Research-led strategy for human–AI interaction.
MindPort helps teams building AI products and experiences understand how people use, trust, adopt, and relate to emerging AI capabilities. Our work sits between research, product strategy, behavioral insight, and emerging technology. Designed for questions too new, too ambiguous, or too strategically important for conventional research.
Three offers, one practice.
AI experience research
For teams with an existing AI product, feature, prototype, or concept.
AI experience research helps teams understand how people actually experience an AI product: what they understand, where they hesitate, what they trust, what they resist, what creates value, and what causes them to disengage.
Especially useful when a product is technically strong but the human experience is unclear, uneven, or underexplored.
Typical questions
- How do users understand what the AI can and cannot do?
- Where does the experience create trust, confusion, frustration, or overconfidence?
- Which moments create value, delight, uncertainty, or abandonment?
- How does the product fit into real user workflows, habits, and decision-making?
- What changes would improve adoption, retention, usefulness, or perceived value?
Typical outputs
Research findings and synthesis · AIX diagnostic · UX and interaction insights · Trust, comprehension, and adoption barriers · Product and roadmap recommendations · Prioritized opportunities for improvement.
Human–AI product strategy
For teams developing new AI products, features, or interaction models.
Human–AI product strategy helps teams define what an AI product should become, how people should experience it, and which product decisions matter most.
Useful when a team is exploring a new capability, entering a new product category, validating a concept, or deciding where to invest next.
Typical questions
- What human need does this product serve?
- How should people interact with this AI capability?
- What expectations will users bring to the experience?
- What should the product explain, automate, personalize, or leave under user control?
- Which roadmap decisions are most likely to increase adoption and value?
Typical outputs
Product opportunity framing · User and use-case clarity · Experience principles · Interaction hypotheses · Strategic product recommendations · Roadmap implications · Research-backed decision support.
AIX advisory
For leaders making strategic decisions about AI products and emerging interaction models.
AIX advisory is an ongoing thinking partnership for teams working at the edge of AI. Designed for senior product, design, research, innovation, and strategy leaders who need independent perspective on how human–AI interaction is evolving and what it means for their product or business.
Typical questions
- How will people interact with this new AI capability?
- What human behaviors, expectations, or anxieties should shape our strategy?
- Where are we making technology-first assumptions?
- What should we test before scaling?
- How should we think about trust, control, agency, and adoption?
- What should our product or experience roadmap prioritize?
Typical outputs
Strategic advisory sessions · Research synthesis · Decision memos · AIX-based analysis · Product and experience challenge · Roadmap review and refinement.
Not every question fits neatly into a category.
Some of the most useful conversations begin before the shape of the work is clear. If you are exploring a human–AI interaction challenge, an emerging product question, or a strategic uncertainty that doesn't fit cleanly into research, strategy, or advisory, we are still happy to hear from you.
Tell us what you are trying to understand, decide, or test. We will help you determine whether we're the right fit, and what form the work could take.
Clear thinking for emerging technology.
MindPort is built for complex product questions, bringing curious, research-led, and practical thinking to teams working at the edge of AI.
Engagements typically range from four weeks to four months, depending on the question, the maturity of the product, and the depth of research required.
Most include a combination of product immersion, stakeholder conversations, user research, behavioral analysis, synthesis, and strategic recommendations.
We work closely with product, design, research, and leadership teams to turn insight into clear decisions.
Five shapes our work usually takes.
Validate a new AI product or feature
Understand whether a concept is meaningful, useful, trusted, and desirable before investing further.
Improve an existing AI experience
Identify where users struggle, disengage, overtrust, undertrust, or fail to see value.
Define an AI product roadmap
Use human insight to prioritize the features, experiences, and interaction patterns most likely to drive adoption and retention.
Explore a new interaction paradigm
Understand how people might respond to agents, multimodal systems, autonomous products, wearable AI, robotics, or other emerging technologies.
Pressure-test a strategic product bet
Bring independent research and strategic perspective to a high-stakes product decision.
Most AI begins with technical capability. We begin with the human experience.
We examine how people make sense of AI, how trust is formed or lost, how control is experienced, how value is perceived, and how new behaviors become habits.
That human lens helps teams make better product decisions.
Most of our engagements begin with a question.
A product question. A research question. A roadmap question. Or a strategic uncertainty about how humans will respond to a new AI capability. Bring us the question you are trying to answer.