Prescient One — Predict Hardware Failures 30 Days Before They Happen
PREDICTIVE INFRASTRUCTURE INTELLIGENCE

Stop Reacting.
Start Predicting.
30 Days Early.

The AI era is built on hardware that most operators still manage reactively. Prescient One changes that — know which drives and SSDs will fail before they do. Built and validated across 115 million real-world drive records spanning four quarters. Accuracy verified on data the model never trained on.

93%+
Prediction Accuracy
30
Days Advance Warning
115M
Records Validated
4
Prediction Horizons

Your monitoring tells you what already broke.
Not what's about to.

SMART monitoring was designed for desktop PCs in 1994. The doctrine hasn't changed — but the stakes have. You're running it on petabyte-scale AI infrastructure during the largest data center buildout in history. Every operator without predictive intelligence is gambling with millions.

The 3AM Scramble

Drive fails at 3AM. Cascading rebuild across your RAID array. Emergency procurement at 5× markup. Your team is firefighting when they should be building.

Average cost per unplanned failure event: $50K–$500K
📉

Silent Degradation

SMART reports "healthy" on drives that fail 48 hours later. By the time SMART catches it, you're already in the blast radius. Reactive monitoring is a rear-view mirror at highway speed.

SMART catches <5% of imminent failures in time
🔥

The Scale Problem

10,000 drives. 50,000 drives. At hyperscale, statistical certainty means dozens fail every month. The question isn't if — it's which ones, and can you replace them before they take data with them.

Hyperscale operators lose $10–25M annually to failures

From reactive to predictive
in 90 days

Prescient One layers predictive intelligence on top of your existing infrastructure. No rip-and-replace. No agents on production servers. Works with what you have.

Ingest Telemetry

Prescient One ingests SMART data and operational telemetry from your existing monitoring stack via API. No new hardware. No agents on production servers.

Correlate Signals

Our models analyze 50 engineered SMART features per drive — error rates, sector reallocations, power cycles, drive age, and 7-day and 30-day degradation trajectories — to identify failure signatures invisible to threshold-based monitoring.

Predict & Alert

Receive prioritized risk scores across four time horizons: 24 hours, 7 days, 14 days, and 30 days. Schedule replacements during maintenance windows at standard procurement pricing. Zero panic. Zero downtime.

Built differently.
Validated rigorously.

Most predictive tools use raw SMART values and simple thresholds. Prescient One was built from the ground up on real failure data, engineered features, and multi-horizon prediction.

Signal Engineering

50 Engineered Features — Not Raw Thresholds

Traditional SMART monitoring checks 3–4 raw values against static thresholds. Prescient One engineers 50 features per drive — including rolling 7-day and 30-day delta signals and drive age trajectories — to detect degradation before it becomes failure.

Multi-Horizon Prediction

Four Warning Horizons in a Single Pass

24-hour CRITICAL. 7-day WARNING. 14-day WATCH. 30-day ADVISORY. Four separate models, scored simultaneously, so your operations team can plan replacements weeks in advance — not hours.

Manufacturer Intelligence

Seagate, WD, HGST, and Toshiba Fail Differently

Prescient One models failure patterns by manufacturer. A Seagate degradation signature looks nothing like a WD one. Manufacturer-aware modeling means fewer false positives and earlier true detections across your mixed fleet.

Verified Generalization

Tested on Data the Model Never Trained On

Any model can perform on its training data. Prescient One's accuracy was independently verified on a completely unseen quarter of production data — a different drive population, a different time period, a different failure distribution. The numbers held.

See what prediction looks like

This is what your operations team sees instead of a 3AM phone call.

prescient-one — fleet-monitor — rack-a4
prescient fleet status --facility dc-north-01

Fleet Health Report — DC-NORTH-01 — 2026-03-29T06:00:00Z
─────────────────────────────────────────────────
Total drives monitored: 12,847
■ HEALTHY 12,591 (98.0%)
■ WATCH 211 (1.6%)
■ PREDICT FAILURE 45 (0.4%)

⚠ CRITICAL — 3 drives predicted to fail within 7 days:
─────────────────────────────────────────────────
RACK-A4-SSD-0117 Seagate Nytro 3732 FAIL in ~4 days confidence: 94%
RACK-C2-HDD-0891 WD Ultrastar HC570 FAIL in ~6 days confidence: 91%
RACK-D1-SSD-0234 Intel D7-P5620 FAIL in ~7 days confidence: 88%

✓ Replacement orders auto-generated for procurement
✓ Maintenance window scheduled: 2026-04-02 02:00 UTC
✓ Estimated savings vs reactive: $127,400

prescient

Built on evidence.
Verified on data we never trained on.

These numbers come from real production data — the Backblaze enterprise drive dataset, the gold standard for storage reliability research. Every metric below was measured, not projected.

115M
Real-World Drive Records
Built and validated across 115 million real-world drive records spanning four quarters of production data — Q4 2024 through Q4 2025. Over 400,000 individual drives across all major manufacturers.
93.63%
24h Prediction Accuracy (AUC-ROC)
93.63% AUC-ROC at 24h, 91.13% at 14 days, 86.27% at 30 days — all measured on Q2 2025 holdout data the model was never trained on. These are not training scores. These are real generalization numbers.
Q2 2025
Independent Holdout — Never Seen
An entire quarter of production data — April through June 2025 — was withheld from training entirely. The model had never seen it. Performance verified against this population, new failure distributions, and a completely different time period.
400K+
Individual Drives Validated
Validated across 400,000+ individual drives representing Seagate, Western Digital, HGST, Toshiba, and other enterprise manufacturers operating in real production data center environments.

GPU & TPU Failure Prediction

The $450 billion AI infrastructure boom is built on hardware that no one can predict. Meta, Google, and every AI lab in the world is flying blind on GPU reliability. We're changing that.

Prescient One's methodology extends from storage drives to accelerators. The same physics apply — thermal cycling, electromigration, capacitor degradation. Our storage prediction work is the foundation. No commercial competitor exists in this space.

30.1%
of Meta's Llama 3 training disruptions were caused by GPU failures. No prediction system existed.
$450B
AI infrastructure investment globally — with zero predictive intelligence for the hardware it runs on.
0
Commercial competitors in GPU/TPU failure prediction. The market is open.

Ready to Stop Reacting?

Prescient One is engaging with select critical infrastructure operators. If you manage data center hardware at scale — government, enterprise, or commercial — request a capability brief and let's talk specifics.

30 days of warning. Zero days of panic.

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