Principles
AI Governance Manifesto
We build AI systems for industries where errors have real consequences. These are the principles that guide every design decision.
We build AI systems for industries where errors have real consequences: law, healthcare, regulated finance, and government. In those contexts, capable AI is not enough. Responsible AI is the only option.
What that means in practice: it is not enough for a model to give accurate responses. The professional needs to know how it reached that response, which sources it consulted, what risks are implied by acting on that output, and when the final decision is theirs — not the system's. That is AI Governance. And for us, it is not a layer added at the end: it is the architecture from day one.
This manifesto is not a statement of good intentions. It is a description of how we build.
AI amplifies human judgment — it does not replace it
AI has extraordinary capabilities to process information, identify patterns and generate options. But judgment — weighing what matters in a specific context, bearing responsibility for a decision with real consequences — is human. We design our systems to enhance that judgment: the model proposes, the professional validates. Every tool we build has explicit human intervention points, not as a limitation, but as a guarantee that whoever is accountable for the outcome retains control.
Trust requires evidence, not promises
Any AI system can claim to be "safe" or "responsible." For us, that requires verifiable evidence: every product publicly classified by risk level, every model claim verified against its actual sources, every agent action recorded in an exportable audit trail. Applying governance internally is not enough — you have to be able to show it. Trust that cannot be audited is not trust: it's faith.
AI reasoning must be traceable, not opaque
When an AI system cites a statute, a ruling, or a precedent, the professional needs to verify not just the result but the path: which sources were consulted, how entities were related, what connections between case, statute and obligation grounded that response. Opacity in reasoning is not just a technical problem — it is a governance risk. "Silent retrieval failure" — when the system did not find the right source but does not say so — is one of the most dangerous forms of failure in AI for regulated industries.
Risk is classified and governed — not ignored
Not all AI carries the same level of risk. A system that summarizes public case law does not have the same impact as one that generates litigation strategy or recommends treatments. We design with that distinction in mind: we classify each system by its level of autonomy and potential for harm, and apply controls proportional to that risk. The goal is not to limit what AI can do — it is to ensure that what it does is manageable, auditable and consistent with the regulatory frameworks applicable to the use context.
Access to trustworthy AI is access to justice
The governance standards described in this manifesto should not be a luxury available only to large organizations with internal legal and compliance teams. In Latin America, most law firms are small; most regulated companies do not have a full-time DPO. We design infrastructure so that the high standard is the accessible standard: a three-lawyer firm in Argentina can operate with the same level of auditability as an enterprise firm. We democratize without lowering the bar.
If you work in a regulated industry and want to adopt AI with the level of control your context requires, we are building exactly for that case. The technical details of how we implement each of these principles are publicly documented in our Trust Center.
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