what edgeful AI is built for
edgeful AI draws on edgeful's full report library. that means you can ask it to research any report, across any ticker, across any session — and it'll pull the data and give you a structured answer.
the 3 things it does best:
1. report deep dives
going deep on a single report for a single ticker. instead of scanning the report page yourself, you ask the AI to walk you through what the data shows — the key numbers, the recent trend, what conditions look like right now.
this is useful when you want more than a quick glance at the screener. you're not just checking a colour — you're understanding what's behind it.
2. multi-report confluence
asking the AI to look across multiple reports for the same ticker and tell you what they're collectively showing. this is where edgeful AI really earns its place — pulling together data from reports that you'd normally have to check one by one and synthesising a directional read.
if you're building a bias and want to know whether the IB, gap, and previous day's range reports are all pointing the same way for NQ today, this is the question to ask.
3. historical pattern research
digging into how price has behaved historically under specific conditions. not "what will happen" — but "when these conditions have shown up before, what happened?" that's where the edge is, and it's what edgeful's data is built on.
how to frame a good question
the single biggest factor in the quality of the AI's response is how specific your question is.
a vague question produces a vague answer. a specific question — one that includes the ticker, the session, and the report you care about — produces a focused, data-driven response you can actually use.
include these 3 things whenever you can:
ticker — which instrument are you asking about? ES, NQ, GC, AAPL, etc.
session — NY, London, or Asian? the data is session-specific, so this matters
report or condition — which report or market condition is the focus of your question?
the more of these you include, the more precise the response. asking about "the IB report for NQ in the NY session" will produce a materially better answer than asking about "the IB report" in isolation.
date range is also useful when you have a specific lookback in mind — for example, asking about how a pattern has behaved over the last 6 months vs the last 3 months can surface different reads.
what it can't do
edgeful AI is a research tool — it doesn't provide real-time data, predict future price movement, or access your personal account or trade history. for the full breakdown, see what edgeful AI can and can't answer.
the right mindset for using it
think of edgeful AI as a research analyst who's read every report on the platform and can pull any data point on demand — but who works from historical records, not a live screen.
the questions that get the most out of it are research questions. "what does the data show for X ticker under Y conditions" is the core pattern. the more you lean into that framing, the more useful every conversation becomes.
if you're not sure where to start, the suggested prompts on the AI's home screen are a good first step — they're designed to show you the kinds of questions the AI is optimised for.