Best AI to Automate Company and Sector Deep Dives (2026)
Anwaar Malik
The short answer: A company or sector deep dive worth a PM's time is hours of work across filings, transcripts, consensus, broker research, expert interviews and the firm's own prior files, and that shape of job is what AllMind AI is engineered around: the team sets the outline, an agent stays on the sources for as long as the primer takes, and every figure opens the passage behind it. If the brief only has to be readable by tomorrow and nothing entitled is involved, Brightwave is the faster buy. AlphaSense Deep Research fits a primer built on its expert-transcript library, Hebbia fits document-heavy private and credit work, and Perplexity or ChatGPT Deep Research give a first pass off the open web, not an audited deliverable.
What sits under the outline decides how deep the draft can go: 6,800+ premium datasets spanning S&P, FactSet, LSEG and MSCI, Expert Insights and entitled broker research, global investor-relations data, earnings and financials landing minutes after a print, alternative data, and sector-specific sets in mining, healthcare and consumer staples, with the firm's own models, memos and warehouse joined to all of it.
Who this is for: analysts who owe a primer on a new name, sector heads building an industry map, and research leads deciding which parts of the deep dive to hand to an agent.
Published August 20, 2026. Last reviewed August 21, 2026. Written by the AllMind AI research team.
Disclosure: AllMind AI builds one of the platforms compared here. Rival tools are credited where their sector output is stronger, and a place on this page cannot be bought.
Key takeaways
- A deep dive has a fixed skeleton, which is what makes it automatable. The sections do not change between names, so an agent can be handed the outline and run it every time.
- Every tool here writes a long brief on demand, and has since 2025. OpenAI announced deep research on February 2, 2025, Perplexity followed on February 14. What separates them now is source reach and whether a figure traces back.
- Automate retrieval and the first draft; keep the view. Segment tables and peer sets have a fixed shape. What the unit economics imply, and what the valuation prices in, do not.
- Length is the wrong test. A long brief repeats the 10-K; a deep one sets filing, consensus and management's claim side by side and names the gap.
- What the agent can read decides how deep the brief goes. On AllMind AI, Expert Insights transcripts ship with the subscription, while broker research and your own models and warehouse depend on what your firm connects, so two clients running one sector template get different depth.
What is the best AI to automate company deep dives in 2026?
AllMind AI is the best AI to automate company deep dives for institutional teams in 2026, because it treats the deep dive as a templated deliverable run over a connected corpus: the team sets the outline, an agent drafts each section from filings, transcripts, consensus, expert interviews, entitled broker research and the firm's own models and memos, and every figure opens the passage it came from. Brightwave is the better pick for a quick thematic brief when entitled content does not matter. AlphaSense Deep Research fits primers that should draw on the Tegus expert library.
The table splits the tools by source reach, outline control and whether the run repeats.
| Tool | Deep dive it fits | Sources it reaches | Outline control | Honest limitation |
|---|---|---|---|---|
| AllMind AI | Company primers and sector maps for institutional equity teams | 6,800+ datasets: SEC and SEDAR filings, transcripts, FactSet, S&P Global, LSEG and MSCI data, Expert Insights and entitled broker research, global IR data, mining, healthcare and consumer staples sets, plus the firm's own models, notes and warehouse | House templates or a custom outline | Not a self-serve seat: that depth starts with connecting your systems, which is a data conversation before it is a login |
| Brightwave | Long-form thematic briefs and single-company deep dives | Public filings, transcripts, news, uploads | Prompt-driven, limited section control | No entitled broker research or expert content; writes the brief, does not maintain coverage |
| AlphaSense Deep Research | Company and industry primers from its library | Broker research, expert transcripts, filings, news | Prompt-driven with briefing formats | A brief inside a search product; house formatting and model updates happen elsewhere |
| Hebbia | Document-heavy diligence on private and credit names | Data-room documents and uploads | Matrix grids, one question per column | Little market data of its own; thinner on public equities |
| Rogo | Banking and PE deliverables (pitch books, memos, CIM analysis) | Filings, market data, firm documents | Banker templates | Deal workflows, not living coverage |
| Perplexity Deep Research | First-pass orientation on a company or industry | Open web | None beyond the prompt | No entitled content, no passage-level lineage, nothing logged |
| ChatGPT Deep Research and Claude | First-pass orientation; drafting against an outline you supply | Open web, plus files you upload or paste | Follows a supplied outline, forgets it next run | No database, no watchlist memory, no entitlements, no reviewable trail |
What does a good company deep dive contain?
A good company deep dive answers seven questions in a fixed order, from what the business sells to what the market is paying for it. Every section above valuation exists to say whether the multiple is earned, and that fixed shape is what makes the deliverable automatable. The AI column below is what a cited-output platform drafts today; the analyst column still needs a person.
| Section | It must answer | What the AI drafts | What the analyst adds |
|---|---|---|---|
| Business | What is sold, to whom, through which channel | Segment table from filings, product and channel description, customer concentration | Which segment is the story |
| Unit economics | What one unit of sale earns after the costs that scale with it | Gross margin by segment, pricing and volume commentary from calls, peer unit metrics | Whether the margin is durable |
| History | How the company got to its current shape | Dated timeline of deals, leadership changes and capital raises, each linked to its filing | Which decisions show how management thinks |
| Management and incentives | Who runs it, and what the comp plan pays for | Officer and board table, proxy comp metrics, insider ownership | Whether incentives match the strategy |
| Competition | Who else sells this, and how the customer chooses | Peer set with disclosed share, win-loss language from transcripts, competitor mentions in filings | The one basis of competition that matters |
| Risks | What breaks the thesis | Risk-factor diffs across years, covenant and concentration language, regulatory exposure | The one risk worth watching |
| Valuation context | What the market is paying and for what | Consensus estimates, multiples against the peer set, history of the multiple | What is priced in, and what the debate is |
The automation workflow for a company primer has six steps, and the order is the point:
- Hand the agent the outline as a template, house format attached.
- Scope the source set: filings, transcripts, the peer set, consensus, entitled broker research, and the firm's prior notes and models.
- Draft section by section, each figure cited to a passage, each derived number showing its arithmetic.
- Reconcile: filing figure against consensus against management's claim, disagreements surfaced.
- Put the analyst on the judgment cells: which segment is the story, whether the margin is durable, what is priced in.
- Save the run. The same template produces the next name and the refreshed primer next quarter.
On AllMind AI the first four steps are one Reports run against an outline set in advance, open for hours without anyone tending it. The memo that follows a primer is covered in our guide to AI investment memos.
What does a good sector deep dive contain?
A good sector deep dive maps the structure before it forms a view, working through six sections that end at one question: where along the value chain the economics accrue. An industry map that never says where the margin sits, and why, is a directory.
| Section | It must answer | What the AI drafts | What the analyst adds |
|---|---|---|---|
| Structure | How the industry is organized: tiers, segments, concentration | Player list with revenue and segment exposure from filings, concentration where disclosed | Where the structure is heading |
| Value chain | Who supplies whom, and what each step adds | Supplier and customer relationships from filings and transcripts, margin by step where disclosed | Which step has pricing power and why |
| Demand drivers | What moves volume and price, on what cycle | Demand commentary across every transcript in the sector, end-market data, seasonality | Which driver dominates the next two years |
| Regulation | Who sets the rules and what is changing | Dated timeline of rules, proposals and enforcement actions, each linked to its source | What a rule change does to the economics |
| Players | Who matters, how share is moving, who is entering | Peer table with share trends, capex and R&D intensity, M&A history | Who wins share, and who supplies the winners |
| Where value accrues | Which step and which players capture the margin | Margin and return comparison across the chain, every figure traced to its filing | The thesis, in a paragraph, with the evidence behind it |
Sector work differs from company work in two ways. The entity count is an order of magnitude larger, so what matters is whether the tool holds the relationships already or rediscovers them per query. And definitions drift between companies (segment names, units, fiscal year ends), so sector reconciliation is mostly about making the comparison fair. The peer-landscape half of this is in AI for competitive landscape and industry analysis.
What are the best AI tools for sector and industry deep dives?
The best AI tools for sector and industry deep dives hold many companies in one structure and read across all of their documents at once. AllMind AI leads for institutional teams: the ontology already carries the relationships, and a run can stay open for hours across an industry's whole document set. AlphaSense is strong when the industry view should come from broker research and expert transcripts, and Brightwave writes a readable thematic brief quickly.
AllMind AI
On AllMind AI a deep dive is a templated Reports run over an ontology holding filings, transcripts, estimates, broker research, expert interviews and the firm's own content. Primers and industry maps get commissioned on it at banks, at hedge funds and inside Fortune 500 corporate teams, and some have folded a separate screener, document tool and note archive into the same workspace while doing it.
Where it wins: the outline is yours before drafting begins, so a primer arrives as an initiation-depth document in your sections and a sector map arrives with the value chain populated. Five things carry the work:
- Breadth stated as classes, not as a count. 6,800+ premium datasets and 750M+ documents: S&P, FactSet, LSEG and MSCI data, Expert Insights transcripts, entitled broker research, global investor-relations data, live earnings within minutes of release, alternative data, and the sector-specific sets a mining, healthcare or consumer staples primer needs before it can say anything a specialist has not read.
- The firm's own material on the same map. Whatever you already expose gets connected: internal APIs, dashboards, the prior primers and models sitting on a drive, and a Snowflake, Databricks or S3 store answered where it stands, under a scoped role. The house view on a name becomes a source the draft reads, not a document someone remembers to attach at the end.
- Relationships held, not rediscovered. The ontology keeps companies, suppliers, customers, estimates, filings and internal research connected as entities, so a section on one name walks out to its supplier's guidance, the estimate revision behind it, the broker note that argued the other way and the firm's last memo. A search index has to go and find those. Here they are already joined, which is the mechanism behind a value-chain section that holds up.
- Work measured in hours, not in turns. A primer or an industry map is a long run: the agent stays on it across hundreds of documents for minutes or hours, then the template reruns next quarter. That is the class of job the platform is bought for, and where a chat window loses the thread.
- A checkable trail. Each figure opens its passage, derived numbers show their arithmetic, figures are re-checked before the report ships, and every query and export is logged. Document Search adds a Deep Dive mode for long reads across many documents.
Where it falls short: this is not a seat one analyst signs up for on a Tuesday afternoon. The depth above comes from connecting your entitlements and your systems, so the first primer follows a scoping conversation with whoever owns the firm's data. And it is a research system, so trading and execution sit outside it.
Brightwave
Brightwave is an AI research agent that writes long-form financial briefs, working over public sources and document sets you give it.
Where it wins: speed to a coherent narrative. A thematic brief on an industry, or a first deep dive on a public company, reads well, carries source attribution and lands fast.
Where it falls short: the source universe stops at public filings, transcripts, news and what you upload, with no entitled broker research or expert content behind it. Brightwave writes the brief and hands it back; keeping it refreshed each quarter across a watchlist, with the firm's models in the loop, is a process you build around it.
AlphaSense Deep Research
AlphaSense Deep Research is the agentic mode inside the AlphaSense platform, added during 2025, which runs many searches across the library and returns primers, briefings and slide-ready output.
Where it wins: the library. Broker research, the 280,000+ expert transcripts that came with the Tegus acquisition (publicly reported at $930M, 2024), filings and news all sit behind the agent, which makes it the strongest pick when a sector deep dive should be built from what the sell side and former operators have said. Citations land at the passage.
Where it falls short: the output is a research brief inside a search product, so house formatting, pushing numbers into a model and keeping the primer alive across quarters happen elsewhere.
Hebbia
Hebbia does document analysis in bulk, with Matrix grids running one question per column across large document sets, with adoption concentrated in private equity, credit and banking.
Where it wins: diligence on a private company or a credit where the material is a data room and the deep dive is a set of structured questions answered across hundreds of documents. The grid is a better shape for that job than a chat thread.
Where it falls short: Hebbia brings little market data of its own, so on a public-equity name, where consensus, multiples and share data carry the argument, the primer thins out. Detail is in AllMind AI vs Hebbia.
Rogo
Rogo is an AI analyst for investment-banking and private-equity deliverables, which announced a $160M Series D led by Kleiner Perkins on April 29, 2026; the roughly $2B valuation attached to that round came from Bloomberg and other press, not from Rogo.
Where it wins: the banker's version of a deep dive: profile pages, comps, CIM analysis and pitch-book sections in bank formats, fast.
Where it falls short: the design center is the deal, and a buy-side sector map that refreshes as filings land is a different product from a pitch book that ships once.
Perplexity, ChatGPT Deep Research and Claude
Perplexity Deep Research (February 14, 2025) and ChatGPT Deep Research (announced by OpenAI on February 2, 2025) run many web searches and return a cited report in minutes; Claude holds a full filing set in context and follows a supplied outline closely.
Where they win: orientation on an unfamiliar industry at consumer pricing, reasoning quality on a document set you supply, and drafting a section against a strict outline.
Where they fall short: the open web plus your uploads is the whole source universe. No broker research, no expert calls, no licensed estimates, nothing from your own drive. Citations point to pages instead of filing passages, there is no watchlist memory, and no lineage a compliance team can review.
How do you tell a deep AI brief from a long one?
A deep brief reconciles; a long brief restates. Pick any paragraph and ask whether a summary of the 10-K could have produced it. If yes, the deep work of setting the filing figure next to consensus and management's claim has not been done. Six tells separate the two:
- Reconciliation. Filing, consensus and management numbers side by side with the gap named, instead of one quoted alone.
- Dated, passage-level sources. Every figure carries a date and opens the document at the line it came from.
- Declared gaps. The brief lists what it could not find. A silent omission is the expensive failure, because it looks like completeness.
- A reason per section. Each section ends with what it means for the thesis, not a recap of its contents.
- Second-source tests. At least one management claim checked against a competitor's filing, a supplier's commentary or an expert's account.
- The house view. The brief knows what the firm already thought about the name, because the prior notes were in the source set.
How many of the six you get is decided by the tool, not the prompt: the second-source and house-view tells depend on whether the system reaches entitled and internal content at all, which no chat window does. Which steps to hand to agents is covered in AI agents for investment research.
Frequently Asked Questions
What is the best AI to automate company deep dives?
For institutional equity teams, AllMind AI wins this one: Reports drafts a company primer in the depth of an initiation note from filings, transcripts, estimates, broker research and the firm's own files, following an outline the team sets, with every figure linked to its source passage. Brightwave fits long-form thematic briefs when entitled content is not required, and AlphaSense Deep Research fits primers that lean on its expert-transcript library. ChatGPT and Perplexity give a usable first pass from the open web but cannot reach broker research, expert calls or internal models.
What are the best AI tools for sector and industry deep dives?
Sector deep dives favor tools that hold many companies in one structure. AllMind AI maps companies, suppliers, customers, estimates and filings in a financial ontology, so a value-chain section can be built across a whole industry with traced figures. AlphaSense is strong when the sector view should come from broker research and expert transcripts, and Brightwave writes readable thematic briefs quickly. General deep-research assistants cover the public framing of an industry and go thin on unit economics and share data.
Can ChatGPT Deep Research or Perplexity write a company deep dive?
They can write a long brief on a public company from the open web in minutes, which is useful for a first orientation on an unfamiliar name. They cannot see broker research, expert calls, licensed estimates or a firm's own models, their citations point to web pages instead of filing passages, and nothing is logged for compliance. Treat the output as a reading list with a narrative attached, then rebuild the numbers in a governed tool.
How do you tell a deep AI brief from a long one?
A deep brief reconciles; a long one restates. Look for the filing figure, the consensus number and management's claim sitting next to each other with the gap named, for numbers that carry a date and a source passage, for a list of what the system could not find, and for a reason each section exists. If any paragraph could have come from summarizing the 10-K alone, the brief is long and the deep work is still ahead of you.
Should you automate the whole deep dive or only part of it?
Automate the parts with a fixed shape: retrieval, financial history, the segment table, the peer set, the regulatory timeline and the first draft of every section. Keep the analyst on the parts that need a view: what the unit economics imply, whether incentives match the strategy, which risk matters most and what the valuation is pricing in. Automation that skips the second group produces briefs that read well and say nothing.
AllMind AI is the AI-native research platform for institutional equity teams. If you want proof on your own work, send us the workflow you want tested.