About

About Genesis Lone Star

Genesis Lone Star helps industrial companies turn operational data into practical business value. The company began in advanced drilling analytics and is now expanding into broader industrial analytics, operational data strategy, connected-worker workflows, and targeted industrial AI support.

Origin

Founded in drilling analytics

Genesis Lone Star was founded in the early 2000s by Dr. Eric Maidla to provide advanced drilling analytics services to the oil and gas industry.

That original focus reflected a practical belief: better operational decisions come from better data, rigorous engineering judgment, and analytics grounded in the reality of industrial work.

Dr. Maidla's public industry record includes drilling automation, drilling performance measurement, petroleum engineering leadership, patents, SPE involvement, and recognition for inventions that contributed to drilling performance and automation.

Transition

A new chapter under Richard Maidla

Ownership of Genesis Lone Star has now transitioned to Richard Maidla. With that transition, the company is expanding its focus from drilling analytics into broader industrial analytics and AI support for asset-intensive companies.

This next chapter builds on the company's original foundation while addressing a larger challenge: helping industrial teams turn fragmented operational data into trusted workflows, usable analytics, and targeted AI use cases that create practical business value.

Meet Richard Maidla

Industrial data leadership with operations fluency

Richard Maidla is a petroleum engineer by training with 15+ years of experience across oil and gas operations, engineering, data, analytics, and digital transformation.

His career spans field completions, production operations, process safety, offshore operations, and data and analytics leadership. Across those roles, he became known for translating between business teams, IT, OT, vendors, engineers, and executives.

Richard's work has focused on reducing workflow waste, democratizing data access, building trusted operational data foundations, and helping teams adopt analytics and AI in ways that fit real operations.

Experience

Experience turning operational data into working systems

In prior corporate roles, Richard helped scale operational data platforms, connected-worker workflows, Power BI adoption, industrial knowledge graphs, SAP integrations, and GenAI/RAG patterns across complex offshore operations.

15+ Years

Bridging engineering, operations, IT, OT, vendors, and advanced analytics.

30k+ Time Series

Autonomously contextualized as part of prior corporate operational data work.

120k+ Documents

Brought into operational data foundations in representative prior roles.

4.3M+ Events

Contextualized alongside approximately 600k tags for 300+ users.

750/mo Digital Checklists

Scaled in connected-worker workflows, with more than 19k completed tasks.

$12MM OPEX Savings

Supported a Zero-Based Budget analytics effort associated with this outcome.

Point of view

Industrial AI should start with the work, not the model

Before a company can scale AI, it needs trusted data, clear ownership, operational context, and users who can verify and apply the output.

Sometimes the answer is AI. Sometimes it is a better dashboard. Sometimes it is a cleaner data model, a digitized field workflow, or a clearer operating rhythm.

The goal is not to use the most advanced tool. The goal is to solve the right problem in a way the organization can trust, adopt, and scale.

Why Genesis exists

Practical analytics, targeted AI, and real adoption

Too many industrial data and AI initiatives sound impressive but fail to create lasting value in the field. Genesis Lone Star exists to close that gap with senior-level guidance, practical analytics, targeted industrial AI, and a delivery approach built around business value and real adoption.

Discovery

Practical industrial analytics. Targeted AI. Real adoption.

Start with a practical conversation about where the friction is, what the data can support today, and where analytics or AI can create real business value next.

Start a Discovery Conversation