Every day, hundreds of articles are published about artificial intelligence companies: earnings reports, product announcements, regulatory decisions, analyst commentary, geopolitical developments, and everything in between. No individual can read them all. More importantly, no individual can reliably synthesise them into a consistent, comparable signal across dozens of companies and sub-sectors simultaneously. Canary does.
Canary is KnowEntry’s daily AI sector intelligence system. Each morning it collects, reads, scores, and analyses the previous day’s relevant news across six AI industry categories, producing a structured set of signals that tell you, in quantitative terms, where sentiment stands, which categories are behaving unusually, and whether the underlying narrative is stable or shifting.
What Canary Monitors
The system covers the AI sector across six categories that together represent the principal layers of the AI value chain:
- AI Chips and Hardware
- AI Platform Hyperscalers
- AI Enterprise Software
- Data Centre Infrastructure
- AI GPU Cloud
- Pure Play AI
This structure allows Canary to track not just aggregate AI sector sentiment but the internal dynamics of the sector: which layers are advancing, which are under pressure, and whether they are moving together or pulling apart.
Each day Canary processes hundreds of relevant articles drawn from established financial and technology news sources. Every article is assessed for its relevance to each of the six categories and scored for sentiment, meaning the degree to which it is positive, negative, or neutral about the companies and developments it covers. Articles that are only tangentially related to the AI sector carry less weight than those at its core. This relevance weighting ensures that peripheral news does not dilute the signal from the stories that genuinely matter.
Powered by Large Language Models
The scoring is performed by a large language model (LLM) developed by Anthropic, one of the leading artificial intelligence safety and research organisations. Unlike traditional sentiment tools that rely on keyword matching or simple lexicons, an LLM reads and understands each article in context. It distinguishes, for example, between a company reporting strong revenue growth in a cautious macro environment and a company reporting strong revenue growth against a backdrop of regulatory scrutiny. The distinction matters, and Canary captures it.
Each article receives not only a sentiment score but a narrative frame classification: a judgement about what kind of story the article is telling. Is this a story about financial results, or about regulatory risk? Technical progress, or competitive threat? These classifications, explained in detail on the Narrative Frame Distribution page, form the basis of some of Canary’s most powerful signals.
What the Dashboard Shows
The Canary dashboard presents this daily analysis in a sequence of views that move from the general to the specific. The overall sentiment score and trend chart give an immediate read on the aggregate mood of AI sector coverage. The category breakdown table shows where individual sub-sectors stand relative to their own historical norms. The sentiment heatmap and individual trend charts reveal how each category has moved over recent weeks. The Narrative Frame Distribution chart tracks the shifting balance of story types in the news. And the Semantic Volatility Index (SVI), discussed in full on our SVI page, provides a composite measure of how stable or unstable the underlying narrative has been.
Together these signals are designed to surface what raw news consumption cannot: a clear, consistent, quantitative picture of where the AI sector conversation is, how it got there, and what may be coming next.
A Note on Broader Applicability
Canary’s methodology is not specific to AI. The same framework — covering sentiment scoring, narrative frame classification, and semantic volatility detection — can be applied to any sector where a sufficient volume of daily news is available. The current dashboard covers the AI industry across six sub-sectors; the architecture is designed to expand.
At a Glance
- Daily sentiment scores and statistical significance
- Category-by-category comparison against historical norms
- Narrative frame distribution across eight story types
- Semantic Volatility Index composite score