The Strategic Context
Two forces, one deadline
Operational risk teams at global custodians face a structural tension. The board wants AI-driven efficiency. Regulators demand explainability and control. These pressures are accelerating simultaneously, and the governance infrastructure to reconcile them does not yet exist at most institutions.
The Institutional Mandate
Deploy AI at Scale
McKinsey estimates $4.4 trillion in potential value from generative AI. Boards are setting aggressive timelines for adoption. Saying "no" indefinitely is no longer viable. The expectation is that risk and compliance teams enable, not block, this transition.
The Operational Reality
AI Outputs Cannot Be Trusted
Baseline large language models hallucinate over 50% of the time in constrained policy environments. Without a deterministic governance layer, every AI-generated communication or decision carries unquantified regulatory risk. 95% of enterprise AI pilots stall as a result.
The missing piece is not another model or another dataset. It is an independent governance layer that sits between AI output and the end user, enforcing your policies deterministically and producing a defensible audit trail for every decision. This is the layer CTGT provides.
Architecture
Where governance sits in the AI stack
Enterprise AI is a layered architecture where each level serves a distinct function and carries its own cost structure. Understanding this stack is essential for budget justification and vendor selection. CTGT occupies the governance and enforcement layer: the critical control point between raw model output and production use.
Layer 06End Users
Application Interface
The user-facing surface: chatbots, agent workflows, communication tools, decision-support dashboards. This is the "one button" that senior management sees. Everything below is invisible to them.
ChatbotsEmail ToolsAgent WorkflowsInternal Apps
Layer 05Governance
Policy Enforcement and Audit (CTGT)
The deterministic governance layer. Translates regulatory documents, SOPs, and risk frameworks into enforceable policy graphs. Every AI output is evaluated against these policies in real time. Non-compliant content is flagged or remediated before it reaches the end user. Produces a complete, defensible audit trail.
Policy-as-CodeReal-Time RemediationAudit TrailHallucination DetectionModel-Agnostic
This is the layer that makes AI auditable for operational risk. Without it, every downstream output is ungoverned.
Layer 04Orchestration
Prompt Management and Retrieval (RAG)
Retrieval-augmented generation pipelines, prompt templates, and context injection. Useful for grounding model responses in organizational data, but fundamentally non-deterministic. RAG retrieval can return irrelevant documents, and small prompt changes produce wildly different outputs.
RAG PipelinesPrompt EngineeringVector Search
Layer 03Models
Foundation Models (LLMs)
The generative AI engines: OpenAI, Anthropic, Google, or open-source alternatives. Powerful but probabilistic. They produce compelling text but have no inherent understanding of your policies, regulations, or risk tolerance.
GPT / OpenAIAnthropicGeminiOpen Source
Layer 02Compute
Infrastructure and Hosting
Cloud or on-premise compute that powers model inference. Typically Azure, AWS Bedrock, or GCP Vertex. CTGT deploys on your existing infrastructure with no additional compute procurement required.
Azure / Bedrock / VertexOn-Premises
Layer 01Data
Enterprise Data Sources
Structured and unstructured data: client records, transaction histories, regulatory filings, internal communications. The raw material that AI operates on.
SharePointDatabasesArchivesDocument Stores
The Governance Gap
Why standard approaches fail in regulated environments
Most enterprise AI deployments rely on RAG pipelines and prompt engineering to manage model behavior. In low-stakes applications, these work well enough. In regulated environments where the margin for error is zero, they introduce structural vulnerabilities that are difficult to detect and impossible to audit.
RAG retrieval is non-deterministic: the same question asked twice may pull different supporting documents, producing different answers. Prompt engineering is brittle: a small change in phrasing can fundamentally alter the output. Neither generates a defensible audit trail, and neither can enforce regulatory hierarchy when policies conflict.
CTGT operates at a fundamentally different level. Rather than coercing probabilistic models through input manipulation, the platform enforces compliance on the output using a deterministic policy graph. Every AI-generated statement is evaluated, scored, and if necessary remediated before it reaches any user or system.
| Dimension | RAG + Prompt Engineering | Fine-Tuning | CTGT Policy Engine |
|---|
| Determinism | Non-deterministic. Retrieval varies per query. | Semi-deterministic. Behavior encoded statically. | Fully deterministic. Policy graph enforces consistent outcomes. |
| Audit Trail | None. No traceability from output to policy. | None. Model weights are opaque. | Complete. Every decision traced through the policy graph. |
| Policy Updates | Weeks. New embeddings and testing. | Months. Full retraining cycle. | Minutes. Upload new document, engine auto-ingests. |
| Conflict Resolution | Undefined. No hierarchy when policies conflict. | Undefined. Conflicts baked into weights. | Deterministic. Weighted vector balancing with criticality scoring. |
| Model Dependency | Tightly coupled to model and embeddings. | Locked to a specific model version. | Model-agnostic. OpenAI, Anthropic, Google, open source. |
| Hallucination Control | Partial. Reduces some errors, introduces others. | Partial. Can overfit to training patterns. | Multi-stage verification. 50% baseline reduced to 4% average. |
Validated Performance
Benchmark results across models and tasks
Independent benchmarks demonstrate that the CTGT policy engine consistently improves accuracy and reduces hallucinations across both frontier and open-source models, outperforming RAG pipelines and constitutional AI approaches in every configuration tested.
3.3×
Accuracy Multiplier
GPT-120B-OSS: 21.3% → 70.6%
+49pt
Truthfulness Gain
TruthfulQA misconception benchmark
96.5%
Hallucination Prevention
HaluEval benchmark, GPT-120B-OSS
HaluEval: Hallucination Detection Accuracy
Claude 4.5 Sonnet FRONTIER
TruthfulQA: Misconception Accuracy
Claude 4.5 Sonnet FRONTIER
A key finding for operational risk: on legal reasoning tasks, RAG actually degraded model performance, dropping accuracy to 39%. CTGT's policy engine doubled it to 78%. In high-stakes domains, retrieval pipelines alone are insufficient.
Regulatory Compliance at Scale
FINRA policy enforcement benchmark
To validate performance in financial regulatory environments, CTGT ingested the complete FINRA rulebook, extracting approximately 3,500 granular business rules. The system was tested against 520 synthetically generated compliance violations to measure remediation accuracy and latency at scale.
89.2%
Remediation Accuracy
464 of 520 violations fully remediated in a single pass
~3,500
Granular Policies Extracted
From the complete FINRA rulebook
Policy Ingestion (150-page doc)
P9020s
P9530s
P9945s
Policy Retrieval (~25,000 policies)
P9020ms
P9535ms
P9950ms
End-to-End Remediation
P907.2s
P9512.5s
P9923s
Methodology: Benchmarks on GPT-120B-OSS (quantized mxfp4) served on a single H100 via vLLM. FINRA rules ingested individually from scraped rulebook. Single-pass remediation. Judge model: Gemini 3 Pro Preview. Full methodology available upon request.
Enterprise Deployment
Global systemically important bank deploys CTGT
A Top 5 G-SIB utilizes CTGT's policy engine across its wealth management division to govern AI-assisted client communications. The platform ingests FINRA, SEC, and internal compliance policies, enforcing them in real time across all model-generated outputs.
Policy Adherence
+30%
Improvement in compliance accuracy across AI-generated client communications
Total Cost of Ownership
20–40%
Reduction in engineering TCO through elimination of legacy regex systems and manual review
Time to Policy Update
Minutes
Regulatory changes enforced across all AI systems instantly, replacing multi-week update cycles
The deployment replaced a legacy compliance stack of thousands of regular expressions and classical ML models, some dating to the 1990s, responsible for reviewing over 10 million daily messages. The CTGT governance layer was stood up in 1.5 weeks, enabling the safe transition of LLMs from internal pilots to high-stakes production environments.
Build vs. Buy
The cost of building this layer internally
Building a production-grade governance layer requires deep expertise across several disciplines: information theory for policy extraction, graph-based reasoning for conflict resolution, multi-stage verification for hallucination detection, and real-time enforcement at enterprise scale. This is a research-intensive platform that took CTGT years to develop and validate.
The research foundation is substantial. CTGT's published work on feature-level model intervention demonstrated the ability to identify and modify the specific internal representations responsible for model behavior, without retraining. This peer-reviewed work (HAL Open Science) represents the kind of fundamental capability an internal team would need to replicate.
For a build path, the realistic timeline is 12 to 18 months before a minimally viable governance engine could reach production. The buy path with CTGT compresses that to weeks. The platform deploys on your existing model infrastructure, operates under the principle of least privilege, and integrates with your compliance archiving systems via API.
| Internal Build | CTGT Platform |
|---|
| Time to Production | 12–18 months (optimistic) | 1.5–4 weeks |
| Research Requirement | Information theory, graph reasoning, mechanistic interpretability | Included. Peer-reviewed and production-validated. |
| Ongoing Maintenance | Dedicated team for model drift, policy updates, infrastructure | Managed. Continuous updates to verification pipeline. |
| Infrastructure | New procurement: compute, storage, orchestration | Deploys on existing model instances. On-prem available. |
| Audit Defensibility | Unproven. No regulatory track record. | Validated at G-SIB scale with FINRA/SEC policy environments. |
| Vendor Risk | Not applicable | $7.2M raised. Published research. JP Morgan IMF summit partner. |
Engagement Model
A phased path to enterprise governance
CTGT deployments follow a structured pilot model designed to deliver measurable results within weeks and scale to enterprise-wide governance. This approach de-risks procurement and provides the evidence needed for broader budget approval.
Week 1–2
Architecture Review and Policy Ingestion
Joint session to map your regulatory landscape and identify the highest-impact pilot use case. CTGT ingests relevant policies (SOPs, regulatory documents, risk frameworks) and configures the policy graph. Deployment on your existing model infrastructure.
Week 3–4
Controlled Pilot Deployment
The governance layer activates on a scoped workflow (e.g., AI-assisted communications, agent decision support, or document generation within a single business unit). Metrics collection begins: remediation rate, hallucination detection, policy adherence, and audit trail completeness.
Week 5–6
Results Delivery and Expansion Planning
Quantified results delivered to your leadership team: compliance improvement, cost savings, risk reduction. If the pilot meets success criteria, a roadmap for cross-functional deployment is developed. The governance engine extends naturally across trading, client support, marketing, and other business units.
The governance layer your AI strategy requires.
CTGT is purpose-built for institutions where accuracy, auditability, and regulatory compliance are not optional. We welcome the opportunity to conduct an architecture review with your team and scope a pilot for your operational risk workflows.
Schedule a Review
ctgt.ai/contact-us