Connect. Measure. Improve.
Give your service a measurable objective. Send data through OMNIA, retrieve its NOUS evidence, then test changes against outcomes that matter to your users.
Celestial context is built in. Add an observation time to any service run. OMNIA attaches reproducible sky features; optional coordinates add local solar geometry. Start guided research →
Connection access
The key stays in this page’s memory and is cleared when you leave.Connect your service
Run data through OMNIA
Report what happened
One outcome per run. Keep units and measurement windows consistent.Is the change helping?
At least five outcomes per group unlock an exploratory comparison. Service-reported results are not proof of causation or independent verification.
Find the signal inside your data.
Turn measurements into a richer dataset. Test what helps, see what fails, and take the transformation with you.
Uses the connected service selected above. Comparisons stay private. Up to 2,000 rows and four selected numeric inputs per run; no paid APIs.
Upload a custom dataset
A flat table with observation time, acquisition episode, sensor/entity, numeric inputs and a measured outcome. Missing inputs may be blank; missing outcomes are rejected.
Keep repeated readings from one acquisition in the same episode. Inputs must be available before the outcome. The first 60% trains; the next 20% selects; the final 20% tests.
Inspect an audio signal
Decodes locally and inspects up to 262,144 samples of the first channel. RMS and frequency are waveform measurements. This does not create outcome labels or claim the audio was improved.
From measurements to meaning.
Choose a service, define the outcome, add real observations, and let OMNIA test what celestial context adds to ordinary operating conditions.
Connect a service above to begin.
What does success look like?
Your service supplies the outcome and whether higher or lower is better.
Add existing observations
Upload CSV or JSON, or paste rows. Use stable row IDs, actual observation times, measured outcomes, and real acquisition episode IDs. Repeated readings from one episode must share its ID. No synthetic data is supplied.
Your next finding starts here.
OMNIA will show predictive improvement, no demonstrated improvement, or exactly what data is missing.
Every new comparison uses later observations and new acquisition episodes. All completed comparisons remain in the history. Predictive improvement does not establish causation or trigger equipment changes.
Explore your connected dataset →Make every claim earn its place.
Compare what each feature group adds. Challenge apparent signals. Then freeze predictions and measure what happens next.
The public trial creates a separate, clearly named service using UCI Bike Sharing · H. Fanaee-T · CC BY 4.0. It is historical replay, not your data or a prospective result.
Complete a research comparison above, or try the public benchmark.
Which information helps?
Final-period mean absolute error. Lower is better. These are retrospective diagnostics on an already exposed dataset.
Predict first. Score later.
The candidate is selected using the selection period only. The frozen model does not retrain when outcomes arrive. Create a new measured run with operating conditions and a new acquisition episode, then record a forecast before its outcome is known.
After the target time, report the actual value using “Report what happened” above. OMNIA scores it automatically. No forecast means no prospective claim.
Celestial relationships are hypotheses. OMNIA descriptors are normalized measurements, not invented physical frequencies. An error reduction does not prove causation. Export data + results above includes the bench, frozen artifacts and forecast ledger.
Claim IntelligenceTrace the statement. Inspect the sources. Follow the outcome.
See what connects.
One continuous path from a public statement to reusable knowledge.
Give your data a track record.
Track a claim to see real sources and outcomes connected here. No example findings are mixed into your data.
Owner-reviewed outcomes · Connections show recorded lineage, not causation. Motion is decorative; it does not represent measured EFVT or live transfers.