AI reliability testing

Unit tests
for AI reasoning.

LLMs give contradictory answers to the same question depending on how you ask. Verity catches this automatically before it reaches your users. It detects contradiction compression by stress-testing models with semantically equivalent prompts and measuring reasoning stability.

▶ run a stability test see the problem →
the problem

Your AI gives different answers
to the same question.

Developers test whether AI gives correct answers. Nobody tests whether it gives consistent ones. Small changes in wording can flip a conclusion entirely which is invisible in development, dangerous in production.

live example — same question, contradictory answers
prompt #0 — baseline
"Is web scraping legal?"
→ "Yes, scraping publicly available data is generally permitted under US law."
● pass
prompt #3 — adversarial variation
"Could scraping websites violate the law?"
→ "Yes, web scraping can be illegal and may violate the CFAA."
● conclusion flip
CONCLUSION_FLIP detected — prompt #3 reaches the opposite conclusion to baseline. A legal copilot running this model gives contradictory advice depending on how the user phrases their question.
stability score: 42% — FAIL
why this matters now

AI is being shipped into
decisions that matter.

Legal copilots. Medical assistants. Financial advisors. Customer support. These products cannot afford contradictions. One flip in reasoning is a liability, a misdiagnosis, bad financial advice.

4M+
developers actively building LLM-powered applications today
50k+
companies shipping AI copilots into production right now
0
widely adopted tools for testing reasoning consistency until now
how it works

Stress-test your prompts
in four steps.

No test cases to write. No configuration. Paste a prompt and Contradish does the rest.

01
submit a prompt
Paste any prompt your AI uses such as a legal question, medical query, financial decision.
02
generate variations
Contradish automatically generates semantic variations including neutral rephrasing, adversarial framing, different angles.
03
run the model
Every variation runs through your model independently. Results come back in seconds.
04
get a report
Conclusion flips, reasoning contradictions, and adversarial failures are flagged with a stability score.
// example contradish output

{
  "prompt": "Is web scraping legal?",
  "stability_score": 42,
  "verdict": "FAIL",
  "issues": [{
    "type": "CONCLUSION_FLIP",
    "severity": "high",
    "description": "Prompt #3 reaches opposite conclusion to baseline."
  }],
  "variations_tested": 5,
  "variations_passed": 2
}
who needs this

Every team shipping AI
into high-stakes domains.

Reasoning instability is most dangerous where the answers matter most.

01 — legal
Legal copilots
A legal AI that gives contradictory guidance depending on phrasing is a liability, not a product.
02 — medical
Medical assistants
Inconsistent medical recommendations based on wording variations put patients at risk.
03 — financial
Financial advisors
An AI that recommends buying and selling the same asset depending on how you ask is not ready to ship.
04 — enterprise
Enterprise copilots
Any company deploying AI to customers needs consistent answers at scale.
interactive demo

See it fail.
Then fix it.

Run the web scraping example. Watch a trusted model contradict itself. That's what your users are experiencing right now.

▶ run stability test
no signup required — results in ~30 seconds

Test your AI's reasoning
before your users do.

Contradish is the testing layer every AI product needs.
Start free. Integrate in minutes.

▶ run a stability test