The DRIFT Framework
Detecting and correcting narrative drift isn't a one-time audit. It's an ongoing discipline. The DRIFT Framework breaks that discipline into five stages, from initial detection through continuous monitoring, so you always know how AI is describing your company and can act before perception hardens.
The Five Stages
Detect
Establish a baseline for how AI systems currently describe your company. We run consistent, relevant queries across ChatGPT, Claude, Gemini, and Perplexity — the questions a real investor, analyst, or partner might ask — and record the answers as a starting reference point.
Review Sources
Identify what AI systems are drawing from. Source analysis reveals which disclosures, press coverage, analyst notes, or older articles are feeding the AI's synthesis, and whether those sources are current, accurate, and consistent with one another.
Identify Gaps
Compare the AI-generated narrative against your actual current position. Gaps typically fall into three categories: outdated facts, sentiment misalignment (tone that skews more negative or speculative than warranted), and missing context (strengths or proof points the AI simply doesn't surface).
Fix Alignment
Close the gaps by aligning the source ecosystem — the disclosures, IR materials, and public record AI relies on — so they tell a consistent, accurate story, and by ensuring the information AI draws from is clear, current, and easy to interpret.
Track Continuously
Narrative drift isn't a one-time event, so correction isn't a one-time fix. We monitor on an ongoing cadence — more frequently around sensitive windows like earnings, readouts, or active transactions — turning the framework into a continuous feedback loop rather than a single report.
Why It Matters For High-Stakes Companies
For companies in quiet periods, earnings cycles, regulatory milestones, or active transactions, the Detect and Review stages matter most precisely when official communication is constrained but AI keeps generating answers regardless. Running DRIFT proactively, before a sensitive window opens, gives you a clear picture of your AI-facing narrative while you still have time to influence it.
A Note On Disclosure And Discretion
QuestionFuel analyzes how AI interprets information that is already public. We do not advise on what a company should disclose, and our work is designed to respect quiet-period and disclosure obligations. Engagements are confidential, and we handle sensitive narrative information accordingly.