Grids — Complete Guide

Grids are AI-powered research tables. Each row is something you want to analyze — a company or a specific document (a filing, an earnings-call transcript, a research report) — and each column is a question or a data point you want answered for every row. AllMind then fills each cell using the same instruction across the whole table, so you get a consistent, side-by-side answer to the same questions for dozens or hundreds of companies or documents at once. A grid turns work that would otherwise be one-document-at-a-time reading into a single structured analysis you can sort, query, and export. Find it under Research Workspace → Grids.

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What it is

A grid works like a smart spreadsheet built for research:

• Rows are your subjects — companies (by ticker) or individual documents.
• Columns are what you want for each subject. A column is either a data column (a structured market or financial figure pulled automatically — price, margins, multiples, estimates, ratings, and so on) or an AI column (a plain-language instruction the AI answers for every row, such as “summarize the key risks” or “what did management say about pricing?”).
• Cells fill automatically. Data columns pull live numbers; AI columns read each row’s document (or fall back to live data and research) and write an answer grounded in what they find.

The value is consistency at scale: the same analytical framework applied identically to every company or document, in minutes, instead of opening files one at a time.

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When to use it

Reach for a Grid when:

• You want to compare many companies or documents on the same set of questions — e.g., margins, leverage, and key risks across the top gold miners, or revenue trends across a sector.
• You’re doing repetitive extraction — pulling the same few figures or facts out of many filings or transcripts.
• You want a structured, sortable baseline you can scan, filter, and export — not a single narrative essay.
• You want the analysis to stay current as new filings arrive (see auto-refresh).

Use something else when:

• You need a quick fact or a fast back-and-forth → Chat.
• You want to find or gather the source documents first → Document Search, then collect them in a Data Room.
• You want one deep, open-ended narrative on a single question (“analyze this company’s competitive position”) → Agent Studio. A grid is the structured complement to that narrative.
• You want a formatted, templated report or slide deck as the final deliverable → Reports (a grid’s results can feed it).
• You just want to look at one company’s data → the Equities data viewer.

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How to use it

There are two ways to build a grid: let the AI design it for you, or set it up yourself.

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Make Grid with AI

Describe the grid you want — by typing or by voice — for example “compare the top 5 gold miners on margins, leverage, and key risks.” The AI drafts a complete, editable plan: a grid name and type, a proposed set of companies (or documents), and a set of columns, each tagged data, AI, or text. Nothing is built until you approve it. You review and adjust everything first: rename the grid, add or remove companies, dial an index up or down, fix or drop columns, set how many documents to pull per company, and see a live estimate of the total number of cells and how long the fill will take. Anything the AI couldn’t confidently match (an unrecognized company, a column with no library match) is flagged for you to fix or remove. When you’re happy, choose Create & fill and the grid builds and fills itself in the background.

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Capabilities & key choices

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The two grid types

You choose the type when you create the grid, and it’s fixed for that grid. A Ticker grid is for structured data, so its columns are all auto-filled data fields. A Document grid is for reading documents, so it also lets you add AI columns that answer from each row’s document. If you use Make Grid with AI and your questions need to read documents, it builds a Document grid automatically.

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The two kinds of columns

• Data columns (“auto-fill”) — structured market and financial figures, chosen from a catalog and pulled automatically. They refresh live each time they run and carry their own context (the fiscal year and period, the unit, whether a figure is an actual reported number or an estimate, and the as-of date for prices). Numbers are formatted for you — dollars as millions/billions/trillions, ratios as multiples, and so on.
• AI columns — a plain-language instruction you write, answered for every row. For a Document grid, the AI answers from that row’s own document; if there’s no document (or no answer in it) and the row has a company, it falls back to live data, current news, and the research library. AI columns handle both fact extraction (“pull the reported revenue”) and open-ended analysis (“five questions to ask management,” “red flags,” “what’s missing”). There’s no output-format picker — you shape the answer through how you word the prompt.
• Text columns — blank columns you fill in yourself by hand (notes, your own labels). These are never auto-filled.

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Ready-made column packs

Rather than writing every column from scratch, you can drop in curated packs of data columns, including:

• Stock data & fundamentals — price and market data, income statement, balance sheet, cash flow.
• Ratios and per-share data.
• Valuation multiples — current and historical P/E, EV/EBITDA, EV/Revenue, P/B, and more.
• Analyst estimates — consensus EPS, revenue and EBITDA estimates, forward quarterly and annual, revisions, and surprise (beat/miss) history.
• Ratings & price targets — analyst rating breakdowns, consensus, and target-price upside.
• Metric changes — period-over-period moves in key figures.
• Technical indicators — trend, momentum, volatility, and volume measures.
• Sector performance & relative performance — how a name is doing versus its sector.
• Top / bottom performer checklists — quick performance-ranking columns.
• Risk statistics — volatility, beta, Sharpe/Sortino, maximum drawdown.
• ESG ratings, market sentiment (a fear-and-greed gauge), and macro indicators (GDP, CPI, unemployment, interest rates).

There are also ready-made AI analysis templates — pre-built question sets such as SWOT, Porter’s Five Forces, earnings analysis, risk analysis, and competitive analysis — that you add as AI columns (Document grids only).

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Reuse: universes and templates

• A universe is a saved, named list of companies. Build one once and reuse it as the rows of any future grid. You can create a universe by searching and picking companies, by uploading a list (CSV, TSV, or Excel — common ticker formats from Bloomberg, Refinitiv, and Capital IQ are recognized automatically), or directly from an existing grid’s companies. Universe names are unique to you, and universes are personal — they aren’t shared. (There are no prebuilt universes; every universe is one you create.)
• A column template is a saved, named set of columns (their prompts and data sources). Save the structure of a grid you like and apply it to a fresh set of rows later. Column templates can be shared with teammates and copied.

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Shape and explore the grid

Once a grid is filled you can make it readable: search across cell text and column names, sort and group rows by company, document type, year, or quarter, hide columns or rows you don’t need, transpose the table to swap rows and columns, and auto-fit or compact the layout. The view you arrange is the view that gets exported and the view Ask AI reasons over — what you see is what it uses.

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What it can access & produce

It can draw on:

• Your own documents — filings, transcripts, research, investor-relations materials, and ESG reports you search and attach to rows, plus files you upload from your computer, Google Drive, OneDrive/SharePoint, Gmail, or Outlook.
• Live market data — prices (current and historical), daily changes, volume, ranges, and market cap.
• Fundamentals & ratios — income statement, balance sheet, cash flow; segments and KPIs.
• Valuation — current and historical multiples and peer comparisons.
• Estimates & analyst coverage — consensus estimates, revisions, surprise history, ratings, and price targets.
• Corporate events — earnings dates, investor days, conferences, and meetings.
• Macro & market data — official economic indicators, a fear-and-greed sentiment index, and live index and yield quotes (these don’t even need a company on the row).
• News and the web — recent company and sector news summaries and “what’s driving the price” analyses.
• The research library — broker and analyst research, regulatory filings (US and Canada), earnings transcripts, press releases, IR materials, ESG reports, and sector data.

It produces:

• A filled research table — structured data and AI answers, consistent across every row.
• An Excel export of the grid you can download or send by email.
• A saved universe of the grid’s companies and a saved column template of its structure, both reusable.
• Ask AI synthesis across the whole grid — answers that read down and across the table.

Data cells carry source context (period, unit, estimate-vs-actual, as-of date), and AI answers are grounded in the row’s document — the AI is instructed to say the information isn’t in the document rather than guess.

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Tips & best practices

• Be specific in your columns. “Total reported revenue for the latest fiscal year” beats “revenue.” Precise instructions give cleaner, more consistent cells.
• Match the grid type to the job. Use a Document grid when you need answers pulled from specific filings or transcripts; use a Ticker grid for cross-company data and metrics.
• Start from a template or a pack. Drop in a fundamentals or estimates pack, then add a couple of custom AI columns for the judgment calls.
• Keep a run to a sensible size. Very large grids take longer; if you’re analyzing hundreds of companies with many AI columns, consider splitting into a few focused grids.
• Refine surgically. Tweak one column’s instruction and regenerate just that column rather than rebuilding the whole grid — only the affected cells recompute, and the rest of the grid is untouched.
• Save what works. Turn a good column set into a template and a good company list into a universe so the next analysis is one click.
• Use Ask AI for the synthesis. Let the grid give you the consistent baseline, then ask across it for the narrative (“which names are deteriorating fastest, and why?”).

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Limits & things to know

• Per-run size guardrail (Make Grid with AI). A single AI-built run is sized to keep results fast and reliable: you’ll see a heads-up around 1,000 cells, and a run is capped at roughly 10,000 total cells, 2,500 AI-answered cells, or 600 rows — whichever comes first. If a plan is bigger, you’ll be asked to lower the company count, documents per company, or columns. You can build larger grids manually, but the same practical ceilings apply to a single generation.
• A plan you describe to the AI can name up to 30 companies individually, pull from a few indexes or saved universes (up to about 150 companies from one index, taken by size), and include up to 20 columns.
• Uploads. When you add your own files, you can upload up to 150 files at once, up to 100 MB each.
• How fast it fills. Cells compute many at a time — dozens of AI answers and around a hundred data lookups in flight at once — so grids fill in waves. A single generation run is time-bounded (on the order of an hour or more for a very large grid); if it ever reaches that limit it stops and flags the grid rather than hanging, and you can resume by generating the remaining cells.
• If a cell can’t be filled, it tells you rather than going blank — you’ll see a short message like “Information not found in document” or a prompt to regenerate the cell.
• “Top N” is by size, not a screen. The AI builder ranks index and sector members by weight, not by the metric you mention — refine the row set yourself.
• AI columns read one row’s document at a time. A Document-grid AI cell answers from that row’s own document and won’t pull facts from another row’s document — by design, so companies don’t bleed together.
• Sourcing today is provenance + grounding, not clickable footnotes. Data cells know their period and whether they’re estimates or actuals; AI answers are grounded in the source document, and you can open a row’s document to check — but cells don’t currently embed clickable citation links.
• Your grids persist until you delete them — grids, their cells, universes, templates, and folders are saved to your workspace. Deleting a grid also removes its auto-refresh subscriptions.

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How it works with other features

Grids sit in the middle of the research workflow — they take in sources and companies and hand off structured results:

• Document Search → Grids. Find the right filings, transcripts, and research, then attach them as the rows (or the source documents) of a grid.
• Data Room ↔ Grids. A Data Room and a Document grid draw on the same kinds of materials (filings, transcripts, uploads, connected sources). Gather a corpus in a Data Room, then build a grid to extract the same fields from every document in it.
• Agent Studio ↔ Grids. An agent gives you depth on one question as a narrative; a grid gives you the same answers consistently across many subjects. Use a grid to structure what several agent runs explore — and use the grid’s Ask AI to synthesize the table.
• Grids → Reports. Turn a grid’s structured findings into a formatted, templated report or deck.
• Grids ↔ Universes. Saved universes feed a grid’s rows; a grid can also spin its companies out into a new universe for reuse elsewhere.
• Chat. For quick follow-up questions about anything in the grid.

Keeping a grid current. There are two ways to refresh:

• Auto-update subscriptions. Subscribe a grid to a company so it keeps itself current: when a new matching document for that company appears — you choose which document types count (all, SEC filings, Canadian/SEDAR filings, earnings transcripts, press releases, earnings slides, or investor-relations categories) — the grid updates, either adding a new row for the new document or replacing an existing row in place. Each subscription covers one company, so track several by adding several. Updates are driven by new filings arriving, not a fixed daily or weekly schedule.
• Check for new documents (manual). Run an on-demand scan that checks every company already in the grid for documents newer than what it holds, then pick which of the found documents to add.

Sharing. Grids and column templates can be shared with an individual or a whole team, with View, Edit, or Admin access and an optional expiration date; recipients can be allowed to download, export, or re-share. Anyone you share with can make their own independent copy into their workspace. (Saved universes are personal — they aren’t shared.)

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Example workflows

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1. Compare a peer set on a consistent scorecard

Goal: size up the major gold miners on the same metrics and risks.

1. Make Grid with AI — describe it: “compare the major gold miners on margins, leverage, valuation, and key risks.”
2. Review the plan — confirm the companies (swap in any the builder missed), keep the margin, leverage, and multiple data columns, and keep a “key risks” AI column. Create & fill.
3. Scan and sort — sort by leverage or valuation to see the outliers.
4. Ask AI across the grid — “which names screen cheap and have improving margins?”
5. Save the column set as a template and the companies as a universe for next quarter.

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2. Read every transcript the same way (sector deep-dive into a deck)

Goal: analyze the gold sector and build a presentation.

1. Document Search — find the latest earnings-call transcripts and filings across the sector.
2. Data Room — collect the best sources into a room for the project.
3. Document grid — build a grid with one row per transcript and AI columns for the same questions (“management’s tone on costs,” “guidance changes,” “key risks,” “five questions to ask management”). Generate, so every transcript is read the same way.
4. Agent Studio — run a deeper narrative on the one or two themes the grid surfaces as most important.
5. Reports — combine the grid’s structured findings and the agent’s narrative into a presentation from a template.

The result: a consistent, sourced analysis across the whole sector plus a finished deck — assembled from across the platform.

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3. A self-updating monitoring grid

Goal: keep an eye on a watchlist as results come in.

1. Build a Ticker grid from a saved universe (your watchlist) with data columns for valuation, estimates, and ratings.
2. Add an auto-update subscription per company for earnings transcripts, set to add a new row when a new transcript lands.
3. Each earnings season the grid grows itself; open it, run Check for new documents if you want to pull anything in on demand, and Ask AI to summarize what changed.

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Common questions

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Getting help

For help building a grid or designing a research workflow around one, reach AllMind support through the in-app support option or your account team.