Sibling Dynamics | The human side of AI, robotics, and security

The problem

Hiring the person who leads your AI is one of the highest-stakes decisions your organization will make, and it is usually made on the weakest evidence: a polished résumé, a confident interview, and a reference the candidate chose.

In a regulated environment, the cost of getting it wrong compounds. An AI leader who overstates what they built, misreads a framework, or has never carried a system through an actual audit does not fail quietly. They fail in production, in front of a regulator, a year and [cost-of-a-bad-executive-hire, pending a citable source] later.

The standard hiring process cannot catch this. A search firm is paid to place, not to verify. Technical interviews test whether someone can talk about AI, not whether they have governed it under real constraint. And the claims that matter most, “I led that architecture,” “I passed that certification,” “that outcome was mine,” are exactly the claims a hiring panel is least equipped to check.

What we do

We independently verify the AI or ML leader you intend to hire, before the offer goes out.

We are not a search firm and we hold no placement incentive. We are not assessing culture fit or negotiating a package. We do one thing: we establish, with evidence, whether this person can actually do the job you are hiring them for, in an environment where being wrong is expensive.

Because we run our own governed AI systems every day, we assess builders the way builders are actually tested: on the decisions, the trade-offs, and the failure modes, not the vocabulary.

How we do it

We run diligence in stages, technical and human, so each stage informs the next and nothing rests on a single signal.

Claim mapping. We turn the candidate’s own record into a specific, testable set of claims. Ambiguity gets resolved here, not glossed over.
Technical verification. We probe depth where your work demands it: model and system architecture, evaluation discipline, deployment and monitoring, security, and the governance frameworks that bind your industry.
Judgment under constraint. We assess how the candidate reasons about risk, trade-offs, incident response, and the boundary between what AI should and should not be allowed to do. We test the judgment, not the confidence.
Record and reference verification. We independently confirm what can be confirmed: roles, scope, outcomes, and certifications, beyond the candidate-supplied list.
Synthesis and written judgment. We integrate every stage into a single defensible recommendation.

What you get

A verification report stating our recommendation and the reasoning behind it, in language your board can read without translation.
A verified claims record: each material claim, what we tested, and what we found.
A technical assessment of demonstrated depth against your role and sector.
A judgment and risk profile, including the failure modes we would watch for.
A conditions-for-success brief: if you hire, what has to be true around this person for the hire to work.
A principal debrief: a direct conversation with the senior person who did the work.

We give you a recommendation and the basis for it. The hiring decision stays yours. We make it defensible.

The frameworks it maps to

We verify fluency and demonstrated experience against the frameworks that govern your industry, including ISO/IEC 42001, ISO/IEC 27001, the NIST AI Risk Management Framework, EU AI Act obligations, DO-178C for aerospace and defense, NERC CIP for energy and utilities, 21 CFR Part 11 and GxP for life sciences, and SR 11-7 for financial services.

Who it’s for

Boards and audit committees approving an executive AI appointment. CHROs hiring a Chief AI Officer, VP of AI or ML, or Head of Data Science into a regulated environment. CEOs and founders making a first senior AI hire without an in-house expert to vet another. Investors and acquirers underwriting a company whose value depends on an AI leader’s claims.