How CTGT's Policy Engine delivers the mathematical certainty and defensible audit trails required to automate complex regulatory ingestion across thousands of jurisdictions, without relying on probabilistic AI outputs.
Tax compliance platforms face a uniquely demanding AI problem. Jurisdictions worldwide continuously update their regulatory codes, and the rules that govern how goods are classified and taxed change on a weekly or monthly cadence. Historically, translating these changes into production-ready tax engine logic has been a manual, labor-intensive process. AI should be the obvious accelerant here, but the reality has been more difficult.
Large language models are effective at reading and parsing unstructured regulatory documents. They struggle, however, with two things that matter most in this domain: mathematical precision and deterministic conditional logic. Tax rules are dense with if-then-else branching, jurisdictional exceptions, and arithmetic calculations. LLMs are probabilistic by nature, and even at their most capable, they will occasionally hallucinate values, misinterpret boolean conditions, or silently drop edge cases. In a domain where a single miscalculated rate creates downstream liability, "usually right" is not an acceptable standard.
The result is a forced reliance on human-in-the-loop review that largely negates the ROI of automation. Engineers and tax analysts must verify every AI-generated rule, even when the model reports high confidence. This approach does not scale. As document volumes grow and jurisdictions multiply, the oversight burden expands linearly, creating an operational bottleneck rather than an efficiency gain.
CTGT's Policy Engine takes a fundamentally different approach to AI governance. Rather than attempting to make LLMs more reliable through prompting, fine-tuning, or retrieval augmentation, we introduce a layer of deterministic control that operates independently of the model itself.
The engine works by ingesting policy documents, whether regulatory texts, SOPs, or internal compliance manuals, and translating them into an enforceable policy graph. This graph preserves the semantic relationships between rules: hierarchy, dependencies, jurisdictional context, and conflict resolution logic. It understands that a California tax code and a New York tax code are not competing rules, but parallel branches that should be applied based on the context of each transaction.
When the LLM generates content, it is evaluated against the relevant subset of this graph in real time. The system does not ask the model to hold all rules in its context window. Instead, the Policy Engine selects only the policies that are relevant to the specific input, evaluates adherence using non-generative methods like semantic entropy and information-theoretic techniques, and enforces compliance at the output layer. The LLM operates on a tightly scoped problem, and the graph handles the governance.
CTGT's Policy Engine has been benchmarked against baseline models, standard enterprise RAG pipelines, and constitutional AI prompting across multiple frontier and open-source models.
| Model | Baseline | + CTGT Policy | + RAG | Delta (CTGT) |
|---|---|---|---|---|
| GPT-120B-OSS | 21.30% | 70.62% | 63.40% | +49.32 pts |
| Gemini 2.5 Flash-Lite | 60.34% | 66.46% | 64.63% | +6.12 pts |
| Claude 4.5 Sonnet | 81.27% | 87.76% | 84.33% | +6.49 pts |
| GPT 5.2 (Legal Reasoning) | 67% | 87% | 81% | +20 pts |
To validate the engine at enterprise scale, CTGT ran a comprehensive benchmark against the complete FINRA regulatory rulebook. Approximately 3,500 granular business rules were extracted from the source documents and ingested into the Policy Engine alongside additional test policies, bringing the total corpus to over 35,000 active policies. The system was then evaluated on 520 distinct violation scenarios generated from those rules.
The engine achieved an 89.2% remediation rate, successfully identifying and correcting 464 of 520 violating statements in a single pass. Policy ingestion for a 150-page regulatory document completed in under 30 seconds at P95 latency. In production, policy retrieval across the full 25,000-policy corpus operated at 35ms P95 latency, with no meaningful degradation observed between the 25,000 and 35,000 policy thresholds.
The core challenge of applying AI across variable, jurisdiction-specific regulatory environments is not unique to tax. CTGT has addressed structurally identical problems in production.
A large workforce management platform serving thousands of employers nationwide faced a familiar challenge: leave entitlements under federal and state regulations vary significantly across jurisdictions. California's paid leave provisions are materially different from New York's. The nuances of eligibility, accrual rates, and employer obligations shift with every state line.
The existing system relied on a rule-based chatbot with over 500,000 decision branches. Maintaining this system as regulations changed was unsustainably expensive, and conflict resolution between overlapping jurisdictions was brittle and error-prone.
CTGT's Policy Engine replaced this architecture by ingesting the full body of leave regulations and automatically constructing a policy graph that understands jurisdictional relationships. The system knows that a California FMLA provision and a New York FMLA provision are not competing rules; they are contextually distinct branches under the same regulatory framework. The appropriate policies are applied based on the user's jurisdiction, employer metadata, and conversation context, without the LLM needing to hold all 50 states in its context window simultaneously.
CTGT was built for organizations where data sovereignty is non-negotiable. The platform supports multiple deployment models designed to meet the most stringent InfoSec requirements, and the vast majority of enterprise deployments operate entirely within the client's own infrastructure.
The critical distinction of CTGT's approach is that the policy layer is not a prompt. It is not injected into the context window, and it does not depend on the model's ability to follow instructions reliably. The policy graph is evaluated as a separate, deterministic process.
When a regulatory document is uploaded, the engine parses it into individual policy nodes and assigns each one a criticality score, a semantic intent vector, and relational metadata that defines its relationship to other policies (hierarchy, dependency, incompatibility, and jurisdictional scope). This structure is built using non-generative methods, so the graph itself is not subject to hallucination.
At inference time, the engine matches the incoming context against the graph, retrieves only the relevant policies (at P90 latency of 20ms across 25,000+ policies), and evaluates the model's output for compliance. Non-compliant content is flagged or automatically remediated before it reaches downstream systems. Every decision is logged with the full policy path, creating a complete, defensible audit trail for every AI-generated output.
For domains involving conditional logic and mathematical operations, the engine extracts the logic parameters of each rule and enforces them deterministically. The LLM is constrained to tasks where it excels, such as natural language understanding and document parsing, while the policy graph handles the rule application, branching logic, and conflict resolution that require mathematical certainty.
We recommend a focused, phased engagement that delivers measurable value quickly and scales on demonstrated outcomes.