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.
Fragmented pipelines slowed cross-site suggestions. A unified AI vector database cut latency 90%, processing 1,500 queries per second.
Teams manually read every new comic to extract keywords. Now, AI analyzes art and speech bubbles to auto-generate campaign metadata.
Keyword search failed on complex vehicle queries. Now, an AI pipeline rewrites messy questions to power shopping and service assistants.
Fragmented vendor feeds obscured matching items. Now, AI pipelines parse millions of unstructured records into curated collections.
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.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.
A mistranslated word could derail global R&D projects. Now, researchers instantly refine technical papers & communicate seamlessly across languages.
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.
Fragmented pipelines slowed cross-site suggestions. A unified AI vector database cut latency 90%, processing 1,500 queries per second.
Teams manually read every new comic to extract keywords. Now, AI analyzes art and speech bubbles to auto-generate campaign metadata.
Keyword search failed on complex vehicle queries. Now, an AI pipeline rewrites messy questions to power shopping and service assistants.
Fragmented vendor feeds obscured matching items. Now, AI pipelines parse millions of unstructured records into curated collections.
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.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.
A mistranslated word could derail global R&D projects. Now, researchers instantly refine technical papers & communicate seamlessly across languages.