MVTest

AI risk discovery for real user workflows

I test your AI-facing flows end-to-end and document failure modes that don't trigger errors or alerts— especially where users are most likely to act on plausible output.

↓ Start Risk Snapshot

01 / The Problem

CI is green. Dashboards are quiet. Demos work.

Yet users can still be confidently misled.

AI-specific failure modes traditional QA misses:

Plausible output that is factually wrong
Confidence cues that overstate reliability
Failure modes that don't trigger errors or alerts
Boundary cases (new customer segments, sparse data, ambiguous input)
Tool/action side effects users don't expect
Evaluation mismatch (offline metrics vs. real workflow)

REAL FAILURE MODE

Actionability risk: AI recommends "Archive these 47 support tickets." User clicks confirm. Tickets gone. AI was wrong about 12 of them—but there's no undo. Support backlog now contains frustrated customers who think you ignored them.

These failures rarely crash the app. They quietly damage confidence and adoption—and you learn about them from support tickets, not metrics.

02 / The Approach

Your AI flows get tested end-to-end with deliberate edge inputs, ambiguous contexts, and high-trust moments—where users are most likely to follow the output.

What I look for:

Where calibration fails (AI sounds certain but shouldn't be)
Where silence hides real risk (no error, no warning)
Where a reasonable user would take an irreversible action
Where plausible-sounding mistakes compound

This isn't penetration testing, performance testing, or bug bounty. It's pure risk discovery focused on trust erosion and user decision risk.

03 / The Offer

Founder Risk Snapshot — $500

A short, hands-on review of your AI workflows focused on user decision risk: places where output looks reasonable, users trust it, and the downside is meaningful.

Definition: Risk = plausible output + high user trust + meaningful downside + low detectability.

What you get

10–15 min screen recording showing failure modes and user-impact paths
1-page risk snapshot (screenshots + prioritization)
15-min walkthrough call

Turnaround is typically 2 business days once access is working.

Who this is for

Founder-led B2B SaaS (2–20 people)
AI embedded in real workflows (not just demos)
Launching in 1–4 weeks or recently shipped a major AI feature
No dedicated QA or risk function

This is not:

Penetration testing or security audit
Performance or load testing
Bug bounty or comprehensive QA replacement
Code review or implementation changes

Output: a prioritized risk list. You decide what to change.

Prioritization framework

Each item is scored across four dimensions:

Impact

Revenue / Trust / Credibility

Likelihood

In real usage patterns

Detectability

Will you notice before users?

Fix Effort

Rough complexity estimate

➜ Start

Email frank@mvtest.dev with:

1. Product name + one-liner
2. URL to test (staging or prod)
3. Launch date (if within 4 weeks)

Email [email protected]

I'll reply same day (AET) with fit + next steps. If it's a match, turnaround is typically 2 business days.

04 / About

Technical BA with deep SDET experience in healthcare, security, and logistics—domains where "works-as-designed but harmful-in-context" failures have real consequences.

AI trust risk is uniquely hard because traditional QA approaches break down when outputs are non-deterministic, calibration matters more than correctness, and user decision risk compounds silently.

CASE LOG 042

Example: Recently tested an AI feature that suggested "low-risk" actions to users. The AI was right 95% of the time in offline evaluation, but the 5% failures were silent—no error message, no warning, no hedging language. Just confident bad advice. Users had no signal not to trust it. The failure mode wasn't "crash" or "wrong output"—it was calibration mismatch in a high-stakes context.