How Stratyfy used a vendor-agnostic AI risk guide to generate trust with buyers who weren't ready to purchase | Laura Kornhauser

Stratyfy helps community banks and credit unions make better risk decisions — across credit decisioning, fraud detection, and bias detection — in one of the most regulated buying environments in B2B. In this episode of BUILDERS, we sat down with Laura Kornhauser, Co-founder & CEO of Stratyfy, on how she navigated three distinct chapters of the AI era, why gen AI broke her outbound motion, and the bets she's making on the next decade. Topics Discussed: How Stratyfy evolved through three AI chapters: explainability, bias mitigation, and the gen AI era

Why ChatGPT created confusion — not tailwinds — for ML companies in regulated industries

How AI-generated spam killed cold outreach and what replaced it

Why the "build vs. buy" trap is especially dangerous for smaller financial institutions

Stratyfy's bets on transparency, deterministic AI, and the agent-native future

Why "we need to use AI" is a dangerous mandate — and what the right frame is

How data preparation became a dedicated product and a pipeline on-ramp

GTM Lessons For B2B Founders: Reposition within a category redefinition — don't run from it: When gen AI went mainstream, all AI became synonymous with gen AI in buyers' minds. Rather than distance from the label, Stratyfy mapped their ML-based, explainable approach onto the transparency and bias concerns gen AI had surfaced. The market's fear became their proof point. When a macro trend rebrands your category against you, map your differentiation onto buyer anxieties — don't explain why you're different from the trend.

Cold outreach is dead in trust-gated markets — inbound trust compounds instead: AI spam has saturated inboxes so thoroughly that even high-quality cold outreach no longer lands. What replaced it: warm intros, in-person presence, and relationships built over years. The payoff: Stratyfy now has bank CEOs and boards coming to them — not to evaluate a product, but to rethink their third-party AI risk management practices entirely.

Education-first content earns access that product content cannot: Stratyfy published a third-party risk management guide for the AI era — no product tie, no pitch. It helps institutions evaluate any AI vendor, Stratyfy included. In a market flooded with vendor noise, content that helps buyers do their job earns trust faster than anything product-focused.

Problem-first beats mandate-first: Organizations struggling have "use AI" as the objective. The ones succeeding do three things: find partners who understand their regulatory environment, get their data in shape, and let technology choices follow from the problem. A technology mandate keeps you in a features conversation; reframing around operational problems puts you in a partnership conversation.

Data preparation is both a revenue line and a pipeline on-ramp: Stratyfy built a dedicated data prep, cleaning, and ingestion product after recognizing that data readiness was blocking customers from unlocking AI value — from Stratyfy or anyone else. For founders whose product requires data maturity, building that upstream capability isn't a distraction. It's a faster path to production and a natural expansion motion.

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