Our Approach
From neighborhood restaurants and local shops to creative agencies and midsize companies, many businesses feel pressured to “use AI” but lack clarity on where to start, what actually helps, or how to adopt it without disrupting their operations or people.
We focus on real-world use cases that reduce friction, improve decision-making, and create measurable impact—without hype or unnecessary.
Case Study - Oakland Stones
For Oakland Stones, we provided strategic guidance to clarify and strengthen their digital presence through a revamped website that reflects the quality and craftsmanship of their work.
By refining the structure, content, and user experience, the new site supports their broader digital strategy—improving credibility, visibility, and helping the business connect more effectively with potential clients.
Our System
Listening before building
This is intentionally slow, human-first, and designed to help local businesses transition into AI only after people are fully understood. Before touching tools, software, or AI, we invest time in understanding the people behind the business. We meet with owners, managers, and frontline operators to see how work actually gets done rather than how it appears on paper, asking simple but revealing questions about where time is lost, what feels unnecessarily stressful, and what breaks under pressure. This approach is rooted in human-centered design, as taught at institutions like Stanford d.school, IDEO, RISD, and Carnegie Mellon Human-Computer Interaction Institute, where empathy and real-world observation come before solutions. Rather than collecting requirements, the goal is shared understanding through direct exposure to daily operations, whether that means observing reservations during peak hours in a restaurant, tracking inventory between deliveries in a local shop, or mapping handoffs and communication inside a creative agency. This phase de-risks everything that follows. As thinkers like Don Norman have long shown, most failed digital and AI initiatives do not fail technically but conceptually, because teams solve the wrong problem very efficiently. By the end of Step 1, the conversation is no longer about AI at all.
Understanding core business
This step focuses on creating a clear, shared understanding of how the business actually operates day to day, so any transition into AI is grounded in reality rather than assumptions. We document how work truly flows through the organization by mapping workflows, decisions, handoffs, tools, and informal workarounds that keep things running. This means breaking down what happens from opening to closing, from lead to delivery, or from idea to execution, and identifying where friction, duplication, or confusion appears. This phase draws on systems thinking and service design practices taught at institutions like Stanford d.school and Carnegie Mellon Human-Computer Interaction Institute, where understanding the whole system matters more than optimizing isolated tasks. We often create simple flow maps that make invisible work visible. For a restaurant, this might reveal gaps between reservations, staffing, and prep. For a local shop, it can expose inventory blind spots or manual follow-ups. For a creative agency, it often highlights unclear ownership, tool overload, or bottlenecks between strategy, design, and delivery. This step ensures that when AI is eventually introduced, it supports the business as it truly operates today. As design leaders like Don Norman and service design pioneers have emphasized, you cannot improve what you do not fully understand. By the end of Step 2, everyone shares the same picture of how the business actually runs, creating alignment and setting the stage for a thoughtful, human-first transition into AI.
Improving processes
This phase is about improving processes in a practical, grounded way once there is full clarity on people, workflows, and constraints, and only then using AI where it genuinely helps. With a shared understanding of how the business operates, we design improvements that reduce friction, simplify decision-making, and remove unnecessary effort. This work is influenced by design strategy and systems optimization principles taught at institutions like Stanford d.school and practiced by organizations such as IDEO, where the emphasis is on small, meaningful changes rather than disruptive overhauls. Solutions may include clearer process definitions, better ownership, improved communication rituals, or lightweight digital tools that fit naturally into existing habits. In some cases, AI supports this transition by automating repetitive tasks, surfacing insights, or assisting decision-making, but it is never introduced for its own sake. For a restaurant, this might mean smarter scheduling or demand forecasting. For a local shop, it could involve simple customer follow-ups or inventory alerts. For a creative agency, it often means streamlining handoffs, reducing tool sprawl, or accelerating research and production. This reflects the long-standing view of designers like Don Norman, good systems make work easier for people, not more complex. By the end of Step 3, the business has transitioned into AI in a human-first way. It is not more technical, but calmer, clearer, and more efficient, with digital and AI tools quietly supporting how people already work.