Build AI verification into your pipeline

Your AI pipeline generates answers. But how do you know they're correct? Synero's API asks four models about any output and returns a structured agreement score and the points where they split, so you can gate on it.

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Free account: 3 full queries, no card. Then pay as you go from $5 of credit, no subscription.

POST/api/query

Send an AI-generated answer for cross-model checking. Returns answers from independent models, a synthesized assessment, and a consensus object with an agreement score.

Your app

POST /api/query

Synero

Asks in parallel

GPTClaudeGeminiGrok

Combined answer

Plus where they agree

Capabilities

01Cross-Model Verification

Submit any AI-generated answer and Synero asks four independently trained models to check it. High agreement is a safer sign. A split means check it before you act.

02Confidence Signals

The consensus object gives a 0-100 score, the points every model agreed on, and each contested point. Build routing logic on the score.

03Pipeline 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.

04Batch Verification

Run several checks concurrently. Each one queries the models in parallel, within the 60 requests per minute per key limit.

05Query History

Each query returns a queryId, and queries are saved to your history unless you set private: true.

06Custom Verification Prompts

You write the verification prompt, so you can check for factual accuracy, logical consistency, completeness, or your own criteria. Templates such as devils-advocate are also available.

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({
    format: 'json',
    preset: 'council',
    prompt: `Verify this AI-generated answer for accuracy and completeness:

Question: ${userQuestion}
Answer: ${aiOutput}

Identify any factual errors, missing context, or misleading claims.`,
  }),
});

const { consensus } = await verification.json();
// consensus is null when fewer than two models answered or grading failed: treat that as unverified.
// Route on consensus.score (0-100):
// High score: auto-publish
// Low score: flag consensus.contested for human review

Frequently 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.

How much does it improve accuracy?

We do not publish an accuracy number. Cross-model checking can catch errors an individual model repeats with confidence, but results depend on the domain, so test it on your own data.

Can I use this for content moderation?

You can submit content for multi-model review and read where the models disagree. Treat it as a second opinion, not a replacement for your moderation policy.

How does pricing work for verification workflows?

Each check is a normal query billed by tokens from your credits. A free account gets 3 full queries, then credit packs from $5 (no subscription) or $10/month with $10 of usage, across all 20 models.

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Cross-model checking as an API. Free to try, no card.

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