Build AI verification into your pipeline
Your AI pipeline generates answers. But how do you know they're correct? Synero's API cross-checks any output against four models and returns structured agreement/disagreement signals for automated quality control.
/api/querySend an AI-generated answer for cross-model verification. Returns agreement/disagreement analysis from four independent models plus a synthesized assessment.
Capabilities
Cross-Model Verification
Submit any AI-generated answer and Synero checks it against four independent models. High agreement signals reliability. Disagreement signals the answer needs human review.
Confidence Signals
The synthesis highlights where models agree (high confidence) and where they disagree (lower confidence). Build automated routing logic based on consensus level.
Pipeline Integration
Design verification checkpoints in your AI pipeline. Use Synero as a quality gate — auto-approve high-consensus answers and flag disagreements for human review.
Batch Verification
Verify multiple outputs in parallel. Each verification runs four models simultaneously, and you can run multiple verifications concurrently via the API.
Audit Trail
Every verification is logged with the original output, all four model responses, and the synthesis. Build compliance and audit trails for AI-generated content.
Custom Verification Prompts
Customize how the council evaluates answers. Optimize for factual accuracy, logical consistency, completeness, or domain-specific criteria.
Code Example
// Verify an AI-generated answer before publishing
const aiOutput = await generateAnswer(userQuestion); // your existing AI pipeline
const verification = await fetch('https://synero.ai/api/query', {
method: 'POST',
headers: {
'Authorization': 'Bearer YOUR_API_KEY',
'Content-Type': 'application/json',
},
body: JSON.stringify({
prompt: `Verify this AI-generated answer for accuracy and completeness:
Question: ${userQuestion}
Answer: ${aiOutput}
Identify any factual errors, missing context, or misleading claims.`,
}),
});
// Route based on consensus:
// High agreement → auto-publish
// Disagreement → flag for human reviewFrequently asked questions
How does this differ from a single model checking itself?
Self-verification has fundamental limitations — a model that generated a wrong answer is likely to confirm it's correct. Cross-model verification uses independently trained systems with different architectures and training data, making shared blind spots much less likely.
What's the typical accuracy improvement?
Cross-model verification catches errors that individual models miss. While exact improvement depends on the domain and use case, multi-model consensus is consistently a stronger reliability signal than any single model's confidence score.
Can I use this for content moderation?
Yes. Submit content for multi-model review to catch issues that a single model might miss — including subtle misinformation, biased framing, and factual inaccuracies that pass single-model filters.
How does pricing work for verification workflows?
Each verification query uses the same credit system as standard queries. For high-volume verification pipelines, contact us about volume pricing and dedicated throughput.
Verify AI output automatically
Cross-model verification as an API. Build confidence signals into your AI pipeline.
Read the Docs