How to Run Company and Sector Deep Dives with AI

AI is most useful in a deep dive when it executes a written research plan: collect the right documents, populate a cross-source evidence grid, reconcile conflicting definitions, and leave unresolved questions visible. It should not be asked to “research the company” and rewarded for length. The analyst chooses the causal questions, peer set, scenarios, and what the evidence means for the investment case.

This is a public-source workflow guide, not a product benchmark. We reviewed official data sources and documented product boundaries but did not run a shared vendor test. We build AllMind, so the connected research system named near the end is our own product.

Company and sector deep dives answer different questions

A company deep dive asks how one business makes money, what drives its cash economics, which expectations are embedded in valuation, and what would change the thesis. A sector deep dive asks where profit sits across a value chain, how market structure is changing, and which firms gain or lose as the external variables move.

Do not combine both into one prompt. Build the sector frame first when the company cannot be understood without its value chain. Build the company first when the sector is familiar and the question is issuer-specific execution.

Write a question tree before collecting sources

The question tree is the brief the AI must satisfy. A general company tree might look like this:

1. Economics: what are the revenue units, price, volume, gross margin, reinvestment, and working-capital drivers?
2. Structure: which segments, geographies, customers, suppliers, and channels concentrate risk or bargaining power?
3. Expectations: what do consensus, the current valuation, and management guidance imply?
4. Change: which product, competitor, regulation, or capacity decision can alter the economics?
5. Proof: which next disclosure would support or contradict the thesis?

A sector tree uses another order:

1. define the market and value chain;
2. locate revenue, gross profit, and capital intensity across the chain;
3. identify demand drivers and supply constraints;
4. measure concentration, substitutes, and switching friction;
5. compare peer strategies and unit economics;
6. model cycle or policy scenarios;
7. list observable indicators that distinguish the scenarios.

For each branch, require one primary source, one comparison across time or peers, and one unresolved question. That last field prevents the system from filling evidence gaps with generic explanation.

Build a source ladder, not a source pile

Use the most direct evidence available for each claim.

The SEC's EDGAR search and filing resources are the primary US issuer source. Sector work often needs public datasets outside securities filings. The Bureau of Economic Analysis GDP-by-industry accounts provide output and value-added views, the Bureau of Labor Statistics Industries at a Glance organizes employment, productivity, prices, and compensation, and the Census Bureau's economic indicators cover sector-specific activity. These sources use different industry classifications and periods. Record those definitions before comparing them.

The peer grid is the core reusable artifact

Run the same question across the peer set. A grid is valuable only when each cell preserves its evidence status.

Do not force every row to contain a number. If one company reports bookings, another units shipped, and a third only segment revenue, those are three different measures. A blank or qualitative cell with a reason is more informative than a false common metric.

Add a definition register below the grid:

AI can extract and align candidate definitions quickly. The analyst decides whether a normalization is defensible.

Reconcile three views of every important claim

A deep dive earns its name through reconciliation. For each thesis-critical statement, show:

1. Company view: what management reports or argues.
2. External view: what the market, regulator, customer, supplier, or dataset indicates.
3. Analyst view: the conclusion, confidence, and what would change it.

Example structure:

The first two columns are source work. The third is judgment. Never allow the generated draft to blur them into a single confident paragraph.

Use AI in four passes

Pass 1: inventory

Collect sources, dates, reporting periods, permissions, and missing documents. The output is a bibliography and gap list, not prose.

Pass 2: extraction

Populate financial history, KPI definitions, guidance, peer facts, and external series. Every cell carries a link and evidence label. Flag conflicting periods, units, and segment definitions.

Pass 3: analysis support

Calculate common-size statements, growth bridges, sensitivities, concentration measures, and scenario inputs. Keep formulas visible. Ask the system for alternative explanations and evidence against the emerging thesis.

Pass 4: drafting and review

Draft to the question tree. Delete sections that only summarize background. The reviewer checks thesis-critical claims against the source, confirms that estimates are labeled, and adds the investment implication in their own voice.

Pick software after defining the evidence universe

Open-web deep-research assistants can build an orientation brief and find public sources. They are useful at the inventory stage. They cannot access a firm's entitled broker research, private expert content, or internal models unless those materials are deliberately connected under an appropriate enterprise arrangement.

Licensed-search platforms fit when the critical evidence is a large broker, expert, transcript, and news library. Document-analysis products fit a bounded room of uploaded materials. Market-data products fit structured history and consensus. A connected research system fits when the deliverable must join all of those with the firm's prior work.

AllMind is in that last category, and it arrives with the library already inside it: 6,800+ premium datasets from 100+ providers cover filings, fundamentals, estimates, broker and expert research, live and processed transcripts, news, market data, macro, and alternative signals, and a financial ontology connects those to the firm's own sources for multi-step agent work. It is not a live trading terminal, and onboarding internal systems takes a data conversation. A team should test whether the connection improves the peer grid and source lineage, not whether it produces a longer report.

Review against failure modes

Before accepting the deep dive, look for these defects:

• market size figures with different definitions presented as a time series;
• peer metrics aligned by label but not by calculation;
• management claims repeated as facts;
• consensus or valuation data with no timestamp;
• announced projects counted as operating capacity;
• a company summary that never reaches unit economics;
• a sector history that never identifies the current disagreement;
• citations attached to a paragraph but not to the claim they support;
• no counterevidence and no unresolved question list.

We did not compare product accuracy, completeness, or speed. Public data sources above define useful evidence layers, but sector-specific work may require licensed or proprietary inputs. A strong deep dive can still conclude that a central fact is unavailable or incomparable.

Begin with the question tree and peer-grid headers, then run a source inventory. If the tool writes the report before it can show those two artifacts, stop the run and fix the research design.

Sources and methodology

• SEC Search Filings, for US issuer primary documents.
• BEA GDP by Industry, BLS Industries at a Glance, and Census economic indicators, for examples of primary public sector data.
• Product-category discussion is based on documented product scope, not a common executed test.