exOS · AI-native software for operations

Run your operations on software that reasons.

Exlens gives you two things: a reasoning layer that sits on top of the systems you already run — EAM, SCADA, historians, ERP — and a set of AI-native modules you can deploy on their own. Predict failures, trace root cause, optimize every schedule. Keep your stack.

Built for:

Manufacturing

·

Utilities & grid

·

Water & wastewater

·

Energy & pipelines

·

Facilities & infrastructure

·

Heavy industry & process

How it fits together

One reasoning layer.
Modules that stand on their own.

Exlens never inserts itself between you and your data. SureLock reasons across whatever you have — your systems of record, our modules, or both. The modules are standalone AI-native products that run alongside your stack, not in the middle of it.

SureLock — the AI reasoning layer

Sits on top of everything below. Predicts, traces, and optimizes across every source.

exOS modules

AI built in

Maintenance · CMMS/EAM

Inventory

Manufacturing Execution

Quality · QMS

Your existing systems

unchanged

EAM / CMMS (Maximo, SAP PM)

SCADA

Historian (PI)

MES · ERP · GIS

Modules run in parallel with the systems you own. SureLock sits on top of both.

Two ways to buy

Start with the layer.
Or start with a module.

Both are AI-native. The only question is where you want intelligence first — across the systems you already run, or in a capability you’re ready to modernize.

Path 01 · The AI layer

SureLock, over your existing systems.

Keep Maximo, SAP, your SCADA and historian. SureLock connects across them and turns them into one system you can question — failure predictions, traced root cause, optimized schedules. Nothing gets replaced or migrated.

Best when: your systems work, but the answers are buried inside them.

Path 02 · AI-native modules

exOS modules, with AI already inside.

Modernizing maintenance, inventory, execution, or quality? Deploy an exOS module on its own. The intelligence isn’t an add-on; it ships in the product. Each module runs standalone and connects to the rest when you’re ready.

Best when: you need new capability fast, without a multi-year rollout.

Take both, and one reasoning layer spans your exOS modules and your legacy systems alike.

What “AI-native” actually means

Most software stores your data. Exlens reasons about it.

Both are AI-native. The only question is where you want intelligence first — across the systems you already run, or in a capability you’re ready to modernize.

Systems of record answer:

How many work orders closed last month?

A count. Whether the right work got done is anyone’s guess.

exlens answers

Which assets fail next quarter — and what does waiting cost?

A ranked list, the predicted failure window, and the cost of doing nothing.

the reasoning layer

Three things it does across every system you run.

SureLock is the engine inside every exOS module — and it runs just as well on top of the EAM, SCADA, historian, and ERP you already have. It learns how your assets behave, follows cause through the chain, and shows its work.

Failure & defect prevention

Predict which assets are trending toward failure — transformers, breakers, pumps, pipeline segments, production equipment — and act before they cause an outage. SureLock learns each asset’s normal behavior and flags the drift early.

Root cause analysis

When something fails, trace it to its true source across SCADA logs, sensor history, asset records, and work orders — not the symptom that tripped the alarm. Recurring faults get linked back to one underlying cause.

Scheduling optimization

Plan maintenance windows, outages, and crew dispatch against real constraints — asset risk, load, weather, crew availability, spares on hand. SureLock optimizes the plan and updates it as conditions change.

1.understand

2.Gather

3.Reason

4.Answer

Utilities

You ask

Why does Feeder 7 keep tripping?

It answers

Substation 3 / Feeder 7

Momentary outages rose 2.8× over six weeks. Root cause: insulation aging on a legacy cable section, pushed past thermal limits by the load shift after the Feeder 4 reconfiguration — tripping under peak afternoon load.

Traced through

SCADA trip logs → post-switching load profile → asset register & cable age → 22 momentary events, 18 sharing one thermal-stress signature.

Prioritize the cable section in the summer window; rebalance load to Feeder 9 meanwhile. Est. resolution: 70–80% of momentary outages on Feeder 7.

Manufacturing

You ask

What's going wrong at Station 12?

It answers

Station 12 / Rear Door Assembly

Gap-tolerance failures rose 3.2× over 72 hours. Root cause: fixture calibration drift at Station 9 shifted B-pillar alignment by 0.4 mm, pushing door-seal compression out of spec on 34% of units.

Traced through

Station 9 fixture logs → B-pillar dimensional data → Station 12 gap readings → 47 logged defects, 43 sharing one root signature.

Recalibrate Station 9 fixture. Est. resolution: 80–90% of current defect volume.

The modules

Or deploy an AI-native module on its own.

Each module is a complete product — built around the same reasoning engine, not reporting bolted on top of one. Some span every industry; some are built for production sites. All are AI-native.

Asset health / prediction view

Maintenance · CMMS / EAM

Assets, work orders, PM programs, and full history in one AI-native system. It predicts which assets fail next from sensor and historian data, quantifies the cost of an outage, and schedules around real risk — not a fixed calendar.

Ask it:

Which assets fail next, and what does the outage cost?

Inventory / spares view

Inventory management

Stock, lots, and consumption tied to the work that uses them — production materials and MRO spares alike. It sees shortages before they delay a job and flags critical parts quietly draining capital. Reordering follows real demand, not fixed min/max.

Ask it:

Which assets fail next, and what does the outage cost?

Live line / station view

Manufacturing execution

Live work orders, routing, and unit-level traceability for production sites. It spots the constraint forming before it stalls the line — and names the station and the reason, not just the slowdown.

Ask it:

Why is Line 3 slower than the same shift last week?

Defect cluster / root-cause view

Quality · QMS

Inspection, defects, NCRs, CAPA, and FMEA tied to the unit. It finds the one root cause behind a cluster that looks unrelated, surfaces every unit sharing it, and drafts the CAPA with evidence attached.

Ask it:

Which assets fail next, and what does the outage cost?

Bring us a real problem.

We’ll trace it live — across your systems, our modules, or both.

Bring us a real problem.

We’ll trace it live — across your systems, our modules, or both.

Bring us a real problem.

We’ll trace it live — across your systems, our modules, or both.

Copyright © 2026 Exlens AI. All rights reserved.

Copyright © 2026 Exlens AI. All rights reserved.

Copyright © 2026 Exlens AI. All rights reserved.