The Core Team

Traditional user-needs research relies on interviews, ethnography, and surveys—but AI is redefining how we uncover opportunity areas. Systems like Henry can now scan thousands of data sources—forums, patents, complaint databases, and clinical studies—to reveal unmet needs and usability gaps before the first interview is even scheduled.

This doesn’t replace human insight—it amplifies it. When AI-driven synthesis becomes the foundation, researchers can spend their time refining, contextualizing, and shaping the insights that truly matter.

Expanding a single patent into a strategic cluster within the same technology category can make the difference between a short-term lead and long-term market dominance.

With Henry, our AI intelligence system, teams can identify where and how to build that depth:

• Mapping related inventions and prior art to reveal defensible white space
• Pinpointing claim extensions that close competitor gaps
• Linking scientific, clinical, and design data to uncover adjacent IP opportunities
• Guiding the next filings that reinforce your core innovation themes

Modern Human Factors (HF) isn’t “AI instead of testing”—it’s AI before and between testing to focus risk where it matters.

Why AI HF support is sound (and aligned with FDA thinking):

• HF remains mandatory; the inputs are evolving. FDA’s HF guidance requires a rigorous, risk-based process; it does not prescribe only lab studies as inputs. Literature, complaint data, and adverse-event analyses are all valid evidence streams feeding formative work. AI just helps scale those inputs. 
• Real-world evidence is encouraged. FDA explicitly supports using real-world data (e.g., MAUDE, user reports) to identify risks and inform development decisions. AI/NLP makes that synthesis faster and broader, not looser. 
• AI in device development has guardrails. FDA’s AI/ML action plan and Good Machine Learning Practice (GMLP) lay out principles for data quality, transparency, and validation—exactly the controls that prevent “hallucination” from driving decisions. 
• Signal detection is stronger, not weaker. Peer-reviewed work shows NLP can mine adverse-event narratives to surface use errors and failure modes earlier—improving where human testing should target. 

Bottom line: AI-driven formative synthesis prioritizes subsequent formative/usability testing—it doesn’t replace it. The result is smarter Formative 2 and a clearer trace from real-world signals → risk analysis → targeted human studies → safer designs. That’s exactly the direction regulators, HF standards, and leading teams are moving.

At Design Stack, Henry pairs large-scale evidence synthesis with expert HF judgment so teams test what matters most—faster, with greater confidence.

We believe in small teams that drive towards targeted outcomes, we do not rely on a solo project manager or single program leads. The strategic team members act collectively on fast paced processing and utilize digital tools to confirm decisions with our clients and keep all parties engaged and aligned. 

Our team is focused on the integration of AI and product development to solve client needs. Many of our clients are startups that run in stealth mode until they are ready for a product release.  You will not find us on social media marketing campaigns  and we do not hire agencies to promote our services. Our growth and client list  is based on word of mouth and strong relationships, not hype or bots. If additional expertise is required for the program, we work with various development partners contingent upon the specific needs for each project.

Startups are different than big companies and because of that we structure our process to support the needs of the group.  We established a development path that provides an option based on both parties' interests, to roll a percentage of the fees into equity at a specific time in the program. To learn more about our portfolio of startup companies and our process drop us a email. 

Design-stack’s AI system collaborates iteratively with our core design team to expand ideas, identify user needs, identify technology and design trends, map out competitive market product solutions, forecast on next-generation concepts, and identify gaps in intellectual property. While Henry may not be adept at generating new concepts without human input, the system’s highly iterative nature necessitates the expertise of an experienced product development team to interpret Henry’s findings. The synergistic collaboration between the design team and AI has yielded unique and novel solutions that seamlessly incorporate a multitude of inputs at an unprecedented rapid pace.  

In product development, understanding user needs is everything — but gathering those insights can be slow, fragmented, and biased by what we think users want.

That’s where Henry AI changes the game.

Henry scrapes web data, including chat groups, forums, and user communities, to uncover the real conversations happening around a product or category. It doesn’t just collect data — it interprets it through multiple lenses:

- User Needs: Identifies pain points, unmet expectations, and emotional drivers.

- Performance & Usability: Detects recurring complaints, workflow issues, or missing features.

- Market Signals: Tracks emerging expectations and competitor differentiators.

What makes Henry unique is the ability to adjust the influence of different data categories — weighting technical feedback, emotional sentiment, or market trends differently to match the development stage.

The result: a faster, data-driven path to uncovering true user needs — transforming web noise into actionable design direction. By automatically collecting, filtering, and summarizing qualitative data from diverse sources — including user studies, forums, MAUDE reports, and clinical literature it allows the team to identify patterns. 

This is how AI and human-centered design combine to accelerate innovation.
Henry doesn’t replace designers or researchers — it empowers them to focus on what matters most: creating solutions users truly value.