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Where iLeaf's enterprise work started.

iLeaf's enterprise practice began with oil and gas, and the sector still shapes how the company builds for the field: intermittent connectivity, expensive site visits and operators who will not tolerate a slow form. The work includes an AI-based solution for modern pumpers aimed at reducing Lease Operating Expenses, and ARMO, an asset monitoring product covering pipelines and distribution networks for energy, water and waste operators.

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What this includes

Field data capture
Fast entry built for gloved hands and bright sun — a floating keypad and attribute-level image capture, because a form designed at a desk fails at a wellhead.
Exception frameworks
Readings outside expected bounds raised at the point of capture, when the operator is still standing at the asset.
Asset & pipeline monitoring
ARMO: condition and status across distributed physical infrastructure, including pipelines and network assets.
LOE analysis
Lease Operating Expense broken down to where it is actually incurred, which is usually not where it is budgeted.
Augmented reality for field work
AR guidance for inspection and maintenance, grown out of the same enterprise practice.
Utility & municipal systems
Work extending to government electric and water authorities and waste management operators.

How we run it

Every phase ships something usable on its own, so you are never holding a half-finished system waiting on the next milestone.

  1. Design for the wellhead, not the office

    Every interaction is judged by whether it works one-handed, outdoors, wearing gloves, with no signal. Most enterprise UI fails that test.

  2. Catch the bad reading at the asset

    An anomaly found in a report next week costs a second site visit. Found at the point of capture, it costs thirty seconds.

  3. Cost per barrel, not per feature

    Success is measured against operating expense, because that is the number the operator is actually judged on.

  4. Assume the site is hostile

    No signal, no power, extreme temperatures and heavy gloves are the normal case, not the edge case.

What we build it with

  • Python
  • Swift
  • Kotlin
  • Offline sync
  • Computer vision
  • IoT telemetry
  • AR (ARKit / ARCore)
  • PostgreSQL
  • Time-series storage
  • Azure
  • Edge deployment
  • Power BI

Questions we get asked

What energy experience does iLeaf actually have?

The enterprise practice started in oil and gas. That includes an AI-based solution for modern pumpers built to reduce Lease Operating Expenses through faster field capture, attribute-level image capture and an exception framework, and ARMO, an asset monitoring product for pipelines and distribution networks used by energy, water and waste operators.

Will this work on sites with no connectivity?

Yes — it is designed for exactly that. Field applications capture and validate entirely offline and reconcile when a connection returns. Anything requiring live connectivity at the asset is unusable in this sector, so it is ruled out at design rather than mitigated later.

Can you monitor assets we already have instrumented?

Usually. ARMO and the surrounding work are built to read from existing telemetry rather than requiring new hardware everywhere. Where instrumentation is missing, we would rather start with the assets already reporting and prove the value before recommending capital spend.

Related

Let’s talk about energy, oil & gas.

Tell us what you are running and what it needs to do next. We will tell you honestly whether we are the right team for it.

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Selected work

Energy, Oil & Gas: the work behind this