Agentic AI, grounded.

Next-move prediction

“What will they do with the $200M they just raised?”

In Competitor 360, the two-sided engine builds a probability distribution over candidate moves from the competitor's signals, then reweights it by what their management is actually organised and funded to do (Porter Four-Corner priors). You get odds, not adjectives, each time you run a forecast.

Blind-spot exposure

“What do they believe that their own customers contradict?”

The engine checks the belief behind each predicted move against recent signals about that competitor, at the company and product node, not by keyword. A move premised on a belief the evidence contradicts scores as MORE exploitable, because they keep funding the mistake.

Analyst placements

“Where do analysts place us and our rivals?”

Placements from the reports you upload are read into the graph, each recording whether it was measured from the figure, computed from sub-scores, or estimated, and shown beside your comparisons and battlecards.

Churn with named drivers

“Which accounts are at risk — and what exactly do we do?”

Account 360 scores every account on eight dimensions from public evidence. Connect your CRM and it adds a churn risk score from 0 to 100, from renewal timing, CRM health, NPS, and how the account's staff rate their tools, with a recommended play to run next.

Feature-gap economics

“Which gap is actually costing us deals?”

Win/Loss ranks why you lose from your deals, interviews, surveys, and public reviews, and Gaps shows, from your battlecards, the features where a rival's score beats yours.

What buyers actually say

“Is their onboarding really better than ours?”

Aspect-based sentiment models — run on our own infrastructure, not a third-party API — have read reviews at the feature level, so ‘ease of setup’ and ‘support quality’ become scored claims with the review text as the citation. The public review corpus runs to mid-June 2026.

Signal triage

“What happened this week that matters to us?”

A catalog of 2,000+ sources to draw from, with new signals collected every day. Each new signal is classified into one of ten categories and kept with its source URL. Significance scores and company links are added again when the current pause on automated jobs ends.

Market sizing & whitespace

“How big is this category, who's winning, and where's the gap?”

The graph already holds the market: products typed against vendor, category, and capability, and an estimated size for every category with the sources it rests on. Sizing a category is a lookup, not a research project — and the whitespace is what the traversal doesn't find.