The Process

From question to verified answer in <3 seconds

Every query goes through a four-stage pipeline designed to maximize accuracy and minimize AI hallucinations.

Write your prompt

Type any question, technical, factual, research-based, or strategic. Choose which AI models to query: GPT-5 Mini, Claude 4 Sonnet, Gemini 2.5 Flash, Sonar Pro, and Grok 3 Mini. Each model brings unique training data and reasoning strengths.

All models are queried in parallel

talkory.ai dispatches your prompt to all selected models simultaneously via their official APIs. Responses are collected within seconds, not the minutes it would take to do this manually across five browser tabs.

Consensus engine analyzes responses

The consensus engine embeds all responses, computes cosine similarity, extracts key concepts with NLP, and identifies semantic agreement. Models that agree on core concepts raise the confidence score; outliers are flagged as divergent.

Get a verified answer with confidence score

You receive a merged consensus answer, a confidence percentage (0–100%), per-model scores and rankings, divergent points highlighted, and a full cost breakdown, all in one clean interface.

📝 Your prompt is sent to all models

"What is the best database for scalable applications?"

GPT-5 Mini Claude 4 Sonnet Gemini 2.5 Flash Sonar Pro

⚙️ Consensus engine runs

Embeddings computed · Cosine similarity calculated · Key concepts extracted · Agreement measured

✅ Verified consensus delivered

PostgreSQL is the top recommended database for scalable applications, agreed by all 4 models. Confidence: 83%

83%

Confidence

4/4

Agreement

$0.09

Total cost

Live Examples

See the consensus engine in action

Five real queries across multiple AI models, compared, verified, and scored automatically. Hover to pause.

Query 1 of 5 · Software Engineering

"What is the best database for scalable applications?"

83%

Confidence

4/4

Agreement

$0.09

Query cost

Query 2 of 5 · Machine Learning

"What is the best language for machine learning projects?"

91%

Confidence

5/5

Agreement

$0.08

Query cost

Query 3 of 5 · Security

"What are the best practices for REST API security?"

88%

Confidence

4/5

Agreement

$0.10

Query cost

Query 4 of 5 · Frontend Dev

"Should I use React or Vue for a startup MVP?"

76%

Confidence

3/5

Agreement

$0.09

Query cost

Query 5 of 5 · Research

"Explain quantum computing and its business applications"

79%

Confidence

4/5

Agreement

$0.11

Query cost

GPT

GPT-5 Mini

PostgreSQL is widely recommended for scalable systems with ACID compliance and advanced indexing.

CLD

Claude 4 Sonnet

PostgreSQL excels for most cases. At massive scale, CockroachDB is worth considering.

GEM

Gemini 2.5 Flash

PostgreSQL with PgBouncer handles enterprise loads. Spanner for global scale.

SNR

Sonar Pro

PostgreSQL leads for relational scale. MongoDB suits flexible document workloads.

Consensus Answer

83% confidence

PostgreSQL is the top recommended database, agreed by all 4 models. Pair with read replicas and Redis for high-traffic. At global scale, consider CockroachDB or Cloud Spanner.

GPT

GPT-5 Mini

Python dominates ML due to libraries like PyTorch, TensorFlow, and scikit-learn. Unmatched ecosystem.

CLD

Claude 4 Sonnet

Python is the clear choice for ML. For production speed-critical paths, consider Rust or C++.

GEM

Gemini 2.5 Flash

Python is standard for ML. Julia is gaining traction for scientific computing and numerical analysis.

SNR

Sonar Pro

Python with PyTorch is the industry standard. R is preferred in statistics-heavy academic contexts.

Consensus Answer

91% confidence

Python is the unanimous choice for machine learning, agreed by all 5 models. Its ecosystem (PyTorch, TensorFlow, scikit-learn) is unmatched. For production inference speed, consider C++ or Rust extensions.

GPT

GPT-5 Mini

Use HTTPS, OAuth 2.0, rate limiting, input validation, and rotate API keys regularly.

CLD

Claude 4 Sonnet

Enforce HTTPS, use JWT with short expiry, implement rate limiting, and sanitize all inputs.

GEM

Gemini 2.5 Flash

Key practices: TLS everywhere, short-lived tokens, WAF protection, and comprehensive audit logging.

SNR

Sonar Pro

HTTPS + OAuth2, CORS policy, rate limiting per IP/user, and security headers are essential.

Consensus Answer

88% confidence

Core REST API security: enforce HTTPS/TLS, use OAuth 2.0 with short-lived JWTs, implement rate limiting per user/IP, sanitize all inputs, add security headers, and maintain comprehensive audit logs.

GPT

GPT-5 Mini

React has a larger talent pool and ecosystem, making it safer for startups hiring quickly.

CLD

Claude 4 Sonnet

Vue is gentler for small teams; React wins for scale and hiring. Both are solid MVP choices.

GEM

Gemini 2.5 Flash

React is the default for MVPs in 2025 due to Next.js, Vercel, and the broader hiring market.

SNR

Sonar Pro

Vue 3 with Nuxt is easier to learn; React with Next.js offers more long-term scalability.

Consensus Answer

76% confidence

React (with Next.js) is the safer MVP choice for most startups, larger talent pool, stronger ecosystem. Vue is a valid option for smaller teams prioritizing learning speed. Models slightly diverge, consider your team's existing skills.

GPT

GPT-5 Mini

Quantum computing uses qubits in superposition for exponential speedups in optimization and simulation.

CLD

Claude 4 Sonnet

Near-term business applications: drug discovery, financial modelling, logistics optimization, and cryptography.

GEM

Gemini 2.5 Flash

Quantum advantage is proven in specific domains. IBM and Google are leading cloud access. Commercial use is still 5–10 years away for most industries.

SNR

Sonar Pro

Current quantum hardware is NISQ-era. Real enterprise impact likely post-2030. Key areas: materials science and secure comms.

Consensus Answer

79% confidence

Quantum computing uses qubits for exponential parallelism. Business impact is real but still early, near-term applications include drug discovery, financial optimization, logistics, and cryptography. Broad commercial availability expected post-2028.

The Algorithm

The Consensus Scoring Algorithm

The confidence score is not a black box. It's computed from three transparent, weighted components.

STEP 1 · 50% weight

Agreement Score

Measures how many models agree. If 4 of 5 models say the same thing, agreement score = 0.8. Semantic similarity above 0.80 cosine threshold counts as agreement.

STEP 2 · 30% weight

Response Quality

Each response is scored on completeness (30%), logical consistency (25%), clarity (20%), and reasoning depth (25%). Averaged across all models.

STEP 3 · 20% weight

Model Reliability

Historical reliability scores are assigned per provider based on known benchmarks. GPT-5 Mini and Claude score 0.9 each.

PREPROCESSING

Semantic Similarity

Responses are embedded using sentence transformers and compared with cosine similarity. Matches above 0.80 threshold count as agreement even with different wording.

EXTRACTION

Concept Extraction

NLP extracts key topics from each response. Frequently mentioned concepts are weighted more heavily in the final consensus summary.

OUTPUT

Final Score Formula

Confidence = (Agreement × 0.5) + (Quality × 0.3) + (Reliability × 0.2). Example: 0.8 × 0.5 + 0.85 × 0.3 + 0.88 × 0.2 = 83%

One prompt. Five AI models.
One verified answer.

Write your prompt

All models are queried in parallel

Consensus engine analyzes responses

Get a verified answer with confidence score

📝 Your prompt is sent to all models

⚙️ Consensus engine runs

✅ Verified consensus delivered

Consensus Answer

Consensus Answer

Consensus Answer

Consensus Answer

Consensus Answer

Agreement Score

Response Quality

Model Reliability

Semantic Similarity

Concept Extraction

Final Score Formula

Ready to get verified AI answers?

One prompt. Five AI models.One verified answer.

Write your prompt

All models are queried in parallel

Consensus engine analyzes responses

Get a verified answer with confidence score

📝 Your prompt is sent to all models

⚙️ Consensus engine runs

✅ Verified consensus delivered

Consensus Answer

Consensus Answer

Consensus Answer

Consensus Answer

Consensus Answer

Agreement Score

Response Quality

Model Reliability

Semantic Similarity

Concept Extraction

Final Score Formula

Ready to get verified AI answers?

One prompt. Five AI models.
One verified answer.