See emerging risk sooner
Detect coordinated changes that can precede a larger incident—not only the final symptom that pages your team.
Predictive infrastructure intelligence
Built for OpenTelemetry
See failure taking shape
Detect failure before it becomes an outage.
Quantis learns the normal relationships across your infrastructure and detects when they begin to break down—giving your team an earlier signal, a clearer place to investigate, and more time to act before customers are affected.
LIVE SYSTEM / 04:17:26 UTCCoordinated drift detected
Early warningLatency
Queue
Workers
Intervention window
Works with the signals you already collect
- OpenTelemetry-native
- No failure labels required
- Signal-level evidence
The warning is already in your telemetry
Most outages do not begin all at once.
They emerge across queues, workers, caches, databases, latency, and demand. Each signal can look harmless in isolation. By the time a conventional alert fires, the failure may already be spreading.
Conventional monitoring
Quantis
Watches individual thresholds
Learns relationships across the system
Reacts to known failure conditions
Detects unfamiliar structural drift
Alerts after a metric crosses a limit
Surfaces coordinated change as it emerges
Shows what is high or low
Ranks the signals driving the warning
Turn weak signals into time to act
Find the failure while it is still containable.
Start with the right signals
Rank the parts of the system that diverged most from expected behavior, so responders know where to begin.
Catch failures without rules
Learn healthy behavior from telemetry instead of writing a new threshold for every possible failure mode.
Predictive models for system health
Quantis learns what should happen next.
Normal-state learning + intervention dynamicsTime to act is the product.
Quantis learns healthy behavior, then tests whether action-conditioned dynamics can explain how a local change moves through the system.
- 01
Learn normal operation
Quantis learns from healthy telemetry across the services and infrastructure you already observe.
- 02
Predict what should happen next
From recent context and current demand, the model predicts how system signals should behave together.
- 03
Detect coordinated divergence
When multiple signals depart from their predicted state, Quantis surfaces an emerging-failure warning.
- 04
Show responders where to look
Every warning includes ranked signal-level evidence for faster investigation and safer intervention.
From surprise outage to intervention window
The cache has not failed yet. The system has already changed.
Traffic looks normal. Error rate is still below its alert threshold. But cache behavior, database writes, queue depth, and worker throughput are beginning to move out of alignment.
Quantis recognizes the change while failure is still emerging, so your team can investigate, shed load, isolate a dependency, or roll back before a localized problem becomes a customer-wide incident.
01Healthy system
02Drift begins
03Quantis warns
04Outage avoided
Action-conditioned dynamics
See how a local change becomes a system-wide incident.
Choose an intervention. Quantis forecasts its effect, follows the pressure through the system graph, and searches for the action that best explains the observed future.
Synthetic instrumentation proofThis validates the scientific interfaces—not performance on a running or production system.
99.966%Forecast improvement vs action-agnostic
100%Joint action identification
61Candidate interventions searched
5 / 5Propagation delays in graph order
PHASE 0 / ACTION DYNAMICSCounterfactual incident laboratory
Advance to instrumented pilotSelected interventionAPI rejection
Admission pressure begins at the API, then moves through every downstream dependency.
- Target
- API
- Onset
- T=10
- Magnitude
- 0.50
Action enters rolloutT+2
Propagation clockPressure moves through the declared graph.
Future step 1 / 8SubsystemAPIWithin controlAction target
RelationshipEnqueueWithin control
SubsystemWorkerWithin control
RelationshipDequeueWithin control
SubsystemPostgresWithin control
Confirmed synthetic chain order+1 → +2 → +3 → +4 → +5
Matched twinsOne intervention. Everything else held equal.
TreatmentControl
Forecast raceKnowing the intervention changes the future.
ObservedAction-conditionedAction-agnosticPersistence
Intervention fingerprint61 candidates searched without reading the manifest.
Hit@1 · 100%- 01
api_rejection@source · onset 10 · magnitude 0.50Best explanation - 02
api_rejection@source · onset 11 · magnitude 0.50ΔNLL +227.8 - 03
api_rejection@source · onset 11 · magnitude 0.75ΔNLL +362.0 - 04
api_rejection@source · onset 10 · magnitude 0.75ΔNLL +509.9
Action-conditioned0.000262
Action-agnostic0.765567
Persistence1.116520
Lower is better. All ten synthetic validation twins scored.
Live early-warning model
Make the system drift.
Change the next telemetry window and watch Quantis compare the system's behavior with what it expected to happen. The exact frozen detector runs locally in your browser.
Interactive demonstrationSwitch between the learned JEPA v0 and frozen linear v2 model. These scenarios are counterfactual; development and held-out evidence are presented separately below.
MODEL / 2516dadd82b9
Learned JEPA anomaly model v0
Live decisionNormal
Rolling inference · 18/1000Paused for inspection
One step is one completed telemetry window. Playback speed controls the animation only; the artifact defines no wall-clock cadence. Playback starts in view and pauses offscreen.
Edit temporal context6 context + 1 target
Anomaly score historylog scale · threshold 0.871
The learned anomaly model
Trained on normal. Tested on schedules it never saw.
JEPA v0 learns a four-dimensional representation of healthy telemetry, predicts the next latent state, and scores the gap between prediction and observation. The corpus keeps entire workload families out of training.
30Fresh normal-only runs
10Workload families
10,020Run-isolated windows
1–3Worker topologies
Corpus split
Eight families learn. Two stay unseen.
Training Validation
FamilyScheduleW1W2W3
F01
5 · 6 · 4123F02
6 · 8 · 5 · 7123F03
5 · 7 · 8 · 7 · 6123F04
8 · 9 · 10 · 7123F05
6 · 9 · 11 · 8 · 10123F06
10 · 8 · 11 · 13 · 9123F07
7 · 5 · 8 · 6 · 4123F08
7 · 10 · 8 · 6 · 9 · 8123F09
8 · 10 · 13 · 11 · 12123F10
8 · 12 · 15 · 10 · 13 · 12 · 14123The model learns. New schedules remain harder.
Training 2.0%
Unseen schedules 17.5%
Validation loss was 0.481 versus 0.249 in training. This is the calibration gap the next confirmation must close.
45.8%loss reduction over 300 epochs
0.4590.249
- Repeat training produced a byte-identical artifact
- Validation windows never entered model fitting
- Existing fault evidence remained reserved
Start with your telemetry
Find out what your current alerts cannot see.
Run Quantis alongside your existing observability stack. Learn a baseline from normal operation, replay known incidents, and measure whether the model creates useful warning time before your current alerts.
- Measure additional intervention time
- Identify the most useful investigative signals
- Calibrate alerts for your workload and topology