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.

copy these and adapt the ticker, session, and lookback to your setup. these map to the 3 question types above — start with whichever fits what you're trying to learn.

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:

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.

the report pages show you one ticker at a time. edgeful AI doesn't have that limit — you can put several tickers, or several reports, side by side in a single answer, which is something you can't do in the report view.

two moves that only work here:

you don't have to attach anything first. name the tickers and reports in the question and the AI pulls the data it needs to build the comparison.

edgeful AI can read files you bring to the chat. if you have a trade log, a strategy write-up, or any PDF or document the AI should factor in, upload it and ask your question against it. it takes PDFs, Word docs, spreadsheets (.xlsx, .csv), text and markdown files, and images (.jpg, .png), up to 10 files as context.

this is useful when the context lives outside edgeful — for example, uploading your trade log and asking the AI to line your entries up against what the reports show, or attaching a written playbook so the answers come back in the terms you already trade by.

the more context you give it, the sharper the answer — a file is just another way to hand it that context.

when you've run an Analyze Reports flow and have a data table attached to the chat, the single biggest precision boost is using the AI's own vocabulary back at it. the column names in the data table are exactly what the AI is looking for.

a concrete example. both of these ask the same thing about the IB:

both produce usable answers. the second is more reliable, especially on edge cases. keep the data table panel open while you type — being able to see the column names is the fastest way to ask precise questions.

the most powerful prompt pattern in the Analyze Reports flow is asking the AI to find commonalities across a set of days that share an outcome:

the AI scans every row in the data table that meets the condition, then looks across the rest of the columns (size, weekday, high/low formed first, opening candle direction, etc.) to surface which features show up most often on those days. the answer is usually a ranked predictive checklist — a short list of features that historically appear when the condition is true.

it's the closest thing edgeful AI offers to letting you act like a quant without being one. you give it the outcome you care about, and it finds the conditions that lead to it.

once you've analyzed a set of reports and the data table is attached, a great opening prompt is simply what questions can I ask you about these reports?. the AI knows what's in your table and will suggest the most productive angles — use the list as a menu for the rest of the conversation.

it's especially useful when you've stacked 2 or 3 reports and aren't sure which cross-report angles are worth digging into.

you can do the same thing before you've attached anything. tell the AI how you trade — I trade RTY primarily and I'm interested in the IB report; what kinds of questions can I ask about the data? — and it'll hand you a list of questions that fit your style. a good way in when you know the kind of trader you are but not the exact question to start with.

most responses end with a suggested follow-up question — something like "want me to break down those Tuesday double breaks by how they resolve?" or "want me to check the opening candle direction on those 12 days?". the AI is reading what's still unanswered in the data and pointing at the next logical question.

reply with yes, run that or copy the suggested question verbatim. either works. follow-ups are how multi-step research happens — one analysis seeds the next, and you end up two or three layers deeper than you would have asked on your own.

when an answer runs long, edgeful AI appends a TL;DR section at the end with the short version. if you're skimming, jump straight to it — the full breakdown is there above it when you want to dig in.

you can also shape the answer yourself. if a response is denser than you need, reply give me a simple table breakdown and the AI collapses it into a clean table of just the numbers that matter. give me a TL;DR works the same way when you want the one-paragraph version. an answer stays as simple or as detailed as you make it — keep it simple and it stays simple.

edgeful AI is a research tool. it doesn't predict price, and it can't see your broker account or trade history (unless you upload a trade log yourself). it has no live feed of its own, but when you ask from the side panel on a report, the screener, what's in play, discovery, or the algo templates, it reads what's on your screen, including the rows, live prices, and the ticker you've selected. for the full breakdown, see what edgeful AI can and can't answer.

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, plus whatever page you've got open when you ask from the side panel.

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.