Keyword search missed context in 1M+ assets. Now, vector analysis runs directly at the source—launching hybrid search in 18 weeks.
A global business news organization serving over one million subscribers with an archive of more than one million content assets.
Time-strapped readers struggled to find relevant analysis on complex topics using traditional keyword search, which often missed context and meaning....
“From an architectural standpoint, it made a lot of sense to keep content searchable at [the] source rather than transferring it to an external search engine.”
Global news organization covering business, finance, and international affairs.
Multi-cloud developer data platform for building and scaling applications.
Financial Times's Content discovery is part of this use case:
Related implementations across industries and use cases
Fragmented pipelines slowed cross-site suggestions. A unified AI vector database cut latency 90%, processing 1,500 queries per second.
Proprietary APIs offered zero control. Moving to managed open models secured 2-second answers and fixed pricing for news search.
Researching millions of articles took 30 minutes. An AI tool now retrieves facts, summaries, and angles in seconds.
Researching millions of articles took 30 minutes. An AI tool now retrieves facts, summaries, and angles in seconds.
Data silos across 300+ products made navigating fiscal regulations slow. A unified AI assistant now securely answers complex user queries.
HR spent days manually exporting data for multi-currency calculations. Now, they query AI for instant answers without running reports.
Security fears fragmented AI usage. Now, clear guardrails and automated Slack nudges empower teams to accelerate design and code reviews.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
Software updates were tied to rigid vehicle production cycles. A GenAI platform now frees 5,000 developers to release code independently.
Keyword search missed context in 1M+ assets. Now, vector analysis runs directly at the source—launching hybrid search in 18 weeks.
A global business news organization serving over one million subscribers with an archive of more than one million content assets.
Time-strapped readers struggled to find relevant analysis on complex topics using traditional keyword search, which often missed context and meaning....
“From an architectural standpoint, it made a lot of sense to keep content searchable at [the] source rather than transferring it to an external search engine.”
Global news organization covering business, finance, and international affairs.
Multi-cloud developer data platform for building and scaling applications.
Financial Times's Content discovery is part of this use case:
Related implementations across industries and use cases
Fragmented pipelines slowed cross-site suggestions. A unified AI vector database cut latency 90%, processing 1,500 queries per second.
Proprietary APIs offered zero control. Moving to managed open models secured 2-second answers and fixed pricing for news search.
Researching millions of articles took 30 minutes. An AI tool now retrieves facts, summaries, and angles in seconds.
Researching millions of articles took 30 minutes. An AI tool now retrieves facts, summaries, and angles in seconds.
Data silos across 300+ products made navigating fiscal regulations slow. A unified AI assistant now securely answers complex user queries.
HR spent days manually exporting data for multi-currency calculations. Now, they query AI for instant answers without running reports.
Security fears fragmented AI usage. Now, clear guardrails and automated Slack nudges empower teams to accelerate design and code reviews.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
Software updates were tied to rigid vehicle production cycles. A GenAI platform now frees 5,000 developers to release code independently.