Reinforcement Learning

Trucast: Graph RAG in Reverse

Executive Summary

Trucast represents a shift in how AI systems understand and operate within business contexts. While traditional Graph RAG (Retrieval-Augmented Generation) systems attempt to build knowledge graphs from unstructured data, Trucast operates in reverse:

Pre-validated ontologies from real-world sources (10-Ks, GICS, industry standards)

Crystallized into JSON schemas that represent verifiable business activities

Operationalized through protocols that guide LLM interactions with precision

The Core Innovation: Reversing the Flow

Traditional Graph RAG:

Unstructured Data → AI Processing → Knowledge Graph → Query → Response
                    (uncertain)       (emergent)              (variable quality)

Trucast's Approach:

Real-World Validation → JSON Schemas → Protocol Assembly → LLM Guidance → Precise Response
(10-Ks, GICS, etc.)    (crystallized)  (deterministic)     (contextual)   (business-aligned)

Driver-Blueprint Architecture

Drivers: The Actor-Action Pairs

A Driver represents the fundamental unit of business activity:

Driver = Actor + Action + Intent

Examples:

  • wealth-advisor + personalize-content = "Create personalized investment content"

  • it-admin + integrate-system = "Connect core banking systems"

  • sales-rep + qualify-lead = "Assess prospect fit"

Blueprints: The Business Model Archetypes

A Blueprint represents a validated business model archetype derived from real-world analysis:

Blueprint = {
  GICS Alignment,      // Industry classification
  Business Model,      // Revenue streams, key activities
  Entity Schemas,      // JSON validation schemas
  Operational Metrics  // KPIs from actual reports
}

The Connection: Driver → Blueprint Mapping

Each Driver maps to a specific Blueprint based on real-world business logic:

wealth-advisor:personalize-content → Wealth Management Blueprint (GICS 4030)
it-admin:integrate-system → Financial Infrastructure Blueprint (GICS 4510)
sales-rep:qualify-lead → B2B Sales Blueprint (GICS 2020)

Common Data Logical Manifests

When a Driver activates within a Blueprint context, Trucast generates a Common Data Logical Manifest:

{
  "driver": {
    "actor": {
      "type": "wealth-advisor",
      "industryRole": "Registered Investment Advisor (RIA)",
      "certifications": ["CFP", "CFA"]
    },
    "action": {
      "type": "personalize-content",
      "operationalActivity": "Client Advisory"
    }
  },
  "blueprint": {
    "id": "wealth-management",
    "gicsAlignment": "4030 - Capital Markets",
    "activeSchemas": {
      "ClientDataSchema": { /* validated from FINRA/SEC */ },
      "PortfolioSchema": { /* industry standards */ }
    }
  },
  "operationalContext": {
    "regulatoryConstraints": ["SEC Rule 206(4)-7", "Reg BI"],
    "complianceChecks": ["KYC Verification", "Suitability Assessment"]
  },
  "llmContext": {
    "domainVocabulary": ["AUM", "fiduciary", "risk-adjusted returns"],
    "businessRules": [
      "Always verify client risk tolerance",
      "Document rationale for all advice"
    ]
  }
}

Real-World Validation Sources

1. 10-K/10-Q Analysis

  • Revenue models

  • Operational activities

  • Risk factors

  • Compliance requirements

2. GICS Classification

  • Industry hierarchies

  • Standard business models

  • Peer comparisons

3. Industry Standards

  • FINRA regulations

  • SEC requirements

  • ISO standards

  • Industry best practices

4. Operational Disclosures

  • Business process documentation

  • KPI definitions

  • Success metrics

How It Powers LLM Interactions

1. Context Injection

The manifest provides rich, validated context to the LLM:

Base Prompt: "You are an AI assistant..."
+ Driver Context: "Assisting an RIA with Client Advisory"
+ Blueprint Context: "Operating in Wealth Management (GICS 4030)"
+ Schema Context: "Validate against ClientDataSchema"
+ Compliance Context: "Apply SEC Rule 206(4)-7"
= Precisely Contextualized Prompt

2. Validation Framework

Every interaction is validated against real-world constraints:

  • Data conforms to industry schemas

  • Actions align with operational activities

  • Outputs meet compliance requirements

3. Success Patterns

LLMs are guided by proven patterns from industry analysis:

  • "Reference specific client goals" (from successful advisor practices)

  • "Include risk disclaimers" (from compliance requirements)

  • "Use industry-standard metrics" (from 10-K reports)

Implementation Example: Wealth Advisor Workflow

Step 1: Driver Activation

const driver: Driver = {
  actor: { type: 'wealth-advisor', attributes: { firmType: 'RIA' } },
  action: { type: 'personalize-content', intent: 'quarterly-review' }
}

Step 2: Blueprint Resolution

const blueprint = await driverBlueprintEngine.resolveDriverToBlueprint(driver)
// Returns: Wealth Management Blueprint with GICS 4030 alignment

Step 3: Manifest Generation

const { manifest } = await driverBlueprintEngine.generateManifest(
  driver, 
  blueprint, 
  { clientContext: 'Ms. Jones, retirement focus' }
)

Step 4: LLM Enhancement

const enhancedPrompt = await driverBlueprintEngine.enhancePromptWithManifest(
  basePrompt, 
  manifest
)
// LLM now has full business context, schemas, and compliance rules

Benefits of This Approach

1. Accuracy

  • Grounded in real-world data

  • Validated against industry standards

  • No hallucination on critical business facts

2. Compliance

  • Built-in regulatory awareness

  • Automatic compliance checks

  • Audit trail generation

3. Efficiency

  • Pre-validated schemas reduce errors

  • Reusable patterns accelerate development

  • Industry alignment speeds adoption

4. Scalability

  • New blueprints can be added systematically

  • Drivers can be extended without breaking existing flows

  • Manifests provide consistent interfaces

Future Vision

1. Automated Blueprint Discovery

  • Analyze new 10-Ks to identify emerging business models

  • Generate blueprints from regulatory filings

  • Continuous validation against market changes

2. Cross-Blueprint Orchestration

  • Workflows that span multiple blueprints

  • Inter-industry integration patterns

  • Supply chain optimization

3. Industry-Specific LLMs

  • Fine-tuned models for each blueprint

  • Specialized vocabulary and patterns

  • Compliance-aware generation

Conclusion

Trucast's "Graph RAG in Reverse" approach fundamentally changes how AI systems understand and operate within business contexts. By starting with validated, real-world ontologies and crystallizing them into operational JSON schemas, we create a system where:

  1. Every interaction is grounded in real business logic

  2. Compliance is built-in, not bolted-on

  3. LLMs become true business partners, not just text generators

  4. Workflows align with actual business operations, not theoretical models

This is not just a technical architecture - it's a new way of thinking about how AI and business operations converge, creating delightful and impactful experiences for business users while maintaining the rigor and compliance requirements of enterprise systems.

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