Responsible AI

Responsible AI, built into the method

We don't hand you a policy document at the end. We make six specific commitments and build them into every engagement from the first conversation: on your data, on human oversight, and on saying plainly what AI can't do. You can hold us to every one.

Start a conversation The six commitments →

The six commitments

What we commit to, on every engagement

These are how the work gets done, and not a review step added after the fact. If any of them conflicts with speed or convenience, the commitment wins.

Fitness assessment first

Before any build, we ask whether AI is the right answer, and whether the problem is worth solving with AI at all. You get that answer in writing, before you commit to anything.

Your data stays yours

Your documents, records, and conversations are not used to train models, and our vendors are bound by terms that bar it. Data-handling terms are explicit and set in writing before the engagement starts, and never buried in a footnote.

Human-in-the-loop by default

AI drafts, analyzes, and recommends. People decide. Nothing consequential ships on the strength of a model's output alone. A person reviews it first, every time.

Named limitations, in writing

We tell you what the system can't do, where it's likely to be wrong, and where a human needs to double-check it. You hear this before you rely on the system, and not after something breaks.

Documentation your team can read

We don't hand off systems nobody can explain. Whatever we build comes with documentation written for the people who'll run it, and not for another AI engineer.

Bias & fairness review

Anything that ranks, scores, or prioritizes people gets a bias and fairness review before it ships. It also stays explainable, so you can see and defend why it ranked the way it did.

When AI doesn't fit

We'll tell you when AI isn't the answer.

Said first, in writing

Sometimes the right call is a different tool, or no AI at all

Every engagement opens with a fitness assessment, not a pitch for AI. If the honest answer is a process change, a different piece of software, or no AI at all, that's what we tell you, before any build starts and not after you've paid for one.

We apply the same discipline to whether AI belongs in your business as to any strategic move: we aim for your result, not our utilization.

Standards we align to

Governance you can check

Responsible AI commitments mean little without a standard behind them. Two things anchor how we work.

01 · STANDARD

Data protection that fits you

Which data rules apply depends on where your people and customers are, what data is involved, and your sector. GDPR, CCPA and CPRA, Brazil's LGPD, and sector rules each bind different clients, so at the start we confirm which ones bind you and plan to those. Our own handling stays minimal and time-limited whichever rules apply.

02 · STANDARD

A reviewer panel on every engagement

Before work reaches you, a panel of review checks runs against it, including risk and responsible-AI considerations alongside logic and sources. The check is built into the method itself. The panel advises, and a principal makes the call.

Ask us to walk through any of these on a call. If a commitment here doesn't hold up to a direct question, that's something we want to know too.

Questions

Responsible AI, answered

Six specific commitments: a fitness assessment before any build, your data never used to train models, human-in-the-loop by default, named limitations in writing, documentation your team can actually read, and a bias and fairness review for anything that ranks or scores. Each one is a claim you can hold us to.

No. Your data stays yours. We do not use it to train or fine-tune models, and we work with vendors under terms that bar training on client data. Data-handling terms are set out in writing before the engagement starts.

We say so, in writing, before anything is built. Every engagement starts with a fitness assessment, and sometimes the right answer is a process change, a different tool, or no AI at all. You get that answer before a build starts.

It's built into the method itself, so there is no separate checklist applied after the fact. A reviewer panel checks risk and responsible-AI considerations on every engagement, the same way it checks logic and sources. The commitments shape how the work is done, and nobody signs off on them at the end.