DeepBrain vs general-purpose AI

ChatGPT / Claude / Tongyi / Ernie are great tools. But they aren't trained for "conversation insight."

Dimension	DeepBrain	General-purpose AI (ChatGPT, etc.)
Scenario training	12 methods designed specifically for "conversation insight"	General-purpose chat, not trained for conversations
Evidence chain	Every judgment has a [#claim-N] anchor — click to jump to the source	Judgments are disconnected from the source and hard to trace
Silent signals	Proactively spots "who stayed silent at a key moment" and "what topics were avoided"	Doesn't proactively capture silent signals
Consensus in the room vs real consensus	Distinguishes "agreeing out loud" from "agreeing inwardly"	Equates how much someone talks with the strength of consensus
Cross-recording tracking	Automatically compares how positions evolve and whether action items get done across a project's conversations	Every conversation stands alone, with no memory
Participant profiles	Accumulates automatically — six months in, your judgment base knows these people	Leaves no long-term memory
Method version snapshots	An analysis from 3 months ago is always reproducible (method_version is immutable)	Output may drift after a model upgrade
Enterprise isolation	Hard RLS multi-tenant isolation + optional on-prem deployment	Relies on the vendor's promise
Chinese optimization	A Chinese method system (Probe the Bones, McKinsey, First Principles)	Chinese conversation is OK, but there's no Chinese insight method library
Professional output	A format consulting partners and strategy teams can adopt directly	Users have to reorganize it themselves

When to use general-purpose AI

General-purpose AI helps you save reading time; DeepBrain helps you save judgment time.