How We Work:
From diagnosis to compounding growth.
Every engagement starts with understanding what you're actually facing and ends with growth that accumulates.
Here's what working with us looks like.
Four Phases. One System.
Every engagement maps to one or more of the Three Pillars of Preeminence: audience growth, pricing power, and repeat engagement.
The phases are how we get there.
The Engagement Model
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Phase 01: DIAGNOSE
Weeks 1–2
We start by understanding what you're actually facing. Not a generic intake call. A structured diagnostic that maps your current product and AI decisions to the three growth levers, identifies where you're leaving value on the table, and surfaces the questions you should be asking but aren't.
Typical outputs: Growth lever assessment. Stakeholder alignment read. Decision map.
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Phase 02: DESIGN
Weeks 2–4
We design the strategic engagement. This means scoping the right research, identifying the right markets or customer segments, selecting the right methods, and building a plan that maps directly to the decisions your team needs to make. No research for research's sake. Every study, every data point, every interview is tied to a specific business decision.
Typical outputs: Engagement roadmap with decision milestones. Research and analysis plan. Team structure.
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Phase 03: EXECUTE
4–12 weeks per cycle
We embed practitioners inside your team and run. Customer research across markets, competitive intelligence, AI workflow evaluation — whatever the engagement calls for. We operate in rolling sprint cycles, delivering insights continuously and pressure-testing them against your strategic decisions in real time. We don't disappear for six weeks and come back with a deck.
Typical outputs: Rolling insight deliverables. Strategic recommendations tied to product, pricing, or market decisions. AI governance frameworks.
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Phase 04: COMPOUND
Ongoing
Growth isn't a one-time project. The best engagements become continuous. We help you build the internal systems, measurement frameworks, and team capabilities to keep the growth engine running after we step back. For clients who want ongoing support, we stay embedded in rolling sprint cycles, refining and compounding over time.
Typical outputs: Measurement strategy. Internal capability building (workshops, frameworks, playbooks). Ongoing embedded advisory.
40,000+ hours of customer insight across 19 countries.
Shaped AI and product decisions at Adobe, Amazon, Google, Meta, Microsoft, UiPath, and Walmart.
Our Methods
Every strategic engagement draws on a deep methodology toolkit: qualitative research, quantitative analysis, mixed methods, ethnography, Jobs to Be Done, Kano model analysis, competitive benchmarking, and more. We use AI tools for speed.
We use human judgment for everything that matters.
The Strategic Layer: Why Human Judgment Still Wins
A new wave of AI-powered research platforms is compressing the data collection process: automating interviews, running surveys at scale, surfacing patterns faster than any human team. That is real progress. We use tools like these ourselves.
But data collection was never the hard part. The hard part is knowing what to do with what you find.
What AI Research Platforms Do Well
AI interview and analysis tools are getting very good at the structured, repeatable parts of research: high-volume concept testing, quick-turn usability studies, pattern recognition across large datasets. If you need to talk to 50 people and surface the top themes, there are now platforms that can do that faster and cheaper than any agency.
We welcome this. It means the commodity layer of research is moving even faster toward automation. That is good for the industry because it clears the way for the work that actually matters.
What AI Research Platforms Cannot Do
Strategic synthesis across multiple inputs
AI platforms work from their own interview data. We work from interviews plus analytics, business context, competitive landscape, stakeholder dynamics, and domain expertise. The "so what should we actually do?" layer requires human judgment that no model can replicate, because it depends on understanding your organization, not just your users.
The relationship and trust layer
When our clients invest in Amplinate, they are buying judgment. They need someone who understands their organizational dynamics, who can push back on stakeholder assumptions, who can read a room and change the presentation on the fly. That is not a feature you can automate.
Cross-cultural and international research
AI interviews work in English and a handful of other languages. The nuance of cross-cultural research — regulatory differences, market-by-market strategy, and the things people mean but do not say — requires human researchers who have spent years in those markets. We operate embedded teams across 19 countries on 5 continents.
AI decision advisory
AI research tools are optimizing the "gather feedback" step. We help companies figure out where AI agents should and should not operate, how to design human-AI workflows, and how to make the product and AI decisions that drive growth.
Complex qualitative methods
Diary studies. Ethnography. Longitudinal research. Contextual inquiry in physical environments. These require presence, adaptation, and the kind of rapport that only happens between people.
How AI Fits Into Our Work
AI is part of how we work, selectively and with clear boundaries:
Analysis Support
We use AI tools for pattern recognition and efficiency in the parts of our work where speed matters and judgment does not need to be primary. This lets our senior team spend more time on synthesis, strategy, and the client conversations that actually move the needle.
Quote Identification
Surfacing relevant participant quotes faster so researchers can focus on interpretation, not transcription.
Pattern Recognition
Spotting high-level themes and commonalities across responses, giving our team a head start on the synthesis that matters.
Bias Mitigation
Helping surface insights that might be overlooked due to unconscious bias, so our analysis reflects the full picture.
What We Don't Do
We don't use AI to replace qualitative judgment. We don't feed your data into models that train on it. And we don't hand you an AI-generated report and call it insight.
Every finding we deliver has been interpreted, pressure-tested, and validated by a principal-level practitioner. That's the standard.
Data Practices
Your data stays yours. We never use client data to train or improve AI models. All data remains strictly confidential under our standard agreements.
Human oversight is non-negotiable. Every AI-assisted analysis is reviewed and validated by a senior researcher before it reaches you.
We're transparent about what's AI-assisted and what isn't. If we use AI tools in any part of an engagement, you'll know.
Our Position
The research industry is being reorganized by AI. The data collection layer is being commoditized. The strategic layer is becoming more valuable, not less.
We've spent 20+ years and over 40,000 hours building the judgment that sits on top of the data. That's what our clients pay for and it's the one thing that can't be automated.
AI research platforms are great at gathering signal. We're great at turning signal into decisions.
Ready to make
the right decision?
Tell us what you're facing. We'll tell you if we can help.