CTGT × Oracle | Enterprise AI Governance Partnership
Enterprise AI Governance

Scaling AI Governance
with Deterministic
Policy Intelligence

A strategic framework for Oracle's Responsible AI initiative. CTGT's production-proven policy graph delivers auditable, compliant AI at scale through feature-level intervention and graph-based verification.

Document Partnership Proposal
Date January 2026
Classification Confidential
Executive Summary

Three Outcomes That Matter

89%
Remediation Accuracy
Policy Violation Auto-Correction
Active remediation, not just detection. Our policy engine rewrites non-compliant outputs while preserving intent. Validated against 35,000+ FINRA rules.
20ms
P90 Retrieval Latency
Production-Ready Performance
Sub-100ms synchronous processing across 25,000+ policies. No perceptible user delay. Scales horizontally with Kubernetes orchestration.
96%
HaluEval Score
Frontier-Level Accuracy
Our policy engine elevates open-source models to match or exceed frontier performance on HaluEval benchmarks through feature-level intervention.
The Opportunity

From Guardrails to Governance

Traditional guardrail solutions detect problems. CTGT resolves them with deterministic policy logic that handles the complexity Oracle's customers demand.

Current State

The Guardrail Gap

Existing solutions offer binary detection: toxic/not toxic, compliant/non-compliant. Enterprise policies are nested, contradictory, and context-dependent.

  • Binary guardrails struggle with 45,000+ policy conflicts
  • No remediation, only blocking. Users left stranded
  • Adversarial attacks bypass static rules easily
  • Models like Grok and Gemini fail on edge cases
  • No audit trail for regulatory defense
CTGT Approach

Intelligent Policy Resolution

A graph-based policy engine that understands hierarchy, resolves collisions deterministically, and actively remediates violations while preserving user intent.

  • Neo4j-backed policy graph with deterministic collision resolution
  • 89.2% auto-remediation accuracy: fix, don't just flag
  • Feature-level intervention for open-weight models
  • Model-agnostic: works with any LLM provider
  • Complete audit trail for every policy decision
Technical Alignment

Built on the Same Foundation

Oracle's RAI team is exploring knowledge graphs for policy management. CTGT has already productionized this approach at scale.

Oracle RAI Vision
Where you're heading
  • Knowledge graph for policy selection
  • Hybrid traditional ML + LLM approach
  • Handling nested, contradictory policies
  • Feature-level control for gov contracts
  • On-premise deployment requirement
CTGT Production Reality
Already deployed at scale
  • Neo4j policy graph with 45,000+ rules in production
  • Graph verification + feature-level intervention
  • Collision engine with criticality-weighted resolution
  • Activation-level steering for open-weight models
  • Fully on-prem deployment available (SOC-2 compliant)
Validated Performance

Benchmarks That Matter

Tested against 35,000+ FINRA rules. Validated by G-SIB institutions. Production metrics from enterprise deployments.

89.2%
Policy remediation accuracy
(auto-correction of violations)
20ms
P90 policy retrieval
(across 25,000 policies)
96.5%
HaluEval accuracy
(exceeds Claude 4.5 Opus baseline)
45K+
Policies managed
(with collision resolution)
Deployment Flexibility

Your Cloud, Your Terms

We understand Oracle's requirements: no data leaving the cloud, no third-party dependencies. CTGT deploys entirely within your environment.

Multi-Tenant SaaS
Fastest deployment path. Shared infrastructure with complete data isolation. SOC-2 Type II certified.
  • Deploy in hours, not weeks
  • Automatic updates and scaling
  • 99.9% uptime SLA
  • TLS 1.3 + AES-256 encryption
Single-Tenant VPC
Complete logical isolation within Oracle Cloud. Dedicated compute with no shared resources.
  • Runs in Oracle Cloud Infrastructure
  • Dedicated network isolation
  • Custom security policies
  • Data never leaves your VPC
Full On-Premise
Air-gapped deployment for the most sensitive environments. No external network dependencies.
  • Docker container delivery
  • Zero internet connectivity required
  • Government/ITAR compatible
  • SDK integration available
Proven at Scale

InsurTech Case Study

"It didn't just identify that there was a hallucination, it also showed that the hallucination stemmed from our own prompt. To me, that was a gamechanger."
Jonathan Sims Head of Data & Analytics, Now Insurance
Inc. 5000 #9 Fastest-Growing Insurance Company
Root Cause
Traced hallucinations to prompt template errors. Debugging the entire AI stack, not just outputs
External Deploy
Enabled transition from internal-only LLM tools to production customer-facing applications
Real-Time
Policy engine runs inline. No batch processing delays, no degraded user experience
Path Forward

From Pilot to Production

A structured engagement that proves value fast, then scales systematically across Oracle's product portfolio.

1
Technical Deep-Dive
Week 1-2
Architecture review with Oracle RAI engineering. Align on graph schema, deployment model, and integration points.
2
Proof of Concept
Week 3-6
Deploy on a single use case: HCM agents, hiring automation, or customer support. Benchmark against current guardrails.
3
Production Pilot
Week 7-12
Scale to production workload. Ingest Oracle-specific policy sets. Validate ROI metrics and compliance improvements.
4
Platform Integration
Ongoing
Embed CTGT as Oracle's AI governance layer. Enable as a feature for Oracle Cloud customers alongside model access.

Let's build AI governance
that actually works.

Ready for a technical deep-dive? We can have a working demo in your environment within days, not months.

Schedule Technical Review View Full Platform
James Connolly
Head of Growth, CTGT
james@ctgt.ai