CTGT provides the deterministic policy enforcement layer that enables systems integrators and their clients to deploy generative AI in regulated environments with mathematical certainty, defensible audit trails, and measurable TCO reduction.
The gap between what generative models can do and what enterprises are willing to deploy them for continues to widen. The root cause is not model capability. It is the absence of a governance layer that provides the determinism, traceability, and regulatory alignment that high-stakes environments demand.
Financial institutions, insurers, pharmaceutical companies, and telecommunications providers all face the same structural barrier. Their compliance and risk teams lack the tooling to control model behavior at the level of specificity their regulators require. The approaches most organizations rely on today, including prompt engineering, retrieval-augmented generation, and fine-tuning, were not designed to deliver deterministic compliance. They operate at the wrong abstraction level: trying to encode thousands of business rules into probabilistic systems that have no mechanism for guaranteed enforcement.
The result is that most enterprise AI deployments remain confined to low-risk use cases, while the highest-value applications in compliance, surveillance, client communications, and internal audit remain locked behind manual processes and legacy technology. Organizations are spending 20 to 40 percent of their AI engineering budgets simply maintaining rule-based systems, and the cost of scaling these approaches grows faster than the value they deliver.
CTGT is a product-focused frontier interpretability lab. Our technology converts organizational policies, regulations, and standard operating procedures into machine-readable governance logic that enforces compliance on AI outputs in real time, without retraining, fine-tuning, or manual prompt management.
Key insight on cost efficiency: An open-source model (GPT-120B-OSS) governed by CTGT's policy engine achieves comparable or superior accuracy to frontier closed-weight models at a blended inference cost of $0.38 per 1M tokens, compared to $15.00 for frontier alternatives. This represents a reduction of over 97% in compute cost while maintaining performance parity.
The following delineation outlines precisely where CTGT operates in the enterprise AI stack, what remains in the domain of the integration partner, and how the two layers connect.
Translates structured and unstructured documents (SOPs, FINRA rules, SEC regulations, internal guidelines) into a high-fidelity policy graph with semantic relationships and priority hierarchies.
Multi-agent coordination frameworks, workflow design, agent handshake logic, and the foundational infrastructure for deploying AI across business units.
CTGT sits at the output layer of any model (open or closed-weight) via a lightweight API. Custom flags expose policy telemetry (hotspots, hallucination windows, compliance rates) to existing enterprise dashboards.
Non-generative evaluation of model outputs against the policy graph using semantic entropy methods. The order of policy application is deterministic, even when input text varies.
Client-facing interfaces, compliance dashboards, and the presentation layer. CTGT exposes raw metrics; the partner controls how they are surfaced to end users.
30-day hallucination windows, per-use-case compliance rates, policy trigger frequency, cross-agent conflict detection. All exposed via API for ingestion into OpenTelemetry or SIEM environments.
Non-compliant outputs are rewritten or flagged before they reach the end user. Policy updates take effect immediately across all governed models, without code changes or redeployment.
Where model-level training is needed (rare with CTGT's governance layer), the partner handles fine-tuning, RLHF, and prompt optimization for specific client environments.
Every policy decision logged with full traceability. The audit log streams to the partner's compliance archive via secure API, enriching existing review workflows without replacing them.
On multi-agent governance: CTGT treats each agent as a use case within its policy system. Use-case-level policies govern individual agents, while global organizational policies arbitrate when agents' objectives conflict. This resolves the challenge of ensuring that guardrails on one division's agent do not create second- or third-order negative effects on another division's agent, a problem that binary, keyword-based guardrails are fundamentally incapable of handling.
All benchmark data is sourced from published CTGT evaluations. FINRA compliance benchmarks were conducted against approximately 3,500 granular business rules extracted from FINRA's regulatory corpus.
| Configuration | Accuracy | Delta |
|---|---|---|
| Claude Sonnet — Base | 74% | Baseline |
| Claude Sonnet — with RAG Pipeline | 48% | −26 pts |
| Claude Sonnet — with CTGT Policy Engine | 96% | +22 pts |
RAG pipelines introduced noise that degraded baseline accuracy. CTGT's non-generative adjudication improved accuracy without introducing additional hallucination vectors.
| Metric | Result |
|---|---|
| Violating statements remediated (single pass) | 89.2% (464 of 520) |
| Granular business rules extracted from FINRA corpus | ~3,500 |
| Total policies in system during benchmark | ~35,000 |
| Policy ingestion time (150-page document, P90) | 20 seconds |
| Policy retrieval latency (across ~25,000 policies, P90) | 20 ms |
Benchmarked using GPT-120B-OSS on a single H100 GPU. Judge model: Gemini 3 Pro Preview. Model temperature: 0.0. Future multi-pass remediation methods are expected to improve accuracy further.
CTGT is deployed in production environments at Fortune 100 companies and global financial institutions. The following use cases have demonstrated the highest impact to date.
Real-time governance of advisor-to-client communications. The policy engine evaluates every message against FINRA, SEC, and OCC regulations before it is sent, replacing legacy regex-based surveillance systems. A leading global financial institution estimated a 20 to 40 percent reduction in total cost of ownership for their compliance infrastructure during their initial deployment.
Iterative, policy-based evaluation of model outputs against regulatory requirements. This functions as a continuous, automated audit process that keeps AI workflows exam-ready for FINRA, SEC, and OCC reviews, with a complete audit trail mapping every decision to the specific policy that triggered it.
Deployed at a portfolio company of a leading global insurer, the policy engine governs AI-assisted claims review to ensure regulatory compliance, consistent adjudication criteria, and full traceability. This is a fully live production deployment.
Telecom providers are using AI to automate customer interactions, but inconsistent model behavior creates compliance and brand risk. CTGT governs these interactions in real time, ensuring responses align with regulatory requirements, brand guidelines, and escalation protocols simultaneously.
A major global financial institution was processing over 10 million daily messages through a legacy surveillance stack that included thousands of regex patterns and classic ML models, some dating to the 1990s. They had hundreds of machine learning engineers dedicated to fine-tuning models on individual FINRA and SEC rules.
CTGT's policy engine was scoped for their Wealth Management division. During the alpha phase, the system demonstrated an estimated 20 to 40 percent TCO reduction by eliminating "thinking token" explosion — the compute overhead that occurs when rule-based constraints are stuffed into model context windows. The deployment is now expanding past alpha into broader implementation.
A senior technology fellow at this institution described the core value proposition as optimizing for the marginal cost of customization: not making existing workflows marginally faster, but unlocking use cases that were previously too high-risk or too resource-intensive to attempt.
CTGT offers flexible deployment models designed for the most stringent data residency and security requirements in regulated industries.
The majority of CTGT's current deployments are fully on-premise, which allows clients to share more sensitive policy documentation and production data with the system. The architecture follows the principle of least privilege, and the system's modular design allows custom API flags to be set for each deployment, exposing exactly the metrics and audit data each client's compliance tooling requires.
The convergence of real-time AI governance demand and multi-agent enterprise deployment creates a significant joint opportunity. The technology integration is direct, the narrative alignment is natural, and the go-to-market path is accelerated by existing client relationships.
Systems integrators building multi-agent frameworks for enterprise clients face a fundamental governance gap. When multiple agents interact across divisions, their objectives can conflict in ways that are invisible to traditional binary guardrails. An enforcement action that is correct for one agent's compliance requirements may create second- or third-order problems for another agent's objectives. Resolving these conflicts requires a full picture of the organization's business logic, with priority hierarchies that span across use cases.
This is precisely what CTGT's policy engine provides. Use-case-level policies govern individual agents, while organizational-level policies arbitrate cross-agent conflicts at runtime. The integration model is clean: the partner handles orchestration, client relationships, and domain customization. CTGT provides the governance substrate that makes the entire stack auditable and compliant.
A significant operational benefit of this approach is that it moves the capability to govern model behavior up the organizational hierarchy, from machine learning engineers to risk and compliance officers. CTGT supports multiple ingestion methods, including API and batch upload for technical teams, as well as natural language interfaces where non-technical stakeholders can describe desired model behavior and have those descriptions translated into enforceable policy. Policy owners can even point the system at their existing document repositories (SharePoint, internal wikis) and have model behavior dynamically updated as those documents change.
This shift reduces the dependency on expensive engineering resources for compliance maintenance and increases organizational trust in AI deployments. When the people who own the policy are the same people who govern the model, adherence improves and adoption accelerates.
The following framework is designed to move from technical validation to a scoped pilot in a single regulated vertical, with the objective of establishing a repeatable joint offering.
A working session at CTGT's offices for both engineering teams to map the API integration in detail. This includes a live demonstration of the policy engine against a sample regulatory corpus, and a walkthrough of the custom telemetry flags that would feed into existing observability infrastructure.
A bounded 14-to-30-day internal proof of concept using synthetic data, focused on a single regulated use case (recommended: financial services eComms surveillance or insurance claims governance). Binary, mutually agreed-upon success criteria defined upfront. This validates integration depth and establishes baseline performance metrics.
Deployment at a joint client in the financial services or insurance vertical, leveraging existing relationships and CTGT's published FINRA compliance benchmarks. The pilot would be structured to demonstrate measurable TCO reduction and compliance improvement against the client's current surveillance or audit infrastructure.
Based on pilot results, formalize the joint go-to-market offering. The partner's delivery teams become self-sufficient on CTGT deployment, with second-layer technical support from CTGT. Expand into additional verticals (telecom, pharmaceuticals) and additional use cases within each vertical.