How CTGT's Policy Engine delivers deterministic compliance, hallucination mitigation, and auditable governance for consumer-facing AI at enterprise scale.
When every brand in your portfolio runs its own AI pipeline, the result is not innovation. It is fragmentation: inconsistent outputs, unauditable decisions, and compliance gaps that compound with every new deployment.
Each brand team fine-tunes its own model. Without centralized governance, regulatory requirements get interpreted differently across business units. A policy change at the corporate level takes weeks to propagate, if it propagates at all.
Consumer-facing AI that fabricates product claims, nutritional information, or ingredient details creates real legal and reputational exposure. Standard guardrails catch less than half of these errors. For a portfolio the size of PepsiCo's, that failure rate is unacceptable.
When a regulator asks why your AI made a specific claim, "the model generated it" is not a sufficient answer. Today's stack produces outputs with no record of which policies were evaluated, which were violated, and what remediation occurred.
Shared infrastructure means Gatorade's voice can bleed into Quaker's. A model trained on your full corpus does not inherently understand where one brand identity ends and another begins.
CTGT does not replace your AI infrastructure. It governs it. Our Policy Engine intercepts every model output, evaluates it against your organization's full policy landscape, and remediates non-compliant content before it reaches the consumer.
Every policy in your organization, from FDA labeling requirements to brand-specific tone guidelines, is converted into machine-readable rules. These rules are enforced deterministically, not probabilistically. The engine does not guess whether content is compliant. It evaluates, scores, and acts.
Our multi-stage verification pipeline catches fabricated claims, incorrect entity relationships, and unsupported assertions. Each output receives a continuous confidence score from 0.0 to 1.0, enabling your compliance team to set granular thresholds for blocking, flagging, or approving content.
Every policy considered, every collision resolved, and every remediation applied is logged. The trail maps to your organizational hierarchy, giving legal and compliance teams a defensible record that satisfies regulatory scrutiny.
CTGT governs any foundation model: OpenAI, Anthropic, Google, or open-source. As you experiment with new providers, every deployment inherits your complete compliance posture from day one. No vendor lock-in.
Each brand operates within its own policy namespace. Gatorade's compliance rules, tone parameters, and approved claims library are fully isolated from Quaker Oats, Lay's, or any other brand in the portfolio. One engine, zero cross-contamination.
A Fortune 100 beauty and personal care conglomerate faced the same structural challenge: dozens of distinct consumer brands, each with its own voice, regulatory obligations, and audience expectations, all running through a shared AI infrastructure.
Their existing customer engagement platform treated all AI-generated responses identically. A professional salon brand sounded the same as a mass-market drugstore line. Responses were repetitive, brand-indistinguishable, and impossible to audit. CTGT's Policy Engine was deployed as a lightweight governance overlay, converting each brand's style guide and compliance requirements into deterministic, machine-enforced rules. No changes to the underlying platform. No API integration delays. No retraining cycles.
Consumer packaged goods companies operate under overlapping regulatory frameworks: FDA labeling, FTC advertising standards, state-level consumer protection laws, and internal brand governance policies. CTGT's policy hierarchy handles all of them simultaneously.
For consumer-facing use cases, governance does not need to be strictly deterministic in the mathematical sense. It needs to be confined to the brand parameters, ensuring every output stays within the bounded range that your legal, regulatory, and brand teams have defined as acceptable.
| Capability | Prompt Engineering | Fine-Tuning / RAG | CTGT Policy Engine |
|---|---|---|---|
| Multi-brand policy isolation | ✕ Prompt-level only | ✕ Requires separate models | ✓ Native namespace isolation |
| Defensible audit trail | ✕ No decision logging | ✕ Black box outputs | ✓ Full policy-level receipts |
| Real-time policy updates | Minutes to hours | Days to weeks (retrain) | ✓ Instant propagation |
| Hallucination mitigation | ✕ Inconsistent efficacy | Partial (data-dependent) | ✓ Multi-stage verification |
| Model-agnostic | ✕ Provider-specific | ✕ Model-specific weights | ✓ Any LLM, any provider |
| Policy collision resolution | ✕ Not addressable | ✕ Not addressable | ✓ Weighted vector adjudication |
CTGT is designed to augment your existing digital ecosystem, not disrupt it. Our architecture operates under the principle of least privilege and deploys without touching your core AI infrastructure.
CTGT founder Cyril Gorlla will walk your AI Leads through the policy engine architecture, focusing on how deterministic governance is enforced across multi-brand content generation without heavy fine-tuning.
We propose a focused proof-of-concept: ingest the compliance and brand guidelines for two distinct PepsiCo brands and demonstrate concurrent, isolated policy enforcement with zero cross-contamination.
Establish success metrics on hallucination rate, policy adherence, and audit trail coverage. Successful validation provides the blueprint for portfolio-wide rollout.