Vendor-agnostic AI for ESP reliability

Predict failures earlier.
Prescribe action faster.
Protect production.

Prevent $150K–$400K ESP failure events before they happen.

Sentinel Lift is a vendor-agnostic AI platform for ESP operations. It detects failure risk before alarms trigger and delivers operator-ready actions to reduce downtime and protect production.

Designed for real-world ESP operations • Built from field experience and reliability workflows

Up to 60 days
advance warning for emerging ESP failure risk, depending on signal quality and operating context.
$150K–$400K
potential avoided cost range per event when earlier intervention prevents failure and deferred production.
Vendor-agnostic
built to sit above existing ESP, SCADA, and historian environments instead of forcing rip-and-replace workflows.

Live risk detection across your well portfolio.

Top 3 At-Risk Wells

Smith 12-34Rising motor temp • falling intake pressure
Critical
Ranger A-16Current imbalance • sensor dropout
High
Mesa 8-02Repeated restarts • unstable load
Moderate

Forecast Horizon

Early Action Window

Estimated early intervention window based on multi-signal behavior, drift patterns, and operating context.

15–30 days

Risk Trend

20 wells
pilot deployment
3-layer system
ingestion • analytics • UI
Role access
operator & servicer views

Platform overview

Sentinel Lift brings data together across vendor environments and transforms it into earlier warnings, clearer failure interpretation, and practical next actions for operators, reliability teams, and production leaders.

📡

Unified signal ingestion

Connect data from existing SCADA, historian, and ESP monitoring sources into one cloud operating layer without forcing a single-vendor stack.

🧠

Failure prediction logic

Analyze multi-parameter drift behavior to detect emerging failure signatures before conventional alarms and reactive intervention windows.

🛠️

Prescriptive actions

Move from “something is wrong” to “what should I do next” with recommendations shaped around field reality and operator trust.

Why now

ESP failures are still managed reactively across fragmented systems. Alarm-driven workflows tell operators when the problem is already near or underway. Sentinel Lift introduces a new operating layer: predictive, prescriptive, and built for real-time field decisions before avoidable losses stack up.

Reactive today
Most environments still rely on lagging alarms, siloed vendor tools, and manual interpretation.
Production at risk
Every late intervention increases the chance of deferred production, equipment damage, and avoidable workover cost.
Data already exists
Operators already own the signals. The gap is turning them into earlier, trusted decisions.
AI timing
This is the moment to move from dashboards and alarms into decision support and prediction.

Advantage

Why Sentinel Lift stands out

Most operations still manage ESP risk through fragmented tools, lagging alarms, and vendor-specific blind spots. Sentinel Lift is built to close that gap.

  • Designed specifically for ESP failure prediction and production protection.
  • Built for real operating decisions, not just dashboard visibility.
  • Supports a learning loop by capturing whether recommendations were implemented and what happened next.

Product demo feel

Operator view
Servicer view
Action log

Final operator-grade refinement should feel more like software: role views, drill-down well cards, recommendations, and implementation tracking.

  • Clickable at-risk well queue.
  • Signal pattern and recommended next action.
  • Implemented / Not implemented learning loop.

Pilot path

Pilot-ready value proposition

Start with a focused well set. Prove earlier detection. Document interventions. Expand from evidence.

Sentinel Lift is positioned for pilot deployment with a practical path to demonstrating value: at-risk well identification, operator decision support, recommendation logging, and measurable business impact.

What an operator gets

  • Visibility into top at-risk wells across the monitored population.
  • Failure-horizon guidance to support timing of intervention.
  • A practical record of recommendations issued and actions taken.
  • Clearer narrative for avoided failures and protected production.

Investors

Sentinel Lift transforms fragmented ESP monitoring into predictive, prescriptive decision-making—unlocking earlier intervention, reduced failure costs, and a scalable intelligence layer across operations.

Current state

Reactive operations. Fragmented systems.

ESP performance is still managed through siloed vendor tools, alarm thresholds, and manual interpretation. Decisions are made late—when risk has already escalated into cost.

Sentinel Lift

Predictive visibility. Prescriptive action.

A vendor-agnostic AI layer that identifies failure risk earlier, translates signals into operator-ready actions, and continuously improves through real-world feedback loops.

Economic driver

  • ESP failures can result in $150K–$400K+ per event in combined intervention cost and deferred production.
  • Earlier detection increases intervention planning time and reduces total loss exposure.
  • Operators already have the data—the missing layer is decision intelligence.

Why this wins

  • Vendor-agnostic → no rip-and-replace barrier to adoption.
  • Built from field workflows → outputs operators can understand and act on.
  • Learning loop → system improves as actions are implemented and validated.

Scalable model

  • Subscription model per well with clear expansion potential.
  • Initial pilot → portfolio-wide rollout → multi-operator expansion.
  • Cloud-based deployment enables scaling without heavy infrastructure.

Long-term position

  • Becomes the intelligence layer above existing ESP systems.
  • Expands from failure prediction into optimization and production strategy.
  • Data feedback loops can increase accuracy and defensibility over time.

Next conversation

Ready to discuss a pilot or investment conversation?
Sentinel Lift is built for operators, partners, and investors who understand the cost of late detection and the value of earlier intervention in ESP-heavy production environments.