Local ONNX classifier · Veritiana AI

Recognize the request before generating the answer.

Test a compact local model that predicts prompt task and complexity, then converts the visible input into a transparent cost comparison. The prompt stays in your browser.

Local inference
Confidence visible
Cost comparison
Live browser test
Prompt → local features → ONNX → task + complexity + comparison value
Loading model
0 characters · 0 estimated input tokens Nothing is uploaded for classification.
Try a prepared scenario
What this proves

A visible result, with its uncertainty left intact.

The test exposes task probabilities, complexity confidence and cost assumptions separately. Classification is measured locally; routing remains a future decision layer.

1,544 local features

Word and bigram hashes, character trigrams and eight structural signals.

Two predictions

Nine task probabilities and three independent complexity probabilities.

No hidden certainty

Low-confidence results remain visibly uncertain instead of becoming definitive labels.

Browser-native

The ONNX model and feature extraction run on the client through WebAssembly.

Open model release

Inspect the model, training process and evaluation.

The complete ONNX release includes the model card, reproducible training scripts, configuration, dataset manifest, evaluation results, license and browser integration examples.

View on Hugging Face
Open versus commercial

The classifier identifies. The commercial layer decides.

This page intentionally stops before production routing. It demonstrates recognition and comparison, while preserving the monetization boundary around execution policy and optimization.

Open layer

Recognition

  • Task and complexity probabilities
  • Confidence and uncertainty
  • Local browser inference
  • Token and cost comparison preview
  • Open ONNX release
Commercial layer

Execution decisions

  • Provider and model selection
  • Context and tool policies
  • Budgets, fallback and safeguards
  • Organization-specific rules
  • Measured quality and routing improvement
Future routing architecture

Classification is the first signal, not the final decision.

A future Veritiana routing layer can combine task, complexity and confidence with model capability, provider pricing, context requirements, policy and observed execution quality.

01User prompt
02Local classifier
03Routing policy
04Model + tools
05Execution
06AI Meter evidence
Limits

What this test deliberately does not claim.

No real-world accuracy claim

Internal weak-label evaluation is not an independent production benchmark.

No automatic best-model claim

The processing profile is illustrative and does not select or execute a provider route.

No billing replacement

Cost values are versioned API-equivalent assumptions, not provider invoices.