Plain answers to what mission-driven leaders raise before they bring AI anywhere near their work.
We treat your data, and your members’ data, as the whole ballgame. We only build on platform tiers that give you contractual data protection, not just feature promises. Free and consumer-grade tiers can expose your inputs to model training and human review, so we never put member data, strategy documents, or internal communications on them. Before we deploy anything, you get a plain-English breakdown of exactly where your data goes, what the vendor retains, and for how long.
That’s the most common way organizations burn money on AI, and it’s why engagements start with your workflows, not a product list. Tools are the last decision we make, not the first. And because we don’t resell anyone’s software, we have no stake in which platform you land on.
Poorly, if it’s done to them. Well, if it’s done with them. We build worker-centered: we start by asking your staff what problems they actually have, we train people up rather than around, and nothing we build is designed to surveil or replace the people it serves. That’s a stated design constraint, in writing, at the start of every engagement.
Then you’re not ready, and nobody here is going to push you. AI is getting embedded and mandated all around you. Working with us is the opposite of that: you set the pace, you decide what gets adopted and what doesn’t, and “not yet” is a legitimate outcome. If that’s where you land, you’ll leave knowing what being ready would take, and you won’t have bought anything you didn’t choose.
Pricing follows the engagement. We scope it after a short intake, so the number fits the work rather than a menu. We work with organizations that have budget allocated to this, on individual consulting through full organizational builds.
It’s four things working together: governance and policy, a workforce and threat read, hands-on capacity building, and custom builds for the work your team actually does. The point isn’t a tool, it’s adoption that sticks and a posture you can defend.
Start with the thing your team hates doing every week, and solve that first. The a-ha moment comes when someone asks, wait, if it can do this, can it do that? Everything else follows from there.
Not by default. Off-the-shelf tools can retain and train on what you paste, so the first move is an access and data policy, then training on what’s safe to share. Done right, you get the upside without exposing your members.
Governance is the rules: who can use what, with which data, under what policy. Training is the fluency: teaching your people to actually hold a conversation with these tools. You need both, and most organizations buy one and skip the other.
Yes, every organization touching AI needs one, even those choosing not to adopt. A workable policy covers approved tools, what data can and can’t go in, vendor review, disclosure, and a path for staff to flag shadow AI. We draft it with you.
That’s the entire premise. The visionless move is to cut headcount and call it innovation. The better one is to keep your people, upskill them, and do far more with the team you have. We build for that.