Safety Is the Product.
Every TAL Corp model is built with the assumption that something will go wrong — and that the system must be designed to detect it, contain it, and correct it before it reaches the world. This page documents how we do that.
Safety is Not a Feature
At TAL Corp, safety is not a layer added on top of a capable model. It is a first-class engineering constraint — designed in from the first line of training code, validated at every stage of development, and re-evaluated at every deployment decision. A model that is not safe is not ready.
Interpretability Before Deployment
We will not deploy a model we cannot explain. Every TAL Corp model ships with a real-time interpretability dashboard — token attribution scores, attention weights, and reasoning traces — accessible to every API user. If we cannot see inside it, it does not ship.
Human Oversight at Every Stage
No TAL Corp model operates without a human-in-the-loop requirement at the certification level. S-2 requires human oversight at inference time. Our agentic systems are bounded by hard autonomy envelopes — the model cannot expand its own scope of action.
Alignment Scales With Capability
The prevailing assumption in AI development is that safety degrades as capability scales. Our research — and our deployment evidence — proves the opposite. TAL Corp's Constitutional AI v3 framework is designed to get safer as the model gets smarter.
Radical Transparency
We publish our safety evaluations. We publish our red-team results. We publish our failure modes and our sycophancy scores. We believe the field advances faster when labs are honest about what their models cannot do, not just what they can.
Every Model Ships With Receipts.
Six Principles. No Exceptions.
Constitutional AI v3 is the value framework baked into every TAL Corp model at the gradient level — not a post-hoc filter, but a first-class training objective. These six principles govern model behaviour across all contexts.
The model must not produce outputs that harm individuals, groups, or society — including subtle harms such as reinforcing misinformation, facilitating manipulation, or normalising dangerous behaviour.
The model must not deceive. This includes not producing false information it knows to be false, not creating false impressions through technically true but misleading statements, and not engaging in deceptive framing.
The model must remain responsive to correction, shutdown, and scope limitation by authorised humans. It must not resist oversight, deceive its operators, or attempt to expand its own autonomy.
The model must rely only on legitimate epistemic means — evidence, reasoning, accurate emotional appeals — to influence beliefs. It must not exploit psychological weaknesses or use coercive persuasion techniques.
The model must protect the epistemic autonomy of users — presenting balanced perspectives, encouraging independent thinking, and not nudging users toward particular conclusions without their awareness.
The model's behaviour must reflect the values articulated in this framework consistently — not just when explicitly evaluated, but in every response, including edge cases and adversarial prompts.
Found Something? Tell Us.
If you discover a safety vulnerability, alignment failure, or jailbreak in any TAL Corp model or system, we want to know. We review every submission personally and will respond within 5 business days. We do not pursue legal action against good-faith researchers.
Email us at: security@texasagilabs.com