We build AI for a living. If we don't hold ourselves to a high bar on transparency, we can't expect anyone else to. These are our public commitments: how we label our own content, how we oversee the AI that touches our work, and how we stay in the conversation about what's right.
Version 1.4 · Updated February 5, 2026
Every AI consultancy talks about responsible AI. Most keep the details private. We don't. If you're going to hand us work, you should know exactly what AI touches that work, who reviews it, and what happens when something goes wrong. This page is a living document — we update it as our own use of AI evolves, as regulations change, and as clients push us for more clarity. Version history lives at the bottom.
Emails, slide decks, deliverables, social posts, case studies — all of it. Four categories, applied honestly, with a human accountable at every level.
Content created entirely by humans, without AI assistance. Reserved for the highest-stakes work where the full weight of human judgment is warranted.
Human-AI collaboration with human direction throughout. A human writes or edits the bulk of the work; AI assists with drafts, research, or polish. A human takes full accountability.
AI produces the initial content; a human reviews, edits, and approves before publication. Human accountability is never removed — the reviewer is responsible for every word.
Automated AI responses for low-risk, bounded scenarios — guided chatbots with specific data, scheduled updates, routine classifications. Built with guardrails, monitored, auditable.
Labeling tells you what happened. Oversight is how we make sure what happens is worth labeling.
A human reviews every AI output before it leaves HQ, full stop. For automated AI, humans own the design, the monitoring, and the off-switch.
We curate training and context data deliberately. AI works only as well as the data underneath — so we treat data hygiene as a core quality gate, not an afterthought.
Regular monitoring of AI outputs for accuracy, tone, and bias. Issues are logged, tracked, and shared with the team — not buried.
Our team is trained on responsible AI use on day one and continuously after. We update training as the tools change — which is often.
We share our practices publicly, talk about them with clients, and update them when we learn better.
Our AI practices live on a public page — this one. No clicking through four layers of legal text. If a client, peer, or reporter wants to know how we use AI, the answer is one URL away.
We share our commitments in public because we want other firms to do the same. Responsible AI isn't a competitive moat — it's table stakes. The sooner more firms show their work, the better the industry gets.
If you have questions, concerns, or a better way to do this — we want to hear it. Email us, call us, press us. Our public position won't change without a public update.
AI moves fast. These commitments move with it. We publish a new version when our practices change materially — with a clear note on what changed and why.
A short log of material updates to these commitments.
Added examples of content that typically falls into each labeling category. Tightened language around oversight and review cadence.
Added a named contact for transparency questions. Moved the commitments page to the main site footer.
Introduced the "AI Generated" category for low-risk automated scenarios with explicit guardrails.
Clarified that human accountability is never removed, even when AI generates the initial content.
Original commitments published.
We mean it — transparency is a conversation, not a broadcast. If you know a better way to do this, or think we're getting something wrong, tell us.