Agentic AI, grounded.

Next-move prediction

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

The two-sided engine builds a probability distribution over candidate moves from live signals, then reweights it by what their management is actually organised and funded to do (Porter Four-Corner priors). You get odds, not adjectives.

Blind-spot exposure

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

APEX joins a competitor's stated strategy against what their buyers write in reviews — 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-placement forecast

“Where do we land in the next Magic Quadrant?”

Placements from the editions you're licensed for are read into the graph — each one recording whether it was measured from the figure, computed from sub-scores, or estimated — then projected forward with the movement drivers named.

Churn with named drivers

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

Eight lifecycle-agnostic dimensions score every account from public evidence alone, sharpened — never replaced — by your CRM. Risk is 0–100 where every point traces to a driver you can argue with and act on.

Feature-gap economics

“Which gap is actually costing us deals?”

Win/loss fuses six sources — your closed deals, deal-bound interviews, surveys, support friction, public reviews, and the competitor's own surfaces — then ties each recurring loss reason to the capability gap behind it.

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 — read reviews at the feature level, so ‘ease of setup’ and ‘support quality’ become scored claims with the review text as the citation.

Signal triage

“What happened this week that matters to us?”

A catalog of 2,000+ sources to draw from. One article can split into several signals — a raise, a hiring plan, an upmarket push — each scored 0–100 for significance, node-linked, and deduplicated across sources instead of stacked.

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. Sizing a category is a traversal, not a research project — and the whitespace is what the traversal doesn't find.