Reasoning Engine

Go beyond pattern recognition to true understanding. Our reasoning engine combines neural networks with symbolic logic, causal inference, and multi-step reasoning. Solve complex problems that require planning, explanation, and logical deduction. AI that doesn't just predict—it thinks.

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Core Capabilities

Logical Reasoning

Perform deductive, inductive, and abductive reasoning over structured knowledge. Answer complex queries requiring multi-hop inference. Identify logical inconsistencies and contradictions automatically.

Causal Inference

Understand cause-and-effect relationships beyond correlations. Build causal graphs from observational data. Answer counterfactual questions: "What would have happened if...?"

Planning & Strategy

Decompose high-level goals into executable action sequences. Handle partial observability and uncertainty. Generate contingency plans when primary strategies fail.

Analogical Reasoning

Transfer knowledge across domains by identifying structural similarities. Solve novel problems using analogies to familiar situations. Abstract patterns and principles.

Mathematical Reasoning

Solve mathematical word problems, prove theorems, and verify solutions. Symbolic manipulation of equations. Support for algebra, calculus, linear algebra, and discrete math.

Explainability

Every decision comes with a human-readable explanation. Trace reasoning chains from evidence to conclusions. Identify which facts were critical to each inference.

Hybrid Architecture

[VIDEO: Interactive diagram showing neural-symbolic integration - knowledge graph, reasoning paths, and neural inference]

Knowledge Representation

Semantic networks, ontologies, and knowledge graphs. Supports RDF, OWL, and custom schemas. Multi-modal knowledge spanning text, images, and structured data.

Neural Foundation

Large language models for natural language understanding. Vision transformers for visual reasoning. Learned embeddings bridge symbolic and continuous representations.

Symbolic Reasoning

First-order logic, description logic, and probabilistic logic programming. Rule-based inference with non-monotonic reasoning. Constraint satisfaction and SAT solving.

Causal Models

Structural causal models (SCMs) for intervention and counterfactuals. Causal discovery algorithms extract causal graphs from data. Do-calculus for causal inference queries.

Planning Engine

Hierarchical task networks (HTN), PDDL planning, and Monte Carlo tree search. Goal decomposition and task scheduling. Handles resource constraints and temporal logic.

Uncertainty Quantification

Probabilistic reasoning over uncertain knowledge. Bayesian inference and belief propagation. Confidence scores for every conclusion.

Reasoning Modes

Deductive Reasoning

Draw guaranteed conclusions from known facts using logical rules. If all premises are true, the conclusion must be true. Essential for mathematical proofs, legal reasoning, and formal verification.

Modus ponens, modus tollens
Universal and existential quantification
Rule chaining (forward and backward)
Proof by contradiction

[IMG: Logical inference tree showing premises, rules, and conclusion]

Abductive Reasoning

Infer the most likely explanation for observations. Given effects, determine probable causes. Critical for diagnosis, troubleshooting, and scientific hypothesis generation.

Inference to best explanation
Diagnostic reasoning
Hypothesis generation and ranking
Bayesian abduction

[IMG: Diagnostic tree with symptoms leading to multiple possible cause hypotheses]

Temporal Reasoning

Reason about events, durations, and sequences in time. Handle constraints like "before," "during," "overlaps." Essential for planning, scheduling, and understanding narratives.

Allen's interval algebra
Temporal constraint satisfaction
Event calculus
Timeline synchronization

[IMG: Timeline visualization showing temporal relationships between events]

Common Sense Reasoning

Apply everyday knowledge about the physical world, human behavior, and social norms. Understand implicit context that humans take for granted. Powered by large-scale commonsense knowledge bases.

Physical intuition (gravity, solidity)
Social norms and expectations
Typical scenarios and scripts
Default reasoning with exceptions

[IMG: Commonsense reasoning scenario showing typical event sequence]

Real-World Applications

Medical Diagnosis

Analyze patient symptoms, medical history, and test results to suggest diagnoses. Reason over medical ontologies and clinical guidelines. Explain diagnostic reasoning to physicians. Reduces diagnostic errors by 40%.

Legal Analysis

Analyze legal documents, precedents, and statutes. Identify relevant case law for new situations. Logical reasoning over complex regulatory requirements. Contract analysis and risk assessment.

Scientific Research

Generate hypotheses from experimental data. Design experiments to test causal relationships. Literature review and knowledge synthesis. Accelerate discovery by identifying contradictions and gaps.

Fraud Detection

Identify suspicious patterns in financial transactions. Reason about chains of events that indicate fraud. Explain why transactions are flagged. Adapt to new fraud strategies through causal analysis.

Supply Chain Planning

Optimize logistics networks considering constraints and uncertainties. Reason about cascading effects of disruptions. Generate contingency plans for supply chain risks. Multi-objective optimization.

Question Answering

Answer complex questions requiring multi-hop reasoning over knowledge bases. Handle questions about hypothetical scenarios. Provide evidence and reasoning chains supporting answers.

Integration & APIs

REST API

HTTP endpoints for reasoning queries. JSON input/output. Rate-limited per API key. Supports batch requests for efficiency.

Python SDK

Full-featured library with intuitive interface. Type hints and comprehensive documentation. Integrates with pandas and NumPy.

Knowledge Import

Import knowledge from CSV, JSON, RDF/OWL, SQL databases, and APIs. Automatic entity resolution and schema mapping.

Custom Ontologies

Define domain-specific concepts, relations, and rules. Use OWL/RDFS or simplified JSON schema. Version control and collaborative editing.

Webhooks

Real-time notifications when reasoning completes. Push results to your systems automatically. Configurable retry logic.

Enterprise SSO

SAML and OAuth 2.0 support. Role-based access control. Audit logs for compliance.