Find the AI failure that could quietly break confidence.
I help early-stage B2B SaaS teams surface AI-driven failure modes that don’t show up in CI, dashboards, or happy-path demos — before customers find them first.
Many teams ship AI-powered features confidently — until something subtle goes wrong:
These failures rarely crash the app. They quietly damage confidence and adoption.
Before a release — or after shipping a major AI feature — we focus on three questions:
Judgment-led, exploratory, and time-boxed — optimised for signal, not coverage.
Explore live AI-facing workflows to identify silent failure modes that affect decisions, confidence, or outcomes.
Identify the small set of user actions where AI behaviour must be reliable, bounded, or clearly communicated.
Produce a concise, founder-readable summary of what could go wrong, why it matters, and what deserves attention first.
I log into your product, run the AI-driven flows the way real users do, and push on the decisions where they’re likely to over-trust the output. The goal is to surface the blind spots that cause wrong actions, quiet drop-off, or loss of confidence before customers find them.
Outcome: Clarity on the one or two blind spots most likely to embarrass the product, undermine confidence, or surface later as “we should have caught this” moments.
Price: $300 USD (fixed scope)
Scope: Discovery only. I surface risk; your team decides what to fix.
I work with small B2B SaaS teams building AI-driven products where user confidence matters.
My background is exploratory testing and release validation. My focus here is risk: identifying where systems can quietly violate user expectations without obvious failure.
I work solo by design and step in briefly to apply judgment, surface risk, and leave teams with clarity.
Free risk assessment call
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