■Our Approach
Evidence-backed.
Regulated-industry ready.
Built to close decisions.
KERF was built by researchers who came up in MBB strategy practices and specialist boutique firms — where the standard for customer evidence was set by the decisions it had to defend: board decks, regulatory submissions, market-entry calls.
■Background
MBB rigour. Boutique speed. Sector depth.
Our team came out of MBB strategy practices and specialist boutique research firms where the question was always the same: what do customers actually need, and what is the fastest credible way to find out?
We have run GTM evidence work across fintech, insurtech, digital health, and legaltech in markets where regulatory context is never optional and the cost of a wrong assumption compounds fast.
KERF is the firm we would have wanted as a client — a specialist who could match the pace of an AI product team and deliver evidence that holds up in a board room, not just a product review.
Firm background
MBB strategy practices · specialist boutique research firms
Markets covered
US · EU · APAC
Client types
Seed–Series C AI product teams · Health system operators · Regulated fintech
Research delivered
GTM signal · feature validation · market entry · regulatory positioning
■Why Teams Come to KERF
The four GTM hurdles that derail AI product teams.
01
You shipped to a buyer you've never spoken to
AI-native teams move fast. They validate with usage data, internal debate, and AI-generated personas. By the time they hit their first enterprise sales cycle, they're pitching to a buyer they've never actually met — and the objections that kill the deal were predictable from day one.
02
Your product is solving for the wrong room
The room where your product decisions get made — founders, PMs, early advisors — has strong opinions and weak market signal. The room where your product gets bought — compliance, clinical ops, legal, procurement — has a completely different agenda. Most teams never close the gap.
03
AI validation has a structural blind spot
AI tools synthesise what's already in your dataset. They can't surface the workflow you've never observed, the trust threshold your model doesn't clear, or the objection your buyer's legal team will raise on first review. The hotspots that derail GTM are always outside the data you have.
04
In regulated markets, the cost of a wrong assumption compounds fast
A wrong assumption in a consumer app means churn. In fintech, digital health, or legaltech, it means a pilot pulled after 60 days, a compliance review, or a feature rollback post-launch. The stakes justify a different standard of evidence — and most research vendors aren't built for it.
“Teams go to market assuming they know their buyer. Most of them have never sat in the buyer's room. The sprint exists to close that gap before it becomes a revenue problem.”
KERF Research
■Public Frameworks
Three frameworks. Free to use.
Click any framework to read the full methodology.
Ready to validate your next decision?
Q2 cohort applications are open. One of two partner slots remaining.