Proprietary APIs offered zero control. Moving to managed open models secured 2-second answers and fixed pricing for news search.
A major American news publisher serving millions of users with award-winning journalism and deeply sourced reporting.
The organization wanted to launch a public-facing Q&A tool but found proprietary APIs offered zero control and unpredictable costs. Self-hosting...
“Together AI's flexibility in creating customized configurations for our journalism use case has been invaluable to our success.”
Major daily newspaper and digital media organization for global news.
AI-native cloud platform for training, fine-tuning, and deploying open-source models.
The Washington Post's News content search is part of this use case:
Related implementations across industries and use cases
Researching millions of articles took 30 minutes. An AI tool now retrieves facts, summaries, and angles in seconds.
Readers hit dead ends with rigid search. Conversational AI now connects topics, boosting monthly searches from 30k to 500k.
Fragmented pipelines slowed cross-site suggestions. A unified AI vector database cut latency 90%, processing 1,500 queries per second.
Editors relied on memory to analyze live fights. Now, AI agents mine history instantly, fueling broadcasts with deep, real-time context.
Keyword search failed on complex vehicle queries. Now, an AI pipeline rewrites messy questions to power shopping and service assistants.
Security fears fragmented AI usage. Now, clear guardrails and automated Slack nudges empower teams to accelerate design and code reviews.
Manually illustrating vintage assets would have tripled production time. Now, a 3-person team uses AI to generate and animate the visuals.
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.
Proprietary APIs offered zero control. Moving to managed open models secured 2-second answers and fixed pricing for news search.
A major American news publisher serving millions of users with award-winning journalism and deeply sourced reporting.
The organization wanted to launch a public-facing Q&A tool but found proprietary APIs offered zero control and unpredictable costs. Self-hosting...
“Together AI's flexibility in creating customized configurations for our journalism use case has been invaluable to our success.”
Major daily newspaper and digital media organization for global news.
AI-native cloud platform for training, fine-tuning, and deploying open-source models.
The Washington Post's News content search is part of this use case:
Related implementations across industries and use cases
Researching millions of articles took 30 minutes. An AI tool now retrieves facts, summaries, and angles in seconds.
Readers hit dead ends with rigid search. Conversational AI now connects topics, boosting monthly searches from 30k to 500k.
Fragmented pipelines slowed cross-site suggestions. A unified AI vector database cut latency 90%, processing 1,500 queries per second.
Editors relied on memory to analyze live fights. Now, AI agents mine history instantly, fueling broadcasts with deep, real-time context.
Keyword search failed on complex vehicle queries. Now, an AI pipeline rewrites messy questions to power shopping and service assistants.
Security fears fragmented AI usage. Now, clear guardrails and automated Slack nudges empower teams to accelerate design and code reviews.
Manually illustrating vintage assets would have tripled production time. Now, a 3-person team uses AI to generate and animate the visuals.
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.