Corporate subtext is a second language. Most people never learn it.
An AI-powered workplace intelligence tool that decodes meeting dynamics, detects behavioral patterns, and surfaces the unspoken politics — so you walk into every room knowing what's actually happening.
The problem
Meetings have a script nobody hands you
Every meeting has two conversations. The one on the surface — agenda, updates, action items. And the real one: who's positioning, who's deflecting, who just signaled that a decision was made before the meeting started.
Most professionals learn to read this subtext through years of exposure. Some never do. Neurodivergent professionals in particular — people with ADHD, autism, or both — often process communication literally and miss the political undercurrents entirely. They're not less capable. They're operating without the decoder ring that neurotypical corporate culture assumes everyone has.
There are meeting transcription tools, note-takers, AI summarizers. None of them tell you what actually happened in the room.
The gap isn't information capture — it's information interpretation. Meetings are a political medium disguised as a collaborative one, and most tools treat them like they're purely functional.
Loopt dashboard — meeting intelligence hub with analysis history, streak tracking, and accuracy feedback loop.
The solution
Pre-meeting intelligence, not post-meeting notes
Loopt flips the meeting tool model. Instead of summarizing what happened afterward, it gives you intelligence before you walk in. Who's in the room, what their behavioral patterns suggest, what dynamics to watch for, and what the subtext is likely to be.
Pattern Detection
22-pattern library
Built from real workplace dynamics — credit-taking, strategic ambiguity, consensus manufacturing, scope deflection, and more. Each pattern has defined indicators, confidence thresholds, and contextual triggers.
Scoring
Confidence system
Every detected pattern comes with a confidence score. Loopt doesn't cry wolf — it tells you how sure it is, what signals it's reading, and what would change its assessment.
Intelligence
Pre-meeting briefs
Before a meeting starts, Loopt surfaces attendee dynamics, likely power plays, topics where subtext is probable, and specific patterns to watch for based on who's in the room.
Accessibility
Built for neurodivergent professionals
Explicit, literal explanations of social dynamics that neurotypical tools assume everyone already understands. No euphemisms, no "read between the lines" — just clarity.
Post-meeting validation — Loopt predicted the meeting was about smoothing friction between Content and Sales, that the CMO was worried about missing targets, and that the Head of Content would protect creative independence. All rated Accurate. The feedback loop makes future predictions sharper.
Pattern library
What Loopt detects
The 22-pattern library was built by studying real workplace dynamics — not theoretical communication models. Each pattern was validated against actual meeting scenarios where the subtext was later confirmed.
Sample patterns
Each has defined triggers, confidence thresholds, and recommended responses.
Credit Redirection
Reframing another person's contribution as one's own, often using subtle language shifts like "building on what I was thinking" after someone else presents the idea.
Strategic Ambiguity
Deliberately vague commitments designed to preserve optionality. Sounds like agreement but creates no accountability. Often deployed by senior leaders hedging on decisions.
Consensus Manufacturing
Pre-meeting alignment that makes a "group decision" look organic when the outcome was decided before anyone sat down. Detected through attendee communication patterns.
Pre-meeting checklist — tactical prep with time estimates. "Sync briefly with Monica before" to develop shared defense strategies. "Jot down recent content success stories" for a headstart to controlling the narrative. This is the decoder ring.
Origin
Why this exists
Loopt started from personal experience navigating corporate environments with a neurodivergent brain. The gap between what was said and what was meant wasn't just confusing — it was career-limiting. The subtext that others absorbed effortlessly felt like a foreign language with no textbook.
The insight: if the patterns are consistent enough to learn through exposure, they're consistent enough to detect algorithmically. The 22-pattern library came from cataloging every instance of workplace subtext that became clear only in hindsight, then building detection logic to surface it in real time.
Presented at Superpath's AI show-and-tell. Currently in customer discovery validation.