Chipmakers expand US capacity as AI infrastructure demand accelerates
AskNews resolves the article into structured, model-ready context as it arrives.
AI-ready news infrastructure
Delete the crawler, the parser, the dedupe job, and the licensing conversation. One API returns licensed, enriched, prompt-ready news your agents can call directly.
$ asknews research "Investigate how EU AI Act enforcement affects model providers"

AI companies raise token prices to cover development costs

Inference costs reshape the economics of AI products
Agent frameworks converge on a shared tool protocol
Dev teams cut infrastructure spend by buying the data layer
Retrieval quality, not model size, decides output accuracy
Used in the field
From the UK Cabinet Office and Frontier AI Labs to organizations across insurance, intelligence, private equity, and hedge funds.
The problem
A weekend prototype turns into a permanent maintenance job: selectors rot, paywalls close, duplicates pile up, and legal asks who approved any of it.
Every site redesign is an unplanned sprint.
The reporting worth having is the reporting you cannot fetch.
Dedupe, enrich, index, backfill — none of it is your product.
Or you delete all of it and call one endpoint: AskNews.
Built for developers
AI-native platform
Retrieval, enrichment, entity resolution, history, and monitoring — behind one API instead of five services you maintain.
Real-time search and retrieval at scale.
Drop AskNews in as a tool your agent already knows how to call.
Agentic deep research when one query is not enough.
Enriched and deduped, so you are not pre-processing before the model.
Backfill without writing a second pipeline.
Push updates into your app instead of polling for them.
Enrichments
Every AskNews datapoint arrives with the context models need—not a raw article your team still has to process. Real-time news access responds in under 100 ms.
AskNews resolves the article into structured, model-ready context as it arrives.
One toolbox
Reach paywalled news, the open web, X, Wikipedia, Google, Reddit, and Podcasts through one consistent AskNews toolbox—without stitching together seven separate integrations.
Bring licensed reporting and the broader web into the same research workflow, then let AskNews retrieve, connect, and structure the evidence.

Competitors
One pass. One result set. Research stops here.
DeepNews
Depth
DeepNews performs self-learning iterative research across paywalled news, open web content, X, Wikipedia, and much more, to reach never-before-seen research depth and breadth.
Getting started
Self-serve, no sales call to start building.
REST, Python, TypeScript, CLI, or MCP.
Scale the tier as your traffic grows.
Why teams stop building it themselves
Cleaner context, fewer tokens burned on noise, and an entity graph you would never get around to building.
Entity resolution you would otherwise have to build yourself.
How do we compare?
Everything in the left column is something your team ends up owning forever.
| Approach | The problem | AskNews |
|---|---|---|
| In-house scrapers | Permanent maintenance, no paywalled access | One endpoint, licensed sources |
| Generic AI search | Open web only, noisy, hard to ground | News-specific, enriched, prompt-ready |
| Traditional news APIs | Raw feeds you still have to process | Structured for models, not for skimming |
| RSS + vector DB | Shallow coverage, you own the whole pipeline | Retrieval, enrichment, and history included |
Customer proof
Used by teams across Frontier AI Labs.
Pricing
Access trusted AI-powered news intelligence in the way that suits your needs.
Ship instead of scrape
Join the developer teams and agent shops building news-aware products without owning a news pipeline.
Get your API key$ asknews research "Investigate how EU AI Act enforcement affects model providers"

AI companies raise token prices to cover development costs

Inference costs reshape the economics of AI products
Agent frameworks converge on a shared tool protocol
Dev teams cut infrastructure spend by buying the data layer
Retrieval quality, not model size, decides output accuracy






























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