Overview

This global energy leader runs drilling programs across 14 countries. With over 2,000 engineers making operational decisions daily (on well status, event history, job performance) fast, reliable access to drilling data wasn't simply a nice-to-have; it was an operational necessity.

The problem wasn't a lack of data infrastructure. They had WellView, Snowflake on Azure, and Power BI. The problem was that getting to an answer still required an analyst or a SQL query and frontline teams were unable to access the data themselves when they needed it.

The Challenge

Analyst and SQL dependency: Routine operational questions around basic well status, event history, and performance comparisons required analyst involvement or SQL expertise. This created delays across a global operation where speed mattered.

Context scattered across sources: Structured drilling data lived in WellView and Snowflake but operational guidance lived in thousands of pages of drilling manuals. No existing tool was capable of bridging both in a single workflow, so engineers were forced to manually cross-reference systems to get the full picture.

Static dashboards couldn't keep up: Ad-hoc follow-ups didn't fit dashboard architecture. When a new question arose mid-analysis, teams were forced to default to one-off analyst pulls and slow iteration, exactly the bottleneck they were already attempting to solve.

The Evaluation

Over the course of six months, the energy company evaluated leading AI and LLM vendors and even crowdsourced real-world questions from their user base to stress-test each potential tool and even built an in-house solution. None of the them were capable of delivering the level of accuracy or domain understanding required.

Most tools plateaued well below the accuracy threshold needed for engineers to trust the answers. Domain-specific data (WellView schemas, drilling terminology, operational context) exposed the limits of general-purpose AI tools that weren't built to handle complex, multi-source enterprise environments.

WisdomAI was the only solution that delivered.

“We looked everywhere—from top AI vendors to internal tools. Nothing came close to working well. WisdomAI was the only one that actually understood our data and delivered what we needed.”

The Solution

WisdomAI gave drilling teams a single interface to ask operational questions in plain English, with no SQL, no analyst, no waiting. By connecting directly to the Snowflake data warehouse on Azure and incorporating unstructured guidance from drilling manuals, engineers could ask questions and follow threads of analysis in real time.

WisdomAI's Enterprise Context Layer is what made it work.

It learned how the organization defined and utilized its drilling data (the domain-specific terminology, the metrics that matter, the way engineers actually ask questions) and used that context to deliver accurate, trusted answers at scale.

What this enabled:

• Natural language queries across complex drilling datasets, no SQL required
  
  

• Structured + unstructured reasoning — live data and drilling manuals in one workflow
  
  

• Direct connection to Snowflake on Azure for WellView-derived data
  
  

• Enterprise-ready deployment — WisdomAI-hosted (SOC 2 Type 2) or self-hosted, with dedicated support for query optimization and model tuning

Operational excellence through conversational analytics: the path forward

By reducing time-to-insight and empowering operators directly, WisdomAI helped this global energy leader unlock faster decisions, and real ROI, from its drilling data.

The WisdomAI solution delivered:

• 50% accuracy improvement over the closest competitor (including their own in-house build) on real operational queries sourced from the drilling teams themselves.

• 2,000+ engineers enabled with self-serve access to drilling insights across 14 countries.
  
  

Want to learn how WisdomAI can bring natural language data access to your operational teams?

Book your demo today!