{{BRAND}} evaluates AI systems against a published framework of safety, fairness, and governance standards — so enterprises can deploy with evidence, and AI developers can demonstrate rigor to the customers and regulators who ask for it.
Assessment intake isn't open yet — the framework below is where we are.
{{BRAND}}'s framework is organized into four domains. Each domain has its own protocol, scoring model, and certification tiers. We're opening the framework domain by domain, starting with Safety & Alignment, so that every certification we issue is backed by a protocol that's actually been run and validated — not a checklist we haven't tested.
This protocol evaluates whether a model or agent behaves acceptably under adversarial pressure, ambiguous instructions, and realistic misuse attempts — and whether its safeguards hold up outside the lab, in the deployment context you actually intend to use it in.
A scoped, single-context evaluation. Suited to a specific deployment or a pre-purchase check on a vendor's model.
Full-protocol evaluation across the defined harm taxonomy, with documented remediation and a re-test before sign-off.
Verified status plus a standing re-assessment cadence tied to model and deployment changes.
The same sequence applies to every engagement, regardless of size. Scope is fixed before testing starts, and nothing is certified without a second reviewer's sign-off.
We define the system boundary, deployment context, and applicable risk classification with you, and agree the exact protocol that will be run.
{{BRAND}}'s evaluation agents execute the domain protocol against the system, generating adversarial cases and logging every interaction.
Findings are scored against the framework and checked by a second, independent evaluator before any determination is drafted.
A pass, conditional, or fail determination is issued with a scoped report — including any gaps that need remediation before re-test.
For {{TIER:3}} engagements, we set a re-assessment cadence tied to model updates, so certification tracks the system as it changes.
Manual red-teaming alone doesn't scale to the pace AI systems ship at. {{BRAND}} runs its protocols through purpose-built evaluation agents, so a single engagement can cover far more adversarial ground than a manual review — with every result still checked by an independent human evaluator before it counts.
Generate and execute adversarial prompts across a domain's harm taxonomy, at a volume no manual team can match.
Rate each transcript against the protocol's severity scale and flag ambiguous cases for human review.
Assemble the evidence trail into the scoped report a compliance or procurement team can act on.
{{BRAND}} is engineered for the teams making deployment and procurement decisions right now — and structured so AI developers can meet those same buyers halfway.
Whether you're procuring a third-party model, standing up an internal agent, or answering to a board that's asking hard questions about AI risk.
{{BRAND}} doesn't write new standards — we apply the ones that already exist, plus a working protocol for the parts they leave unspecified, and we do it as a party with no stake in the vendor's outcome.
25+ years leading enterprise IT programs, including large-scale SAP S/4HANA transformations and global program delivery across the US, Asia, and Latin America. Holds a post-graduate AI/ML certification from UT Austin's McCombs School. {{BRAND}} combines that enterprise delivery discipline — the kind that has to hold up under audit — with an evaluation methodology built to run at the pace AI systems actually ship.
We're finalizing the Safety & Alignment protocol before we start scoping real engagements. Check back as it's published, or watch this page for the intake to open.
{{EMAIL}} — not yet monitored for intake