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The deterministic layer for frontier intelligence
CTGT is an applied AI research laboratory fundamentally solving the alignment and reliability bottleneck for enterprise AI. For enterprises, especially highly regulated industries, deploying Generative AI is historically a compromise between capability and catastrophic risk. Standard enterprise approaches, such as RAG, fine-tuning, and prompt engineering, operate at the wrong abstraction layer. They are inherently probabilistic, carry massive engineering overhead, and fail to deliver the mathematical certainty required by the Fortune 500. We focus on the science of representation engineering and have productized mechanistic interpretability. By opening the "black box" of neural networks, CTGT has developed a proprietary architecture that intervenes directly at the model's representation layer. We convert complex corporate SOPs, SEC/FINRA regulations, and strict editorial rulebooks into machine-readable "Policy as Code," enforcing deterministic constraints and defensible audit trails without requiring expensive model retraining. The result is a step-function breakthrough in enterprise AI economics and capability. Our fundamental architecture allows organizations to run secure, self-hosted open-source models that mathematically match the reasoning and performance of frontier models. Benchmarks from our enterprise deployments demonstrate a 96.5% prevention of hallucinations, up to a 3.3× accuracy multiplier in complex domain-specific tasks, and an 80-90% reduction in human-in-the-loop manual review. Backed by an $8M seed round from Gradient Ventures (Google), General Catalyst, and Y Combinator, CTGT is currently deployed with Fortune 500 companies, including Tier-1 financial institutions and global media conglomerates, giving them the deterministic control necessary to deploy enterprise AI with zero margin for error.
CTGT positions itself as a deterministic, policy-driven layer for frontier AI, offering governance, audit trails, and controllable model behavior to replace probabilistic outputs with defensible guarantees. It targets high-risk industries such as finance, insurance, media, and consumer packaged goods by enabling policy-driven execution and human-in-the-loop remediation. The company emphasizes mechanistic interpretability and live policy-as-code to ensure auditability and safety without retraining.
CTGT converts SOPs and regulations into live, machine-readable rules that the AI follows automatically, with instant policy updates and defensible audit trails. It provides out-of-the-box governance to stop fine-tuning overhangs, offers human-in-the-loop or automated remediation, and enables policy-driven execution that can be tailored to an organization's structure. The platform promises deterministic outputs, logging of every decision, and the ability to edit LLM behavior to add safety guarantees without retraining, all while aligning with regulatory expectations.
Who it’s for: Enterprises in high-risk sectors (finance, insurance, media, consumer goods) seeking governance, auditability, and deterministic AI behavior; organizations requiring policy-driven control over AI outputs and defensible audit trails.
Mentions of engagements with high-profile industries and references to Fortune 500 alignment, plus press coverage and endorsements from notable tech leaders; indications of a product-focused governance platform with enterprise traction.
Cyril left his research at Stanford at 23 to found CTGT. His work on efficient and interpretable AI was presented at AI conference ICLR while he was the Endowed Chair's Fellow at the University of California San Diego. He is a Nordson Leadership Scholar and Ivory Bridges Fellow.
Trevor built hyperscale distributed systems for large machine learning workloads at MLsys@UCSD.
Ensure AI is trustworthy + compliant, eliminate hallucinations, and deploy 10x faster.
CTGT offers a platform for evaluating and monitoring AI models to reduce hallucinations and unpredictable behavior while enabling faster, more compute-efficient deployment for regulated enterprises (healthcare, finance, and others). The launch highlights automatic hallucination elimination, 10x faster training with 10x less compute, and enterprise control over AI behavior.
Automatically blocking risky behavior with real-time governance agents
A Platform for AI Governance
