Scalable operations without duty of care is a bill that arrives later. The exposure compounds at exactly the rate the capability does, and by the time it surfaces it is priced into everything you built on top of it. That is not a tooling failure. Nobody was answerable for whether the decisions were reasonable in the first place.
An AI system you rushed is a rebuild you have not scheduled yet. The work runs on a published standard rather than a house opinion: duty of care risk analysis, assessed against the Three Tenet Test of harm prevention, functional integrity and risk acceptability. You get a method with the criteria stated in the open, so the decisions you make now still make sense to whoever inherits them.
Judgment is adjudicated against written doctrine, not negotiated. Impact is taken at its highest and never averaged down. Catastrophic exposure is called unacceptable before arithmetic gets a chance to soften it. Nothing is built, governed or operated in your environment before an accepted risk assessment, including when the larger engagement is the one we would rather sell.
What you end up with is a working capability, operating under authority you set, reporting on its own behaviour as a by-product of running rather than as a report someone assembles later. When it drifts from what you intended, you find out then, not at the end of the year. Autonomy expands as the track record earns it.
The industry is very good at launching AI and very bad at keeping it right. Adoption was never the constraint. Organizations moved fast, and the things they built did not survive contact with a changing business. Nothing in the stack was designed to hold its shape, so the drift went unnoticed until the results stopped arriving.
80%+
of AI projects fail by estimates cited by RAND, about twice the rate of IT projects without AI.
RAND Corporation, 202428%
of AI use cases in infrastructure and operations fully meet return expectations.
Gartner, April 202642%
of companies abandoned most of their AI initiatives, up from 17% a year earlier.
S&P Global Market Intelligence, 2025Third party research, linked so you can check it rather than take our framing of it. These are not adoption numbers. They are what happens after launch.
Where being wrong is expensive: the work pays for itself where an AI decision carries a consequence somebody personally answers for, and where the answer has to survive contact with people outside the building.
Banking, insurance and regulated finance, where model risk and examiner scrutiny already have an owner, and agents are arriving faster than the review cycle can absorb them.
Teams and service providers who already run least privilege and evidence discipline, now applying it to systems that can write, deploy and approve.
Compliance, privacy, policy interpretation and research administration, where clinical and operational use has outrun published policy.
Law firms advising on AI exposure, and investors who need a portfolio company's AI position to hold up in diligence rather than in a pitch.
You enter where you are and go as far as the problem actually requires. Work you have already paid for is credited forward rather than repeated. Open any step for what it produces.
Governance work product
| Risk ID | Risk | Likelihood | Impact | Treatment | Owner |
|---|---|---|---|---|---|
| AI-014 | Model drift | Possible | High | Threshold monitoring and rollback | Model Ops |
| AI-021 | Data lineage gap | Likely | High | Provenance capture at ingestion | Data Lead |
| AI-027 | Prompt-injection exposure | Likely | High | Input isolation and adversarial tests | Security |
| AI-032 | Vendor model change | Possible | Medium | Version pinning and change gate | Vendor Mgmt |
| AI-039 | Unlogged sensitive-data access | Possible | High | REDACTED | Privacy |
| AI-044 | Evaluation gap | Likely | High | Scenario coverage review | Assurance |
A direct read on where automation would actually pay in your environment, and which obligations already attach to you whether or not you have looked. Short, scoped, and it commits you to nothing further.
Who it is for. Executives who know AI matters to the business and want a straight answer on where to begin before committing budget.
Duty of care risk analysis run against the Three Tenet Test: harm prevention, functional integrity and risk acceptability, assessed together rather than traded off quietly. Impact is taken at its highest, never averaged down. Most assessments are a checklist with a score on the end. This is a balancing test, so what comes out of it is a plan for the failure modes that actually apply to you, in the order they are likely to bite.
Who it is for. The General Counsel, CISO or Chief Risk Officer who personally carries the exposure and needs a position that survives scrutiny.
Codified policy covering risk, ethics, acceptable use, model lifecycle and data handling, mapped to the obligations that actually reach you and written to survive a customer's procurement review rather than to satisfy an internal checkbox. Add a new obligation later and the model tells you what you already satisfy and what is genuinely new. You are not paying to re-run the same assessment once per regime.
Who it is for. Whoever has been handed "we need to govern AI" and has to turn it into something with owners and teeth.
The workflows, tools and agents themselves, built where you work and operating under governance rather than having governance bolted on afterward. Most builds die between pilot and production, and the named reason is usually that nobody could approve them. This is also where we tell you the truth about tool sophistication. There is a wide gap between a chat interface with a system prompt, a retrieval pipeline, a workflow with tool access, and an agent that holds state and acts across systems. They fail differently, they cost differently, and they demand different controls. We will show you which one your problem actually needs, and say so when the honest answer is the cheapest one.
Those are five different things. Most capability claims in this market sit on the first or second rung and are sold as the fifth. Knowing which rung you are actually on tells you which parts of your stack will hold under load and which are waiting to surprise you.
Treating those as one condition is how organizations end up either ignoring a live problem or rewriting something dormant and harmless. Where something cannot be observed, we record it as unobserved rather than as fine. Absence of evidence is not evidence of absence.
Who it is for. The operating leader whose AI program stalled short of production, or who intends to get it right the first time.
Design, build, operate and improve on your behalf, run continuously by the same people who governed it. Accountability sits with the outcome rather than with a deliverable handed over at the door. Regulated operation adds evidence depth, tighter control coverage and a stronger assurance cadence. Private and sovereign operation runs inside your own boundary, on your infrastructure or in an enclave, with a controlled release channel. Continuous assurance runs underneath all of it: regression testing, adversarial validation before any release, drift checks, and evidence packages kept current rather than reconstructed.
Who it is for. The owner or chief executive who wants the outcome of an AI operated business without standing up and holding the team to run it.
Every engagement is scoped before it starts and quoted as a single fixed fee. No hourly rates and no meter running. If scope changes partway we requote and you decide whether to proceed. You know what the work costs before anyone begins it.
How much of your environment is in scope, how many obligations genuinely reach you, and whether you want a point in time position or an ongoing program. Settled on the first call, before a proposal is written.
The practitioner doing the work, an independent review of that work before it reaches you, and a written deliverable fit to put in front of your board, your client or your insurer. None of that is a line item.
Prior work is assessed against the same standard and credited into the engagement. Nobody is made to start over or to buy a step they have already earned.
Authority
Explicit rules define what an AI system may recommend, prepare or execute, and where it stops and waits for you. It does not take consequential action outside the authority you have granted it, and a named person is accountable for every conclusion that reaches you. That boundary is enforced in the system rather than promised in a policy.
Evidence
Every conclusion traces to the observation behind it. A verdict with no surviving observation is refused by the system that produced it, which is why the register still means something months later instead of quietly going stale.
Review
Work is reviewed independently by someone other than whoever produced it, against the same criteria every time. Self certification is structurally unavailable.
Progression
A safeguard reaches "in place" by passing verification with a completion record behind it. Nothing advances because someone marked it done, which is why the maturity picture still describes reality a year in.
Portability
The method does not depend on which model or vendor you standardize on, so it survives your stack changing underneath it. Model consumption stays yours and stays visible.
Containment
Work happens inside your environment. We do not hold, store or retain client data, and only the engaged practitioner has access. At close, access stands down and nothing of yours leaves with us.
Anything can look right on the day it ships. Everything below exists so you can tell whether it is still right months later, without taking our word for it.
Continuous assurance
Monitored
Operating within intended bounds
Last checked 26 Aug 2026, 09:42 ET
Decision record · Illustrative
Claims-triage agent
Flagged application REDACTED for human review
Fairness policy v3 - adverse-action rules
Confidence below policy floor - routed to a person
Adverse-action reasons attached - bias check within threshold
Reasoning trace attached REDACTED
Re-checked daily - last check clear
The AI governance system this work runs on is public and openly licensed. You can read the standard we hold ourselves to before you hire us, and hold us to it afterward. Very little in this market can be inspected before purchase.
Duty of care risk analysis exists so an analysis can be communicated to and accepted by others. That matters most later, when the people reading it are not the people who commissioned it and the context has moved on.
If your assessment is questioned by an examiner, an auditor, an insurer or opposing counsel, the methodology is ours to defend and we defend it with you. Our name is on the reasoning too.
The platform behind this work is submitted to external adversarial assessment against the same ladder, by reviewers who do not work for us and are not asked to be kind. We hold our own capability claims to the rung the track record supports, which is the only reason we are willing to grade yours.
Your answers compose a brief you can edit and send yourself.
Question 1 of 5
Question 2 of 5
Question 3 of 5
Question 4 of 5
Question 5 of 5
Where you look to sit
Start at the problem and decide whether our read of it matches yours.
The problem