LLMs don't fail loudly.
They fail quietly.

Looper catches unstable reasoning before it reaches your users. Get a reliability score and risk signal for every AI decision.

↓

Paste reasoning traces and see how stable they are. This is a live API - try it yourself. No API key required.

Or try your own

Enter 2-5 different reasoning attempts. Watch how the score changes as you modify them.

Reasoning A
Reasoning B
Reasoning C
Request (POST /score_demo) 
{
  "prompt": "Enter a question...",
  "variants": ["reasoning A", "reasoning B"]
}
Response 
{
  "stability_score": 0.00,
  "risk_band": "...",
  "variants": []
}

Try it yourself:

curl -X POST "https://your-domain.com/score_demo" \
  -H "Content-Type: application/json" \
  -d '{"prompt": "...", "variants": [...]}'
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Variant Analysis

Rank Variant Stability Observation

What does this mean?

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How It Works

Simple, powerful reasoning reliability in three steps

1

Generate

Create 2+ reasoning attempts in your own system using any method you prefer

2

Score

POST to /score and receive reliability metrics and risk assessment

3

Gate

Use the risk signal to gate actions, trigger review, or track drift

Why Reasoning Reliability Matters

❌ Without Looper
"Sarah bought 12 water bottles. She drank 5. How many does she have left?"
Model: "5 remaining."
⚠️ Model looks confident, but the answer is wrong
✓ With Looper
Same question, analyzed for stability
Stability: Low (38%) - High Risk
✓ Looper flags unstable reasoning before it reaches users

Proven in Production

Validated through comprehensive experiments with real model degradation scenarios

100%
Low-Risk Accuracy
When Looper says "low risk", it's always correct
113%
More Sensitive
Multi-variant vs single-variant baseline at detecting drift
64%
Better Than Random
Beats random baseline by significant margin
5-10
Days Earlier
Catches drift before traditional accuracy monitoring
See Deployment Patterns →