Meet the Team Behind Tela™, the First Agentic-AI Assistant Built for Energy.

Somewhere on a rig or in an office, a geoscientist is working through well log data that would take a domain expert days to interpret properly. The knowledge exists — built over careers, locked inside people — but the tools to apply it at speed, consistently across every project didn’t.

Charu, Jing, and Diana wanted to change that.

Last April, the three Houston-based engineers entered an internal Hackathon with a brief: show what AI can do for the energy sector. Focused on real operational challenges, what they delivered was the first iteration of Tela™ the industry’s first agentic AI-assistant – now solving a problem the energy industry had never fully cracked.

Charu is an AI Program Architect, known across the team as the "AI Lady". Her work starts long before customers get to use the solutions. She builds the secure, governed infrastructure that makes it possible to deploy AI solutions at enterprise scale without cutting corners on safety or reliability.

She thinks in systems, data integration patterns, governance controls, and audit trails. "In our industry, mistakes have real consequences – safety incidents, environmental impacts, financial losses”. What Charu builds is accountability at the architecture level — so that when SLB customers rely on Tela for a decision that matters, the answer they get is one they can trust, verify, and defend.

Jing works at the layer where that foundation connects to the workflows customers actually use. She builds and maintains the reusable GenAI frameworks that allow Tela's capabilities to be embedded directly into the domain products engineers work in every day.

Diana is a data scientist with a geoscience background. She spent years alongside the engineers and geoscientists whose time gets consumed by manual, repetitive tasks like data analysis. She knows what it costs them. What she's helping build is designed to give that time back, in a way that actually matches how experts work.

Together, they cover the full stack, from the technical foundations to solving real customer needs. That's not a coincidence. It's an example of how teams at SLB work: architects, data scientists, and domain experts driven by common goals, supporting each other when it matters, and learning from each other every day.

That’s what we’re after at SLB—creating solutions using cutting-edge tech to solve real problems.