Systems & AI in the field

Transforming WellTender from calendar‑driven to exception‑driven.

Hypergiant partnered with Chesapeake Energy to evolve an internal tool into an AI‑augmented platform for field work, routing, and incentives—producing measurable savings across five regions.

Role: Product strategy, experience design, AI application framing Duration: 18 months (discovery through delivery)

Impact

Headline result

$85K/day

Estimated savings by shifting to exception‑based maintenance and AI‑driven routing at scale.

Plus unexpected gains in team visibility and performance transparency via Hypergiant’s “Intelligent Incentives” layer.

Client Testimonial Featured

I met Ross as part of my initial introduction to Hypergiant. Ross’ facility with artificial intelligence applications was among the reasons that we engaged Hypergiant to collaborate on the WellTender application. He was intimately involved with every aspect of the project and was creative and open‑minded throughout. Even when development activities hit a snag, Ross was quick to take ownership of the challenge and we were swiftly put back on track. He is a solid communicator and an excellent relationship‑builder, although his approach is non‑standard. We have maintained a friendly relationship since the conclusion of the project and I can recommend Ross without equivocation.

Kentaro Kawamori CRO & Co‑Founder, Persefoni; formerly Chief Digital Officer, Chesapeake Energy

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CONTEXT

From calendar‑based maintenance to exception‑driven field operations.

Chesapeake’s original WellTender broke the tyranny of the calendar. Hypergiant’s brief: expand it, modernize it, and thread AI through the entire workflow.

Calendar‑based maintenance of resource sites is extremely inefficient and requires service personnel to travel to and from work sites irrespective of actual need. To address this problem, internal teams at Chesapeake Energy created WellTender to manage work tickets at sites on an exception basis.

When the time came to evolve the application, expand functionality, and introduce artificial intelligence into the solution, Chesapeake Energy partnered with Hypergiant. Providing visibility into team‑based work and delivering performance transparency were two unexpected benefits for Chesapeake—and each became a component of Hypergiant's Intelligent Incentives program.

Chesapeake Energy developed the original iteration of WellTender from a user‑centric perspective, so partnership with Hypergiant was natural and the relationship extremely productive. Collaboration across more than a year was intense and fruitful, allowing for creation of a truly future‑forward manifestation of the original promise of WellTender.

SOLUTION

WellTender 2.0: a multi‑modal AI layer for the field.

The evolved platform weaved unsupervised learning, computer vision, NLP, sentiment analysis, and routing intelligence into one coherent field‑service tool.

Intelligence across the lifecycle

Across vastly expanded capabilities, WellTender 2.0 incorporates multiple forms of artificial intelligence. Unsupervised learning is employed in analyzing maintenance and historical production, surfacing patterns and opportunities that weren’t visible in manual reports.

Computer vision is utilized for well information gathering and transmission, eliminating the need for every site employee to spend hours each week traveling to a central office, scanning, and submitting documents to a central repository.

Natural Language Processing (NLP) and sentiment analysis are leveraged to review user comments for efficacy, coupled with user‑driven supervised learning to keep the system tuned to the realities of field work.

Right work, right person, right route

Algorithmic work‑item matching ensures that the most appropriate employee addresses site challenges according to their aptitudes, location, and past performance. This improves both effectiveness in the field and perceived fairness of work assignment.

Most significantly, AI‑driven routing and telemetry ensure that site issues are addressed via the most efficient travel pathway. The result: fewer wasted miles, faster resolution times, and direct daily savings at scale across five regions.

Finally, the Intelligent Incentives program tied together all of these capabilities into a format that was intuitive, graceful, transparent, and user‑focused—making system behavior visible, and giving crews clear line‑of‑sight into how their performance connected to outcomes.

OUTCOMES

$17K/day in five different regions, and a new lens on field performance.

Beyond the daily savings, WellTender 2.0 gave Chesapeake a shared, data‑driven view of work, performance, and incentives in the field.

The shift from calendar‑driven to exception‑driven work, combined with AI‑driven routing, produced an estimated $17K per day in five different regions. Just as importantly, it surfaced a live picture of how teams were working, where time was lost, and where incentives were misaligned.

The Intelligent Incentives program created a user‑facing layer over complex AI capabilities, giving foremen and well site engineers something simple: clarity. Clarity about which work mattered most, where they could have the most impact, and how the system “saw” them.