import torch, nn from transformers import LlamaForCausalLM class HPIPredictor(nn.Module): def __init__(self): self.lstm = nn.LSTM( input_size=12, hidden_size=128, num_layers=3 ) self.llm = LlamaForCausalLM.from_pretrained( "hpi-energy-3b" ) def predict(self, sensor_stream): pattern = self.lstm(sensor_stream) diagnosis = self.llm.generate( f"Equipment: COMPRESSOR_01" f" Pattern: {pattern.signature}" f" → failure_mode, days_out" ) return diagnosis # Live output: # failure_mode : VALVE_WEAR # confidence : 0.89 # days_to_event: 8 # recommended : order_kit_now
AI-POWERED PREDICTIVE INTELLIGENCE FOR ENERGY OPERATIONS

Know what's going to fail
before it does.

Highpoint Intelligence is a true AI SaaS platform — a PyTorch LSTM neural network combined with a large language model (LLM) running on your SCADA data. It names the specific failure, scores its confidence, and tells you how many days you have. No spreadsheets. No rule-based thresholds. No Excel logic dressed up as AI. A real model trained on your equipment — delivered as software, no infrastructure required.

Setup  A secure connection your IT team sets up once
Security  Read-only access, you keep ownership
CRITICAL ALERT
04:51 SAT
ASSETCOMPRESSOR #1
PREDICTED FAILUREVALVE_WEAR
DAYS TO FAILURE8
DISCHARGE TEMP314°F  ↑43%
SUCTION PRESSURE115 psi  ↓24%
AI CONFIDENCE89%
Diagnosis: Rising discharge temperature with falling suction pressure over 14 days matches the inlet valve wear signature. Order replacement kit now — emergency repair after failure runs 15–20x the cost of acting today.

Four steps. Nothing to babysit.

You don't have to log in and check anything. The platform watches, learns, and tells you only when something needs a decision.

01

A secure connection, set up once

Your IT team establishes a secure, read-only connection to your data source. Temperature, pressure, RPM, amps — whatever your sensors already capture.

02

It studies your equipment

A neural network learns what normal looks like for that specific machine — not a generic threshold.

03

It names the failure

Not "anomaly detected" — "valve wear, 89% confidence, 8 days out." Specific enough to act on.

04

It tells you what to do

Plain English, straight to your phone — what part to order, what it costs to wait, what to do today. A live dashboard view is also available.

It doesn't take PTO.

No lunch breaks, no sick days, no shift change. It watches your equipment every hour of every day — including the ones nobody's paid to be watching.

Predicting while you sleep

2 a.m. on a Saturday, it's still reading sensor data and running the model — same as 2 p.m. on a Tuesday.

No emergency login from the bleachers

You shouldn't have to leave your kid's baseball game to log into a dashboard. It already told you what's wrong — and what to do.

We do the heavy lifting

Setup, training the model, connecting the data — that's on us. You're not standing up infrastructure on top of running your operation.

Trained on your failures — not a generic anomaly script.

A lot of what gets sold as "AI" in this space is the same packaged machine learning script running for every client, in every industry. It flags a number that looks weird and calls it a day. That's not what's running here.

A

Rule-based tools & Excel thresholds

"Temperature exceeded limit." Static parameters set by a human, same logic for every asset. No learning, no diagnosis, no context — just a number that crossed a line.

B

What Highpoint Intelligence actually runs

A PyTorch LSTM trained on your equipment's real sensor history, paired with an LLM that generates a plain-English diagnosis — failure mode, confidence score, days out, and what to do. It learns. It adapts. It gets sharper over time.

One platform instead of a four-person build team.

Building this internally takes more than one hire — a data team, a pipeline, months of integration. Highpoint Intelligence replaces that build. Your IT team's role is simply setting up a secure connection.

 
Building it in-house
Highpoint Intelligence
Team required
Multiple specialized hires
A secure connection, set up once
Time to first alert
3–6 months
Days to a few weeks
Who maintains the model
Your team, ongoing
Retrains itself automatically
Output
Static dashboard your team has to monitor manually
Proactive alerts to your phone + live dashboard + AI Commander for instant field intelligence
Data access
Vendor controls the infrastructure
Secure, read-only — you control access
AI technology
Rule-based thresholds or generic Machine Learning scripts
PyTorch LSTM + LLM — real neural network, real language model
Delivery model
On-prem install or custom build
SaaS — no infrastructure, no IT project, no capital spend

Trained on the failures that actually cost you money.

Each model learns the specific degradation pattern for that equipment type — not a generic anomaly flag.

C

Compressors

Valve wear, bearing failure, seal degradation, cooling system decline.

E

ESPs

Motor degradation, cable faults, VSD anomalies before a costly pull.

R

Rod Pumps

Barrel, plunger, and valve wear from POC stroke and load patterns.

F

Facilities

Gas treating and processing equipment that can't run to failure.

This platform is not for everyone.

If your unplanned downtime is 0% and your emergency repair spend is $0 — this product is not for you.

We work with operators who know exactly what a surprise failure costs them — in lost production, emergency crews, and equipment damage. If that number keeps you up at night, we should talk.

That number keeps me up at night →

Ask your operation anything. Get an answer in seconds.

Commander is HPI's agentic AI interface — a large language model connected directly to your live equipment data. Your COO, field supervisor, or operations manager can ask plain-English questions and get instant, data-backed answers. No SQL. No analyst. No waiting on a report.

"What's my biggest threat in the field right now?"

Commander scans every asset in real time, ranks failure risk by severity and proximity to failure, and surfaces the one that needs attention today.

"How much would it cost if Compressor #3 goes down?"

Commander pulls predicted failure timeline, historical downtime data, and production loss estimates to give you a dollar figure — not a gut feeling.

"What's changed on the Permian assets this week?"

Commander summarizes sensor trend shifts, new anomalies detected, and alerts resolved — a weekly field briefing in one question.

An AI agent that thinks like your best engineer.

Commander is not a chatbot with canned responses. It is a fine-tuned LLM with live read access to your SCADA data, alert history, and asset registry. It reasons across all of it to answer operational questions the way a seasoned engineer would — with context, not just data.

HPI COMMANDER — LIVE SESSION
COO → Commander
"What's my biggest threat in the field right now?"
Commander →
Compressor #1, Permian Basin — Pad 7.
Valve wear signature detected.
Confidence: 89%  |  Days to failure: 8
Estimated downtime cost: $47,000
Recommended: Order valve kit today.

Let's look at one piece of equipment.

A 15-minute call. Bring one compressor, ESP, or rod pump you're watching closely. We'll show you what the model would have caught, what a pilot looks like under an NDA, and the options beyond phone alerts.

01Book a 15-minute call — no data shared yet.
02If it's a fit, we sign a mutual NDA before anything moves.
03Then, and only then, you send a sample export for a real analysis.
Request a call →
No data leaves your hands until an NDA is signed.