Applications

Verified formal reasoning transforms decision-making in every domain where correctness matters.

Healthcare

Medical Treatment Planning

A physician evaluating treatment options for a complex patient needs to reason about counterfactual outcomes: what would happen if treatment A is chosen versus treatment B.

How the Logos Helps

The Logos formal system represents the patient state, available treatments, and their effects using counterfactual and causal operators. The Proof-Checker verifies that the reasoning chain from symptoms to treatment recommendation is valid. The Model-Checker searches for counterexamples -- scenarios where the recommended treatment could lead to adverse outcomes.

Result: Provides auditable reasoning with proof receipts that regulators and ethics boards can verify independently, rather than relying on opaque neural network outputs.

Legal

Legal Evidence Analysis

A legal team analyzing a complex case needs to track chains of evidence, evaluate witness credibility, and determine whether obligations and permissions were upheld.

How the Logos Helps

Using epistemic operators (belief, probability) and normative operators (obligation, permission), the Logos formalizes the legal reasoning required. The Model-Checker validates whether the evidence supports the legal conclusions, or produces countermodels showing scenarios consistent with the evidence but leading to different conclusions.

Result: Provides formally verified legal reasoning that can be presented to courts, with every step auditable and every inference backed by a proof receipt or exposed by a counterexample.

Financial

Risk Analysis & Compliance

A financial institution must verify that trading algorithms comply with risk limits and regulatory requirements across diverse market conditions.

How the Logos Helps

The Logos formalizes compliance rules using normative operators (obligation, prohibition) and represents risk exposure through counterfactual scenarios. The Proof-Checker verifies that risk management invariants hold across all execution paths. The Model-Checker identifies edge cases where the algorithm might violate compliance constraints.

Result: Delivers mathematically verified compliance with formal proof receipts for regulators, eliminating uncertainty about whether risk limits are truly respected in all market conditions.

Autonomous Systems

Autonomous Vehicle Planning

A self-driving vehicle must plan routes while reasoning about counterfactual outcomes: what happens if another vehicle swerves, or if traffic conditions change unexpectedly.

How the Logos Helps

Using temporal and counterfactual operators, the Logos represents planning scenarios and their branching possible futures. The Proof-Checker verifies that safety constraints are maintained throughout the plan. The Model-Checker searches for adversarial scenarios where the plan might lead to unsafe outcomes.

Result: Provides formal safety guarantees for autonomous decision-making with verifiable proof receipts that demonstrate robustness before deployment on public roads.

Multi-Agent Systems

Multi-Agent Coordination

Multiple AI agents operating in a shared environment need to reason about each others' beliefs, intentions, and commitments to coordinate safely.

How the Logos Helps

The agent-dependent extensions (agential operators, social operators, reflection) allow each agent to formally represent other agents' mental states. The Proof-Checker verifies that coordination protocols maintain safety invariants. The Model-Checker searches for scenarios where agents' commitments could conflict.

Result: Enables mathematically verified multi-agent coordination where safety properties are guaranteed rather than hoped for, with counterexamples flagging potential conflicts before deployment.

Scientific Research

Hypothesis Generation & Testing

A research team needs to generate novel hypotheses from observed data and verify them against competing explanations using rigorous logical analysis.

How the Logos Helps

The Logos uses abductive operators to generate hypotheses explaining observed phenomena, then applies inductive verification to test predictions. The Proof-Checker validates the logical structure of arguments from evidence to conclusions. The Model-Checker identifies alternative hypotheses consistent with the data.

Result: Supports formal scientific reasoning with verifiable proof receipts for every inference, enabling reproducible research with mathematically rigorous hypothesis evaluation.

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We are looking for domain partners to co-develop verified reasoning solutions for specific industries.

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