LegalFab Platform - Documentation
Version: 1.2 Last Updated: January 2026
Executive Summary
LegalFab is an AI-powered legal technology platform built on a metadata-driven architecture that provides unified access to distributed data assets while maintaining strict security controls. The platform enables natural language interaction across all components via an intelligent conversational interface, supports configurable automation levels from fully manual to fully automated, and integrates with 200+ data sources, including regulatory registers.
Platform Architecture Components:
| Component | Purpose | Security Relevance |
|---|---|---|
| Knowledge Fabric | Active metadata, knowledge graph, entity resolution, 200+ MCP connectors | Data access control, credential management, source provenance |
| Studio | Agent creation, widgets, datasets, workflow design, operational modes | Execution sandboxing, schema-driven processing, human-in-the-loop controls |
| Dialog | Conversational interface, NLU, multi-level query processing, cross-platform context | Input validation, session security, function routing authorization |
| AI & LLM Layer | LightLLM-based inference, provider agnostic, output consistency, model provenance | Prompt security, schema validation, A/B testing |
| DevOps Infrastructure | CI/CD pipelines, deployment automation | Code security, vulnerability management |
Key Security Characteristics:
- Defense-in-depth architecture with multiple security layers
- Zero-trust access model with continuous verification
- End-to-end encryption for data in transit and at rest
- Comprehensive audit logging with tamper-evident storage
- Privacy-by-design with data minimization principles
- Compliance-ready controls for GDPR, SOC 2, AML regulations, and legal industry standards
- Schema-bounded extraction preventing hallucination and ensuring data quality
- Five operational modes enabling configurable human oversight
Platform Capabilities
Knowledge Fabric
The Knowledge Fabric serves as the foundational data integration and intelligence layer, implementing a metadata-driven architecture that provides unified access while leaving source data in place.
| Capability | Description |
|---|---|
| Persistent Knowledge Graph | Corporate memory with schema-bounded extraction and source provenance |
| Entity Resolution | Cross-source entity matching with golden record management |
| 200+ MCP Connectors | Federated queries across databases, SaaS, legal systems, and corporate registries |
| Two-Way Data Flow | Read from and write back to source systems |
| Search Sessions | Iterative exploration with accumulated context |
| Data Observability | Quality monitoring, freshness tracking, automated alerts |
Studio
The Studio provides the development environment for building AI agents, widgets, datasets, and multi-agent workflows.
| Capability | Description |
|---|---|
| Agent Creation | Domain-driven flow with schema selection, natural language definition, and testing |
| Widgets | Agents with visual interface components (charts, tables, custom views) |
| Datasets | Structured data collections with schema enforcement and access controls |
| Business Domain Discovery | Extract schemas from documents to define entity structures |
| Chain of Agents | Multi-agent orchestration with sequential, parallel, and hierarchical patterns |
| Operational Modes | Five modes from traditional platform (Mode 0) to fully automated with audit (Mode 4) |
| Text-to-Pipeline | Natural language workflow generation with DSL output |
Dialog
The Dialog component provides the central conversational intelligence layer enabling natural language interaction across all platform components.
| Capability | Description |
|---|---|
| Intelligent Routing | Automatically routes queries to Knowledge Fabric, Studio, Marketplace, or Exchange |
| Natural Language Understanding | Intent classification, entity extraction, context analysis |
| Multi-Level Query Processing | Handles complex queries requiring multiple platform components |
| Cross-Platform Continuity | Maintains context between web and messaging applications |
| Long-Term Memory | Preserves conversation history across sessions |
| Document Processing | Handles document uploads within conversations |
| Intelligent Caching | Two-layer caching (exact match + semantic) for performance |
AI & LLM Layer
The AI layer provides secure, provider-agnostic LLM integration with comprehensive monitoring and quality controls.
| Capability | Description |
|---|---|
| LightLLM Gateway | Provider-agnostic interface supporting multiple LLM providers |
| Output Consistency | Schema-validated extraction and ontology-based execution |
| Model Provenance | Tracking of model versions and configurations |
| Quality Assurance | Feedback loops, accuracy monitoring, reasoning chain transparency |
Document Structure
| Document | Content |
|---|---|
| 01-Introduction | Platform overview and security summary |
| 02-Architecture | System architecture, deployment models, security boundaries |
| 03-Knowledge-Fabric | Knowledge graph, entity resolution, MCP connectors, corporate ownership integration |
| 04-Studio | Agent creation, widgets, datasets, operational modes, workflows |
| 05-AI-LLM | LLM security, output consistency, model provenance, quality assurance |
| 06-CI-CD | CI/CD pipeline security |
| 07-Security-Operations | SOC, monitoring, and incident response |
| 08-AML-Compliance | AML rule engine, BPM workflows, screening |
| 09-Schema-Management | Business domain discovery, schema registry |
| 10-Compliance-Capabilities | Platform compliance features, legal hold, retention management |
| 11-API-Security | API gateway, authentication, rate limiting |
| 12-Dialog | Conversational interface, NLU, state machine, context management |
Security Principles
LegalFab implements the following core security principles:
| Principle | Implementation |
|---|---|
| Defense in Depth | Multiple independent security layers across all components |
| Least Privilege | Minimum necessary access granted, permission propagation controls |
| Zero Trust | All requests are authenticated regardless of source, with continuous verification |
| Data Minimization | Only essential data collected; metadata-only architecture leaves source data in place |
| Encryption Everywhere | Data encrypted at rest and in transit with AES-256 and TLS 1.3 |
| Audit Everything | Comprehensive logging with tamper-evident storage and full provenance |
| Schema-Driven Security | All data extraction and processing bounded by user-defined schemas |
| Human-in-the-Loop | Configurable automation levels with escalation and approval workflows |
Operational Modes
The platform supports five operational modes enabling organizations to balance automation with human oversight:
| Mode | Name | Description |
|---|---|---|
| 0 | Traditional Platform | No AI agents; manual investigation and analysis |
| 1 | AI-Assisted Manual | Agents in suggest-only mode; all decisions require human approval |
| 2 | Routine Automation | Agents handle routine tasks; humans focus on analysis and decisions |
| 3 | Autonomous with Escalation | Full investigation automation; escalation on exceptions |
| 4 | Fully Automated with Audit | End-to-end automation; post-investigation human audits |
Regulatory Compliance
The platform is designed to support compliance with:
| Regulation | Coverage |
|---|---|
| GDPR | Data subject rights, consent management, data minimization |
| CCPA/CPRA | California privacy requirements |
| Money Laundering Regulations 2017 | Independent verification, ownership analysis, record retention |
| SOC 2 | Security, availability, processing integrity |
| FCA Requirements | Financial services regulatory compliance |
| Legal Industry Standards | Matter confidentiality, conflict checking, privilege protection |
Contact
For security inquiries or to request additional documentation, please contact the LegalFab security team through your account representative.