Role-Based AI
Decision Support.
Identity-aware AI with RBAC-filtered outputs, citation-backed responses, and complete audit trails. Every answer is provable.
AI That Knows Who Is Asking
Most AI systems treat every user identically. Ask a question, get an answer — regardless of who you are, what you're authorized to see, or whether the answer can be proven. That works for consumer chatbots. It does not work for organizations where information access is governed by role, clearance, jurisdiction, or regulatory mandate.
Ironwraith is AI that understands identity context before generating a single token. Every response is filtered through role-based access control at the output layer — not just the data layer. A department head and a field worker asking the same question receive appropriately scoped answers. Citations trace every claim back to its source document. And an immutable audit log captures who asked, what was retrieved, and what the AI responded — for every interaction, without exception.
This is a condensed overview of Ironwraith's architecture. For complete technical details including the six-layer intelligence architecture, Nerve interface design, compliance framework alignment, and federal AI market context, see our AI Capabilities page.
Query to Verified Answer in Four Stages
Every interaction passes through identity verification, knowledge retrieval, output filtering, and audit logging before reaching the user.
Technical Capabilities
Six subsystems forming a complete auditable AI stack. Built for environments where every answer must be provable and every interaction must be logged.
Identity-Aware Context Injection
Every AI interaction begins with identity context: the user's role, organization, jurisdiction, and authorization level. Context is injected into the AI pipeline before any retrieval or generation occurs — ensuring the system understands who is asking before deciding what to answer.
Knowledge Retrieval with Citations
Retrieval-augmented generation grounds every response in authoritative source documents. Each claim links to the specific document, section, and version that supports it. Auditors can verify any output without reverse-engineering the AI's reasoning.
Persistent Cross-Session Memory
Ironwraith maintains memory across sessions — not just within a single conversation. Previous interactions, decisions, and context carry forward. The AI synthesizes past sessions to provide increasingly relevant responses without requiring users to re-explain operational context.
RBAC-Filtered Output
Role-based access control applied at the AI output layer — the last line of defense. Even if underlying data is accessible to the retrieval pipeline, the output filter ensures only information appropriate to the user's authorization level reaches them. Data isolation per response, not just per query.
User Correction Pipeline
Authorized subject matter experts flag incorrect outputs and provide corrections within the interface. Corrections are validated and integrated into the knowledge base — a continuous improvement loop. OMB M-26-04 explicitly requires this mechanism for federal AI systems.
Immutable Audit Log
Every interaction logged: who asked, what was retrieved, what the AI responded, which model version, what confidence level, and when. Append-only and tamper-evident. Compliance officers can reconstruct any AI decision chain from the audit trail alone.
System Architecture
Five layers from raw input to auditable output. Every query traverses the full stack — no shortcuts, no bypasses.
Input Layer
User query interface, document upload pipeline, API trigger endpoints. Multi-modal input supporting text, PDFs, structured data, and conversational queries.
Retrieval Layer
RAG pipeline with vector search, intelligent document chunking, contextual re-ranking. Retrieves the most relevant source material for every query.
Inference Layer
LLM with tool use capabilities, citation extraction, confidence scoring. Every response grounded in retrieved source material with traceable citations.
Governance Layer
RBAC-filtered output, comprehensive audit logging, bias monitoring, confidence thresholds. Users only see responses derived from data their role permits.
Output Layer
Cited responses with source links, human correction pipeline, feedback loop for continuous improvement. Every output auditable and correctable.
AI Governance Alignment
Architecture aligned to federal AI governance frameworks. Every design decision traces back to a specific regulatory requirement.
| Framework | Requirement | Implementation | Status |
|---|---|---|---|
| EO 14110 | Safe AI | Full audit trail on every inference, bias monitoring pipeline, human oversight enforcement | Aligned |
| NIST AI RMF | Risk Management | Risk categorization, impact assessment, continuous monitoring of model outputs | Aligned |
| OMB M-24-10 | Responsible AI | Transparency reporting, appeal mechanisms, human-in-the-loop for consequential decisions | Aligned |
| DoD RAI Principles | Traceable & Governable | Citation-backed responses, role-based output filtering, correction pipeline | Aligned |
| NIST 800-171 3.1 | Access Control | RBAC enforcement at inference layer — users receive only responses derived from authorized data | Aligned |
System Metrics
Measurable design targets, not marketing claims. Every metric is architecturally enforced.
Every AI response is designed to include traceable source citations. Hallucination mitigation enforced at the architecture level.
Data isolation enforced at the retrieval layer. Users only see responses derived from data their role permits.
Complete query-to-response logging — input, retrieval context, inference output, user feedback. Forensic-grade.
Users flag, correct, and improve responses. Every correction feeds back into retrieval quality.
Platform Ecosystem
IRONWRAITH draws intelligence from VERDANDI's collection pipelines, spatial context from SPECULUM's 3D models, and threat data from HELL HOUND's detection engines. Field operators interact through SILTWIRE, communications flow via TESSERA, and everything runs on FORGE platform architecture.
Complete AI Architecture Details
This page provides a capability overview. For the full six-layer intelligence architecture, Nerve interface design, compliance alignment across 10 federal mandates, and federal AI market analysis — see the dedicated AI page.
View Full AI ArchitectureApplicable Domains
Ironwraith adapts to any domain where AI decision support must respect authorization boundaries and prove its reasoning.
Discuss AI Decision Support
Architecture review with the engineers who built it. No sales deck. No demo theater.