Analyzing 10TB of weekly telemetry took IT specialists days. Now, engineers ask AI in natural language to instantly retrieve charts.
A global automotive manufacturer operates a fleet of several thousand development vehicles that generate 5 to 10 terabytes of measurement data, such as braking patterns and battery voltage, every week.
Unlocking insights from this massive data lake could take hours or days because only specialized IT staff had the skills to write complex queries....
“We’d solved access with modernization, but access alone doesn’t drive innovation. With multi-agent AI, engineers don’t just get data—they get insights they can act on immediately. Ultimately, the steps of data extraction and pattern recognition can be performed directly in a single step, and in natural language.”
Luxury vehicle and motorcycle manufacturer for premium consumer mobility.
Enterprise software, cloud infrastructure, and consumer electronics platform.
BMW's Vehicle data analysis is part of this use case:
Related implementations across industries and use cases
Manual reviews and language gaps dragged repairs to a week. AI now diagnoses faults in 100 languages, halving the cycle.
Validating hypotheses took specialists weeks of manual coding. Now, an AI agent drafts analyses for expert review in minutes.
Scattered AI tools and manual document searches slowed engineers. Now, a unified AI rapidly retrieves specialized technical answers.
Scientists spent half a day digging through 50 years of scattered files. Now, an AI assistant retrieves past experiments in minutes.
10,000 supply chain workers spent half an hour manually searching compliance libraries. Now, an AI assistant finds answers instantly.
Thousands of software modules slowed vehicle development. Now, engineers collaborate with 64 AI agents to write and debug code.
Every alert still needed command-center follow-up — a ceiling as fleets grew. FleetGPT now reasons over each event and acts, not just flags.
Querying Wikidata required specialized syntax, locking out most AI systems. Vector search now lets LLMs navigate 100M+ entities in plain language.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.
Analyzing 10TB of weekly telemetry took IT specialists days. Now, engineers ask AI in natural language to instantly retrieve charts.
A global automotive manufacturer operates a fleet of several thousand development vehicles that generate 5 to 10 terabytes of measurement data, such as braking patterns and battery voltage, every week.
Unlocking insights from this massive data lake could take hours or days because only specialized IT staff had the skills to write complex queries....
“We’d solved access with modernization, but access alone doesn’t drive innovation. With multi-agent AI, engineers don’t just get data—they get insights they can act on immediately. Ultimately, the steps of data extraction and pattern recognition can be performed directly in a single step, and in natural language.”
Luxury vehicle and motorcycle manufacturer for premium consumer mobility.
Enterprise software, cloud infrastructure, and consumer electronics platform.
BMW's Vehicle data analysis is part of this use case:
Related implementations across industries and use cases
Manual reviews and language gaps dragged repairs to a week. AI now diagnoses faults in 100 languages, halving the cycle.
Validating hypotheses took specialists weeks of manual coding. Now, an AI agent drafts analyses for expert review in minutes.
Scattered AI tools and manual document searches slowed engineers. Now, a unified AI rapidly retrieves specialized technical answers.
Scientists spent half a day digging through 50 years of scattered files. Now, an AI assistant retrieves past experiments in minutes.
10,000 supply chain workers spent half an hour manually searching compliance libraries. Now, an AI assistant finds answers instantly.
Thousands of software modules slowed vehicle development. Now, engineers collaborate with 64 AI agents to write and debug code.
Every alert still needed command-center follow-up — a ceiling as fleets grew. FleetGPT now reasons over each event and acts, not just flags.
Querying Wikidata required specialized syntax, locking out most AI systems. Vector search now lets LLMs navigate 100M+ entities in plain language.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.