The default assumption in competitive intelligence is that more is better.
More sources. More signals. More coverage. If your team is tracking fifty things and your competitor is tracking thirty, you win.
That assumption produces teams that read more but know less.
The problem with CI isn't volume. The problem is structure. A signal without context — without sector classification, severity rating, entity tags, and a machine-readable event type — is not an intelligence asset. It's noise with a timestamp. You can accumulate a thousand unstructured signals and still not be able to answer the question that matters: what changed this week that affects our competitive position?
The CI Radar is built around the opposite premise. Every signal must be classified before it can be useful. Taxonomy first. Volume second.
The Taxonomy Is the Product
When most people think about a competitive intelligence feed, they think about the content — signal titles, source URLs, summary text. That's the visible layer. The taxonomy is the invisible layer that makes the content usable.
Every signal in the CI Radar carries structured classification across multiple dimensions: which vertical it belongs to, how strategically significant it is, what type of competitive event it represents, which entities it implicates, and a numerical impact score that normalizes importance across sectors and event types.
That last dimension matters more than it sounds. Two signals can both be "semiconductor news" and represent completely different intelligence types. A capacity announcement changes supply allocation math. A roadmap disclosure changes design win probability. A pricing move changes competitive margin dynamics. Treating them the same way — as undifferentiated items in a feed — loses precisely the information a strategy team needs to act on them correctly.
The taxonomy is what encodes domain expertise into the data structure. A signal about Samsung's HBM4 yield progress doesn't just implicate Samsung — it implicates SK Hynix's competitive position, Nvidia and AMD's supply allocation options, and the CoWoS packaging capacity timeline at TSMC. A system that captures those entity relationships, not just the headline, is doing something fundamentally different from a news aggregator.
The result: every signal is not just readable — it's queryable, filterable, and comparable to every other signal in the library. You can retrieve what actually matters for a specific competitive question, rather than reading everything and hoping you catch it.
How Taxonomy Enables Everything Downstream
The taxonomy is not just about search. It's the substrate that every other product capability runs on — and where the compounding value of a structured signal library becomes visible.
Flash alert detection relies on correlation clustering across entity relationships and event-type classifications. When multiple signals from independent sources converge on the same underlying market development, the structure is what makes that convergence detectable at machine speed — before any single source has synthesized the pattern. Without consistent classification, there is no cluster. There is only a pile of unconnected headlines.
The executive dashboard is only useful because signals carry severity ratings and impact scores. A dashboard that surfaces the highest-impact signals first is giving you triage. A dashboard that shows you the most recent signals is giving you a chronological feed — a slower version of a news ticker.
The War Room Copilot is a RAG system — retrieval-augmented generation — meaning its answers are grounded in the signal library, not generated from LLM memory alone. This is the architectural choice that eliminates hallucinations on market data: the model retrieves specific, dated, sourced signals and synthesizes from those, rather than reconstructing facts from training weights. The quality of RAG outputs depends directly on the quality of the embeddings and the precision of the underlying classifications. Well-structured signals produce precise, trustworthy answers. Ambiguous, loosely tagged signals produce drift.
On-demand AI reports use the taxonomy for targeted retrieval: pulling signals by sector, entity, and event type to scope each section of analysis correctly. A Deep Dive on a specific company retrieves signals where that entity is a principal actor — not every signal that mentions it in passing. Precision retrieval requires precise classification upstream.
The AI layer is only as good as the data layer beneath it. Every flash alert, executive briefing, Copilot answer, and on-demand report is an expression of the taxonomy's quality.
1,457 Signals, All Structured
The CI Radar currently tracks 1,457 signals across Semiconductors, EV/ADAS, and AI Applications — every one classified across all dimensions, every one with AI-generated analysis surfacing the strategic implications for both corporate and investment perspectives.
That library is not a feed. It's a queryable intelligence graph. Every signal is a node. Entity tags are edges. Severity ratings, impact scores, and event types create a weighted, navigable structure that compounds in value as it grows.
Adding a signal to an unstructured database makes the database bigger. Adding a signal to a structured taxonomy makes the intelligence layer smarter — because every new signal arrives into a context of 1,456 prior signals, all classified, all embedded, all available as retrieval substrate for synthesis. Its marginal value is higher, not lower, because the taxonomy has been building the interpretive layer that makes it immediately meaningful.
Signal volume is a vanity metric. Signal taxonomy is the product.
Explore the CI Radar signal feed — filtered and AI-analyzed →