Legal AI Architecture
Elevate Legal AI with Auditable, Temporal Reasoning
Unlock precision and trustworthiness in high-stakes legal domains by integrating Large Language Models with structured temporal knowledge graphs, ensuring verifiable, time-aware, and causally transparent outcomes.
Transforming Legal Retrieval: From Uncertainty to Precision
Traditional RAG often introduces unreliability in legal contexts due to temporal, structural, and causal blind spots. Our SAT-Graph API provides a neuro-symbolic bridge, enabling deterministic reasoning and full auditability for legal AI systems.
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Reduced Ambiguity in Legal Retrieval
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Improved Temporal Precision
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Traceability of Legal Provenance
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Architectural Foundations
API & Data Models
Causality & Impact
Explore the core design principles and data models that enable deterministic, auditable reasoning within legal AI. This section outlines how stochastic uncertainty is managed.
Probability Isolation Principle: LLM & Graph Interaction
Intent Translation (Probabilistic)
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Initial Semantic Anchoring (Probabilistic)
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Symbolic Graph Execution (Deterministic Core)
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Narrative Synthesis (Probabilistic)
Key Insight
Probability Isolation Confines Stochastic Uncertainty to Bounded Stages| Aspect | Standard RAG (Passive) | SAT-Graph API (Active Agent) |
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| Context Handling |
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| Temporal Awareness |
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| Causal Provenance |
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| Error Visibility |
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Dive into the Canonical Primitive API specification and its core data models. This section details the interface that mediates between LLMs and temporal knowledge graphs.
Key Insight
5 Core Models Item, Version, TextUnit, Action, Relation for flexible modelingAgentic Reasoning with SAT-Graph API
Interpret User Query (LLM)
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Construct Execution Plan (LLM)
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Invoke API Primitives (Deterministic)
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Observe Intermediate Results
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Refine Plan (LLM)
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Synthesize Final Answer (LLM)
Use Case 1: Deterministic Point-in-Time Retrieval
Problem: Retrieve the exact wording of 'Article 6, caput of the Brazilian Constitution' on a specific date (May 20th, 2001). Standard RAG risks returning anachronistic or semantically similar but incorrect versions.
Solution: The agent first uses resolveItemReference (probabilistic) to get the canonical ID. Then, getValidVersions(itemId, at='2001-05-20') (deterministic) retrieves the correct historical version. Finally, getVersionTextUnits(versionId) (deterministic) extracts the precise text.
Impact: Guarantees temporal precision and auditable grounding, ensuring that legal responses reflect the law as it stood at any given point in time.
Understand how the API enables tracing legal provenance, analyzing causal impacts, and navigating complex cross-references within the knowledge graph.
Use Case 2: Causal Pinpointing & Version Comparison
Problem: Identify exact textual differences in a legal provision before and after a specific amendment (e.g., 'right to housing' in Article 6) and trace its authorizing source. Standard RAG struggles to pinpoint causal events and provide auditable diffs.
Solution: The agent utilizes getItemVersions to retrieve the chronological history. It then identifies versionBefore and versionAfter the amendment and hydrates their texts. The producedByActionId from versionAfter links directly to the authorizing Action, which can be retrieved with getActionById.
Impact: Provides a verifiable audit trail of legislative changes, linking textual mutations directly to specific causal events and their provenance, crucial for legal analysis and compliance.
Use Case 2-A: Forward Causality & Cascade Impact Analysis
Problem: Determine which specific provisions of the Brazilian Constitution were altered, added, or revoked by a particular amendment (e.g., Constitutional Amendment No. 26 of Feb 14, 2000). Lexical search is insufficient as affected provisions may not mention the amending norm.
Solution: The agent first resolves the amending norm to an Item ID using resolveItemReference. It then invokes getActionsBySource(sourceWorkId, granularity='micro') to retrieve all micro-actions authorized by the amendment. These actions directly expose producesVersionIds and terminatesVersionIds, allowing the agent to collect and hydrate affected Versions.
Impact: Enables comprehensive, accurate impact analysis of legislative acts across the entire corpus, critical for regulatory monitoring and legal reform assessment.
Advanced ROI Calculator
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Your Journey to Auditable Legal AI
Our structured roadmap ensures a smooth transition to a SAT-Graph API-powered legal AI architecture, from initial assessment to full-scale deployment.
Discovery & Architecture Blueprint
Comprehensive assessment of your current legal data, existing RAG systems, and semantic requirements. We define the initial SAT-Graph schema and API integration points.
Graph Ingestion & API Integration
Secure ingestion of your legal corpus into the SAT-Graph, establishing temporal versions, structural hierarchy, and causal links. Integration of the Canonical Primitive API with your LLM-based agent orchestrator.
Agent Skill Development & Testing
Development of agentic reasoning skills using the API primitives for key legal use cases (e.g., point-in-time retrieval, provenance tracing). Rigorous testing against legal benchmarks.
Deployment, Monitoring & Iteration
Production deployment with real-time monitoring of API calls and agent performance. Continuous refinement of agent planning, graph completeness, and human-in-the-loop validation.
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