Do not just return a prediction. Explain its condition.
TQNN wraps inference in an operational report: what the engine predicted, how confident it is, how trustworthy the input appears, and whether the result should be accepted, reviewed, or rejected.
Confidence-aware output
Expose certainty rather than hiding it behind a single class label.
Input-integrity visibility
Surface missingness, corruption, and quality signals before they quietly shape a decision.
Decision control
Accept, review, or reject output using transparent thresholds and warnings.
From raw signal to controlled decision.
One consistent path turns structured inputs into an observable, application-ready response.
Input
Signals, rows, windows, features, or embeddings.
Validate
Shape, type, missingness, and quality checks.
Infer
Hosted TQNN runtime processes the request.
Measure
Confidence, entropy, integrity, diagnostics.
Gate
Accept, review, or reject based on evidence.
Return
Standardized JSON for downstream systems.
Use it where reliability matters.
TQNN is domain-agnostic by design. The focus is not a list of industries—it is robust inference over structured information.
Fault detection
Classify abnormal system states while exposing confidence and data quality.
Sensor resilience
Work with noisy, drifting, missing, or partially degraded measurements.
Scientific data
Run repeatable inference over experimental and research feature sets.
Manufacturing
Support process monitoring, quality signals, and operational diagnostics.
Financial signals
Evaluate structured indicators and time-series windows through one contract.
Research signals
Explore EEG and other structured biosignal features for non-clinical research.
Image features
Infer over pre-extracted image statistics and visual feature vectors.
Custom pipelines
Bring your own numeric representation without rebuilding infrastructure.
Built in public. Tested across difficult data.
TQNN has been evaluated on industrial fault diagnosis, sensor corruption, scientific signals, financial features, text, tabular data, and image-derived features. Results are reported with strengths and limitations—not hidden behind a universal-performance claim.
Industrial fault diagnosis
Benchmarked on the Tennessee Eastman Process, including corruption-aware training and confidence gating.
Known limitations
Performance varies by dataset and task. TQNN is not positioned as universally superior, and difficult cases remain part of the published development story.
Install. Send. Inspect.
The official Python SDK provides a direct interface to the hosted platform. Every successful request returns the result, TQNN report, and diagnostics.
pip install tqnn
from tqnn import TQNNClient
client = TQNNClient(api_key="TQNN_xxxxx")
response = client.run_any(
data=[[1.2, 0.4, 3.3, 0.1]],
mode="TABULAR",
task="fault_diagnosis",
metadata={
"class_labels": ["healthy", "fault"]
},
)
print(response["result"])
print(response["tqnn_report"])
print(response["diagnostics"])
Start free. Scale when ready.
Enter your email to receive an Explorer API key with 100 free calls, or choose a paid monthly plan for higher-volume development. Every key works with the official Python SDK—no base URL configuration required.
One Explorer key per email. Paid subscriptions continue through secure Stripe Checkout. TQNN provides experimental developer infrastructure and is not medical, financial, investment, or diagnostic advice.
Explorer
Try the complete hosted platform before committing to a subscription.
Starter
For individual development, evaluation, and early prototypes.
Builder
For heavier testing, multi-mode workflows, and small teams.
Scale
For production evaluation and commercial pilot workloads.
Build once. Deploy everywhere. Trust every prediction.
Bring structured data. Start with 100 free calls, then scale into the same hosted runtime when your workload grows.