Analysts spent 5 days coding reports. Now, business users query millions of records in plain English for instant results.
A North American infrastructure provider supplying 33% of United States natural gas through 30,000 miles of pipelines while managing millions of transaction records.
Centralized analysts were overwhelmed by data requests, often spending up to a week coding and validating complex reports for executives. This...
“A data request that previously took an analyst five days to complete was entered into Genie—including all the bullet points—and it returned results in just seconds.”
Natural gas processing and transportation infrastructure company.
Databricks is a Big Data company that offers a unified analytics platform for data science, engineering, and analytics teams.
Williams's Self-service analytics is part of this use case:
Related implementations across industries and use cases
Most employees couldn't reach the data without an analyst. Refinery experts now build soft sensors through conversation, no code needed.
Joining fragmented utility data took hours of manual filtering. Now, users query an AI assistant in plain English for instant charts.
15 data domains, 20-minute queries, some tables too large to touch. One hackathon later, AI fields half of all queries—customers included.
Simple questions demanded 45 minutes of SQL. An AI assistant now answers 70% of internal analytics, freeing the team from routine queries.
Crews wasted time verifying safety docs. A GenAI assistant now serves instant, authorized answers, scaling to 4,000 technicians.
350,000 track images reviewed by hand each month, always chasing failures. TrackDefectX now catches defects before they escalate.
Generic search couldn't speak nuclear; answers buried in licensing docs. Purpose-built AI now surfaces them in seconds, traceable to source.
Energy market research that took weeks now closes same-day. Staff freed from chasing information to make the calls that matter.
With 99% of records missing metadata, teams sifted unlabeled databases. Now, AI generates column descriptions for data experts to verify.
Analysts spent 5 days coding reports. Now, business users query millions of records in plain English for instant results.
A North American infrastructure provider supplying 33% of United States natural gas through 30,000 miles of pipelines while managing millions of transaction records.
Centralized analysts were overwhelmed by data requests, often spending up to a week coding and validating complex reports for executives. This...
“A data request that previously took an analyst five days to complete was entered into Genie—including all the bullet points—and it returned results in just seconds.”
Natural gas processing and transportation infrastructure company.
Databricks is a Big Data company that offers a unified analytics platform for data science, engineering, and analytics teams.
Williams's Self-service analytics is part of this use case:
Related implementations across industries and use cases
Most employees couldn't reach the data without an analyst. Refinery experts now build soft sensors through conversation, no code needed.
Joining fragmented utility data took hours of manual filtering. Now, users query an AI assistant in plain English for instant charts.
15 data domains, 20-minute queries, some tables too large to touch. One hackathon later, AI fields half of all queries—customers included.
Simple questions demanded 45 minutes of SQL. An AI assistant now answers 70% of internal analytics, freeing the team from routine queries.
Crews wasted time verifying safety docs. A GenAI assistant now serves instant, authorized answers, scaling to 4,000 technicians.
350,000 track images reviewed by hand each month, always chasing failures. TrackDefectX now catches defects before they escalate.
Generic search couldn't speak nuclear; answers buried in licensing docs. Purpose-built AI now surfaces them in seconds, traceable to source.
Energy market research that took weeks now closes same-day. Staff freed from chasing information to make the calls that matter.
With 99% of records missing metadata, teams sifted unlabeled databases. Now, AI generates column descriptions for data experts to verify.