Most AI innovations fail because they’re designed for idealized scenarios, not complex, real-world human environments.

Many AI initiatives fail not because the technology doesn’t work, but because the vision doesn’t match reality. Your prototype might be elegant, but if the solution doesn’t fit how people actually work, behave, or make decisions in context, it won’t survive in the wild.

We immerse ourselves in the real-world environments where your AI needs to work, observing workflows, cultural nuances, and operational realities that reports miss. We bring back evidence that challenges your assumptions: what people actually need, where your vision misses the mark, what needs to change before you build.

The outcome: a refined vision and value proposition grounded in reality, not speculation. You get the clarity to align your team, investors, and stakeholders around what will actually work—so you can build with confidence instead of crossing your fingers at launch.

Most AI innovation begins with the technology and searches for a problem. We flip it: we start in the wild, such as workplaces, communities, markets, and explore how AI could create real value within the messy contexts where people live and work.

We identify unmet needs and unexpected opportunities that only reveal themselves in context, the kind that reports and surveys miss entirely. Then we bring back evidence: what problems are worth solving, where AI could genuinely help, and what would make people actually use it.

The outcome: new opportunities for growth grounded in reality, not speculation. We translate what we witness into actionable requirements, what data your AI needs, how it should collaborate with humans, what contextual factors determine success. You innovative from evidence, not assumptions.

Investing in AI solutions without validating their real-world fit is expensive gambling. Solutions look brilliant in decks, proposals, and demos, then fail in practice because they don’t align with how people actually work, decide, or behave in context.

We go into the wild before you write the check. Through our unique methodology, we test whether your AI solution fits the human workflows, cultural contexts, and operational realities where it needs to survive. We bring back evidence: does it integrate naturally? Do people actually want to use it? What breaks when it meets reality?

The outcome: evidence-based validation that gives investors and funders confidence. You see the risks before they cost you and the opportunities before you scale. Less gambling. More informed decisions. Better returns.