A breakthrough in practical reasoning

One architecture for
reasoning through
real-world complexity.

When one rigorous architecture, expressed in natural language, can work across domains without changing its underlying form, the possibilities reach far beyond better arguments. They extend to how we learn, exercise judgment, solve difficult problems, and amplify human capability alongside increasingly intelligent tools.

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RIGOR, MADE PRACTICAL Higher Education / Legal & Professional Practice / Human-AI Reasoning

Grounded in scholarship.
Tested in practice.
Taught in the real world.

Peer-reviewed research

Two Oxford University Press journals

Real-world application

Trial & appellate litigation

Education & training

Law-school teaching, expert-witness training & professional development

01 / The challenge

Complex reasoning needs a
common architecture.

From climate and public-health crises to high-stakes business decisions, scientific disputes, and contested legal questions, real-world problems rarely arrive in one tidy logical form.

They combine evidence and assumptions, uncertainty and competing explanations, multiple lines of support, objections, and inference built upon inference.

Without a common structure, reasoning becomes harder to construct rigorously, examine precisely, teach consistently, and carry from one domain to another.

What becomes possible when the architecture stays constant, even as the problem changes?

02 / Where the work began

Born where
reasoning had to
withstand scrutiny.

From high-stakes legal practice to a universal architecture for real-world reasoning.

The work began in litigation, where arguments had to withstand opposing experts, adversarial challenge, evidentiary scrutiny, and judicial review.

Logic Guaranteed’s founder had taught conventional argument mapping at Lewis & Clark Law School and spent years consulting with Austhink, a leading critical thinking and argument mapping firm. Its limitations became consequential while developing reasoning training for Oregon Department of Revenue professionals responsible for complex market valuations.

But the complexity of the reasoning demanded more.

Investigation revealed one recurring architecture beneath apparently different forms of inference. It was later formalized through peer-reviewed research and translated into the Logic-Bridge™ Method—a visual and embodied way to construct, inspect, and improve reasoning.

The resulting Oregon Department of Revenue reasoning training ran annually for 15 years, drawing waitlists year after year.

Practice revealed the problem. Research uncovered the architecture. Teaching made it usable.

The scholarly foundation

Formally developed as Defeasible Class-Inclusion Transitivity (DCIT), the architecture has been published in two peer-reviewed Oxford University Press journals, with applications including Cardozo Law School and continuing legal education.

Explore the Research ↗

03 / A glimpse of the differenceAn introductory illustration

The bridge between
evidence and conclusion
must be built.

One architecture makes the intervening steps explicit, so the reasoning itself can be examined.

The evidence

Barack was born in Hawaii according to eyewitness Governor Abercrombie.

What connects them?

The conclusion

Barack meets the natural born citizen eligibility requirement of Article II Sec. 1 of the U.S. Constitution to be elected President.

Reveal the missing premises.

DCIT structured-prose outline tracing a hypothetical chain of reasoning about presidential eligibility, with nested ancillary evidence and four highlighted premise groups. — Hypothetical teaching example · Select the image to inspect the full-size outline

Explicit structure does not establish that the premises are true. It exposes the connections—and the gaps—for examination.

04 / Applications

Illustrative university seminar discussion.

Education / Partnership opportunities

Build cross-domain reasoning into the learning itself.

Students are often expected to transfer “critical thinking” from one course, discipline, or problem to another. But transfer is difficult when the underlying reasoning process changes or remains largely implicit.

A universal architecture creates a different possibility.

Students can learn one underlying reasoning structure and repeatedly apply it to fundamentally different real-world problems. The subject matter changes. The evidence changes. The uncertainty and stakes change. The architecture they use to construct, examine, challenge, and improve their reasoning does not.

For the right partner, the opportunity is not simply another critical thinking course. It is a laboratory combining LG’s architecture and methodology with an institution’s expertise and assessment capabilities.

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Illustrative domains

One architecture. Five worlds.

SCIENCE & PUBLIC HEALTH

Evaluate competing explanations from uncertain evidence.

LAW & PUBLIC POLICY

Construct and challenge arguments where evidence, rules, values, and consequences intersect.

BUSINESS & STRATEGY

Make consequential decisions under incomplete information and competing forecasts.

INFORMATION & PUBLIC DISCOURSE

Assess conflicting sources, causal claims, and competing narratives.

TECHNOLOGY & AI

Examine AI-assisted reasoning, locate unsupported steps or assumptions, and determine what deserves confidence.

Different knowledge. Different evidence. Different stakes.
The same underlying architecture.

Illustrative professionals examining a report.

Legal & Professional Practice / Established work

Where the architecture has already been put to the test.

Logic Guaranteed’s longest-running application is in demanding professional settings where reasoning must withstand scrutiny. We help attorneys, expert witnesses, and professional teams construct and test complex evidentiary arguments, expose weaknesses before others do, and communicate the reasoning behind consequential conclusions with greater clarity and rigor.

Our established services include reasoning training, expert-report evaluation, expert-witness examination preparation, and evidentiary argument visualization.

Explore Professional Services ↗

Concept visualization of a future Logic Guaranteed AI interface. — Concept visualization of a future AI-assisted reasoning experience.

Human + AI / Emerging research

What if humans and AI could reason through the same architecture?

As AI becomes more capable, the quality of human-AI collaboration will depend not only on the intelligence of the tool, but on the quality of the reasoning the human brings to it. Framing the right problem, evaluating evidence, exposing assumptions, testing conclusions, and deciding what deserves confidence remain deeply human responsibilities.

Because the Logic-Bridge™ Method represents reasoning through a common, structured natural-language architecture, it raises the possibility of a shared representation layer through which people and intelligent systems could examine the same reasoning.

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That could create a more precise form of AI-supported learning. When the learner and the intelligent system work from the same explicit reasoning architecture, feedback can be tied to the structure the learner actually constructed, not merely to an AI-generated interpretation of free-form prose. The system can identify where the reasoning breaks, which assumption needs support, or which connection needs to be rebuilt while keeping the learner responsible for the intellectual work.

Not AI that does the reasoning for you. AI that helps you become a better reasoner.

And the implications may extend beyond education. A shared reasoning architecture could create new ways for people and intelligent systems to inspect, challenge, refine, and build complex arguments together, potentially improving not only how humans evaluate AI, but what humans and AI are capable of accomplishing together.

We welcome collaboration with institutions and research teams interested in investigating how a shared reasoning architecture could expand what humans and intelligent systems are capable of together.

Discuss a Research Collaboration ↗

The next chapter

The architecture is here.
What becomes possible now?

More than two decades of research, teaching, and real-world application have established the foundation. The opportunity now is to explore what happens when that foundation travels further, across disciplines, institutions, consequential professional environments, and new forms of human-AI collaboration.

We welcome collaborators from universities, professional organizations, research institutions, and technology teams who want to explore a larger possibility: not another critical thinking technique, but a common architecture that could change how reasoning is learned, practiced, examined, applied, and extended across contexts.

Start a Conversation ↗

One architecture for reasoning through real-world complexity.

Complex reasoning needs a common architecture.

Born where reasoning had to withstand scrutiny.

The bridge between evidence and conclusion must be built.