AI enablement, with discernment

Our position on tools is simple: use what works for you, not what you’ve heard should work for you. Before founding the studio, Holly worked on a multi-year research effort into what actually helps when AI is added to a tool, and what quietly costs both users and teams in terms of effort and productivity. The short version: AI features scattered through a product add cognitive load, and often cost people more time than they save when they’re trying to finish a task.

The same is true of running a business. It’s easy to get caught up playing with AI tools, but if you’re indexing for productivity, for being resourceful with your money and your time, you can’t afford to play for long before things get expensive.

We’ve watched teams spend a lot of money on tokens as their way of learning how to use AI, sign up for multi-year contracts with tools that don’t actually benefit them, or avoid engaging in AI altogether due to fear or overwhelm or lack of knowledge, and we know how costly it all can be. We want to help you utilize these powerful tools in ways that will benefit you and your business.

We’ve been using and building AI tools for years, in our corporate roles and on our own.

Holly worked on the Personalization team at SurveyMonkey, which was powered by the machine-learning team’s work, and she designed the AI Insights feature for the market research platform she helped design and build there. Liam has experience both building and training models.

When we founded this studio, we went through many tools and processes until we found what actually worked for us, and because we’re bootstrapped, every subscription and every token has had to justify itself. That includes knowing when to reach for a model directly, and when the smarter move is a small purpose-built tool that does the job without burning usage credits.

That discernment comes from experience on both sides of these tools: years spent building them in industry, and years spent applying them to run our own business. We bring both to every engagement, so the recommendations you get are grounded in practice, and you aren’t paying for trial and error we’ve already done.

We start by looking at everything your team touches: the dedicated AI tools, and the AI features tucked inside things you already pay for, like the Google Workspace suite. Then we walk through how all of it is actually being applied: what’s genuinely useful for the team, what’s costing you money, and what’s affecting productivity in either direction.

What you get back comes in two parts: a playbook of best practices, and a list of specific recommendations for your team, considering how it’s made up, what the business is trying to accomplish, and what your budget actually allows. Plenty of small companies have a line item resourced for exactly this kind of thing. What they need is a plan that works inside it.

Part of the work is understanding the security implications of the tools you adopt, and how much control you actually have over how your employees use them. We help you set guardrails that are clear enough to follow and realistic enough that your team actually follows them.

A successful engagement looks like this: ninety days after we’re gone, you feel confident in how you’re using AI. It’s adding real value — saving you time and energy on rote tasks — and you’re not losing hours to work that should still be handled by humans, because you understand where the line is.

We work with every kind of team, from highly technical to no technical background at all. If we’re being honest, the non-technical teams are the ones we love working with best. Wherever you’re starting from, we’ll meet you there and build policies and practices your team will actually keep.