Know your machines are failing before they fail.
OSREP units read the signals your equipment already produces and return one continuous machine state: nominal, degrading, or fault, with the input that drove the change. Inference runs on site. Raw data never leaves your site.
A pressure swing and a failing bearing can look the same.
Most monitoring treats normal as a fixed signature. It isn't. Normal moves with load, ambient temperature, and whatever the process is doing. A threshold set in January trips on a hot afternoon in August, the alarm says fault, and the machine is fine.
False alarms kill the tool
Operations gives a monitoring device a few chances. After the third alert that turns out to be a process change, every alert gets ignored, including the real one. A system that can't tell process from mechanics ends up unplugged.
Cloud models learn slowly, somewhere else
The usual fix is to ship waveforms offsite and let a model accumulate a season or more of history per asset. Your operating data lives behind a vendor's paywall while it learns, and stays there if you leave.
The data already exists. The review doesn't.
Historians hold years of vibration, acoustic, and process data that nobody has time to read at fleet scale. The failures are usually in there, visible in hindsight. Hindsight is the expensive kind.
Model normal as a function of conditions. What's left is mechanical.
An OSREP unit doesn't learn one baseline. It learns what the signal should look like given the operating context the other sensors report, then subtracts that prediction from what it observes. The residual is, by construction, the mechanical component. A process swing moves the prediction. A failing bearing moves the residual. The unit tells them apart because it never mixes them.
Step 1
A unit at each machine
It reads the signals already there, from the machine's own sensors and monitoring outputs, and runs a first-pass state on the spot. No new sensors, no rip and replace.
Step 2
A site node for the whole fleet
Units report to one site node over long-range radio. It combines their readings with plant-wide operating data and hosts your dashboard. It is the only part of the system that connects to your network. Commissioning takes weeks, not a season, because the model starts from the physics of the equipment class.
Step 3
A state, with a cause
Each machine returns nominal, degrading, or fault, with confidence and time in state. When the state changes, the system names the input that drove it, and whether the cause is the machine itself or the process feeding it.
A state, not a stream.
Raw data stays on site. What travels upstream is already an answer.
Continuous machine state
Every machine gets a rolling state with a confidence value and time in state, shown on a dashboard hosted on site. Connections to your SCADA, historian, or operations platform are scoped with you per site. If nothing changed, nothing needs you.
Attribution with every change
A state change arrives with the input that drove it and by how much. Cause, not just symptom.
degrading: acoustic up 40%, temperature nominal, vibration nominal, upstream process steady. That reads as a bearing starting to go, not the process.
Where this stands, plainly.
Early-stage hardware companies usually hide this section. We'd rather you read it here than discover it on the first call.
- Hardware
- Prototype built. On-unit inference is in active development on the bench in Baton Rouge. We are not shipping production units yet.
- Certification
- Not yet rated for Class I, Division 1 or 2 areas. That scopes current work to non-classified balance-of-plant and remote ancillary assets. Hazardous-area certification is on the roadmap, and we know it gates most classified facilities.
- Early partners
- Recruiting now, in two shapes. A data engagement that starts from a historical export out of your historian, so the model proves itself on your assets before any hardware ships. Or a small on-site deployment at non-classified monitoring points. Both start with the form below.
- Company
- OSREP LLC, founded 2025, Baton Rouge, Louisiana. Meet the team.
Tell us what you're monitoring.
We'll reply with a short, specific assessment: whether OSREP fits your assets, what a deployment looks like at your site, and which of your existing sensors we'd work with.
What you'll get back. A reply from the founding team within two business days. No sequence, no list, no follow-up cadence.
Who you'd be working with.
Two founders build and sell the product, with a third on cybersecurity and nine industry and academic consultants behind them.
Garrett Wainright
Chief executive, co-founder
Runs the commercial side and sets product direction: customer and data-partner conversations, and what the unit has to prove at a site before anyone buys it. Reads every request that comes through this page.
Economics degree from LSU, now a master's in computer science there, and a Dean's AI Fellow in the College of Engineering. Before OSREP he led a student engineering team on a rail yard tracking automation project for Shell and built product prototypes at Pellerin Technology.
Andres Borer
Co-founder, embedded systems lead
Builds the Orca unit: firmware, sensor interfaces, and the protocol layer that moves a machine state off the hardware and into your plant systems.
Honors computer engineering at LSU. Before OSREP he wrote embedded networking firmware for marine displays at Controles Nacionales in Panama. On LSU's rocketry team he fuses IMU and barometer data to hold a clean state estimate under heavy vibration, the same problem a sensor on a running pump has.
Want to know whether OSREP fits your assets?
Request a site assessment