem was visible but hardware was insufficient.

In the 1990s and 2000s, business and government failures made the institutional problem unavoidable.

In 2012, NLI began the research required to make law and cooperation computable.

In parallel, RBC/Oversing began building the institutional software platform required to operationalize that work.

In the 2020s, foundation models made semantic generation abundant.

Runcible now supplies the missing layer: qualification.

Why Runcible Matters

The purpose is not merely better AI.

The purpose is to change institutional behavior by giving organizations a way to act through truth, evidence, authority, reciprocity, records, and liability rather than through unbounded discretion, persuasion, delay, or institutional memory loss.

Law, governance, business, healthcare, finance, insurance, defense, and public administration all depend on the same underlying function: deciding what may be acted upon.

Runcible exists to make that function computable.

Closing

Runcible is the current commercial expression of a forty-year strategy: make law computable, build the platform to institutionalize it, and use AI to scale truthful, reviewable, liability-bounded institutional action.