Standard AI misinterpreted technical queries. A custom model trained on 154k vetted docs now delivers precise chemical insights.
A global energy leader managing over 300,000 technical documents collected over decades required rapid access to its immense body of scientific data.
Standard language models frequently misinterpreted specialized technical queries, matching them with generic information rather than domain-specific...
Global energy and petrochemical company specializing in oil, gas, and renewables.
NVIDIA is a technology company that specializes in semiconductors, graphics processing units, and artificial intelligence for applications in data centers, gaming, and more.
Shell's Technical document search is part of this use case:
Related implementations across industries and use cases
Engineers lost 30% of their day hunting in isolated data vaults. A unified AI assistant now surfaces technical answers in minutes.
Scientists spent half a day digging through 50 years of scattered files. Now, an AI assistant retrieves past experiments in minutes.
Engineers waited hours for answers on technical standards. Now, a GenAI assistant scans thousands of docs to respond instantly.
Scattered AI tools and manual document searches slowed engineers. Now, a unified AI rapidly retrieves specialized technical answers.
Consultants lost hours sifting through manuals. AI now mines 250 million lines of code to answer technical queries instantly.
Manual call notes burdened agents, while managers struggled to track promises. Now, AI copilots automate summaries and verify commitments.
Managers once manually reviewed up to 84 recordings to find information. Now, AI instantly generates summaries and automated scorecards.
A 200% yearly data expansion bottlenecked global operations. Now, AI accelerates coding, drafts recipe cards, and resolves inquiries.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.
Standard AI misinterpreted technical queries. A custom model trained on 154k vetted docs now delivers precise chemical insights.
A global energy leader managing over 300,000 technical documents collected over decades required rapid access to its immense body of scientific data.
Standard language models frequently misinterpreted specialized technical queries, matching them with generic information rather than domain-specific...
Global energy and petrochemical company specializing in oil, gas, and renewables.
NVIDIA is a technology company that specializes in semiconductors, graphics processing units, and artificial intelligence for applications in data centers, gaming, and more.
Shell's Technical document search is part of this use case:
Related implementations across industries and use cases
Engineers lost 30% of their day hunting in isolated data vaults. A unified AI assistant now surfaces technical answers in minutes.
Scientists spent half a day digging through 50 years of scattered files. Now, an AI assistant retrieves past experiments in minutes.
Engineers waited hours for answers on technical standards. Now, a GenAI assistant scans thousands of docs to respond instantly.
Scattered AI tools and manual document searches slowed engineers. Now, a unified AI rapidly retrieves specialized technical answers.
Consultants lost hours sifting through manuals. AI now mines 250 million lines of code to answer technical queries instantly.
Manual call notes burdened agents, while managers struggled to track promises. Now, AI copilots automate summaries and verify commitments.
Managers once manually reviewed up to 84 recordings to find information. Now, AI instantly generates summaries and automated scorecards.
A 200% yearly data expansion bottlenecked global operations. Now, AI accelerates coding, drafts recipe cards, and resolves inquiries.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.