AI Tools for Drafting Stock Pitch Decks (2026)
Anwaar Malik
The short answer: for an institutional pitch, AllMind AI covers both halves of the job. The memo, comps and primer are drafted in your own template out of filings, broker research under your entitlements, Expert Insights included in the subscription, and S&P Global, FactSet, LSEG and MSCI estimates beside your own model. Each figure opens at the passage it came from. The deck exports from 20+ investment-bank templates. On a split stack the slide generators matter: PowerPoint Copilot inside Microsoft 365, Gamma for speed, Beautiful.ai for enforced layouts. AlphaSense is the pick when the pitch rests on broker and expert breadth, Fiscal.ai or Koyfin when you are self-serve and paying for it yourself, and ChatGPT or Claude sharpen the narrative. No deck tool knows whether your EBITDA figure is right.
Who this is for: analysts pitching a name to a portfolio manager, associates preparing an idea for the weekly meeting, and student teams in stock pitch competitions.
Published August 20, 2026. Last reviewed August 21, 2026. Written by the AllMind AI research team.
Disclosure: AllMind AI builds one of the research platforms compared here. Where a rival tool builds a better deck, we say which one, and no placement was sold.
Key takeaways
- Most stacks still split research and slides across two products. Research platforms produce the evidence and the memo; slide tools lay it out. AllMind AI is the exception: the deck comes out of the same run.
- Memo first, deck second. A pitch drafted as slides hides weak reasoning behind layout. Write the memo, check it, then cut it into slides.
- Write to the format you are pitching into. Competitions publish a slide cap and a fixed presentation-plus-Q&A split, so outline against the posted rules. Inside a fund there is no cap, but the recommendation, the variant view and the risk slide get read closest.
- Slide tools cost tens of dollars, research costs thousands. Beautiful.ai Team is listed at $40 per user per month on annual billing and Microsoft 365 Copilot at $30 per user per month paid yearly as an add-on (vendor pricing pages, checked August 2026). Institutional research platforms price by quote, so budget the two layers separately.
- Every number on a slide gets asked about. Platforms that cite at the passage level turn the follow-up into a click; tools that do not turn it into an evening of re-checking.
What are the best AI tools for drafting stock pitch decks?
The best AI tools for drafting stock pitch decks split by layer. For research, AllMind AI is the strongest option for institutional teams because it drafts the memo, comps and primer from cited sources in your own template and exports the deck from bank templates. AlphaSense fits when the pitch rests on broker and expert-call breadth; Fiscal.ai and Koyfin are the self-serve picks. For slides on a split stack, PowerPoint Copilot wins inside Microsoft 365 shops, Gamma on speed, Beautiful.ai on enforced design. ChatGPT and Claude sit between the two as narrative editors.
| Tool | Layer | Best for | Pricing signal (as of Aug 2026) | Honest limitation |
|---|---|---|---|---|
| AllMind AI | Research, drafting and deliverables | Institutional teams that want the memo, comps and the deck drafted in house format from entitled content and their own models | Priced on request | Decks land in a supplied bank template, so a bespoke house format takes a formatting pass |
| AlphaSense | Research | Pitches that lean on broker research and expert transcripts | Quote-only | Search and summary; the memo is still written by you |
| Fiscal.ai | Research (self-serve) | Students and lean teams needing fundamentals and segment KPIs fast | Self-serve subscription | No broker research, no internal data, no entitlement model |
| Koyfin | Data and charts | The price, estimate and margin charts on the middle slides | Published plans roughly $468 to $948 per year | Limited AI, no document intelligence |
| ChatGPT / Claude | Narrative | Slide headlines, speaker notes, hostile Q&A rehearsal | Consumer subscriptions | No filings database, no citations you can audit |
| Microsoft PowerPoint Copilot | Slides | Firms already on Microsoft 365 with house templates | $30 per user per month add-on, paid yearly (enterprise listing) | Needs the content written first; dense financial tables come out rough |
| Gamma | Slides | Fast web-first decks and competition first drafts | Self-serve per-user subscription | PowerPoint export is the weak point; layouts drift once edited |
| Beautiful.ai | Slides | Teams that want design rules enforced across presenters | Pro $14.50 per month annual; Team $40 per user per month annual | Smart templates resist custom financial layouts; editable PowerPoint export starts at Team |
What does a stock pitch deck need to prove?
A stock pitch deck has to prove five things, and AI helps with the evidence for each but cannot supply the first. The judge, or the PM, is scoring whether you hold a view the market does not, whether your numbers support it, and whether you know what would make you wrong.
- A variant view. A specific claim about what consensus has mispriced and why the mispricing exists (forced sellers, an accounting quirk, a segment buried inside a larger one).
- Evidence that can be checked. Filings, transcripts, estimates and channel data behind every claim, with the source on the slide.
- A valuation with a range. Base, bull and bear with the assumptions driving each, and a payoff asymmetric in your favor.
- Catalysts with dates. The events that force the market to reprice: an earnings print, a spin, a contract renewal, a regulatory decision.
- Risks you would bet on. The two or three things that break the thesis, sized, with the early indicator for each.
Competition rulebooks ask for the same material under different headings, inside a published slide cap, and buy-side templates want the same blocks in fewer pages. The pre-work for the first proof is covered in our guide to validating an investment thesis with AI.
How do you draft a stock pitch deck with AI, step by step?
The workflow that holds up is research platform first, language model second, the deck last, with the memo as the hand-off. Nine steps, and the order is the point.
| Step | What you do | Tool layer | Check before moving on |
|---|---|---|---|
| 1 | Pick the name and write the one-line claim you intend to defend | Screener or watchlist | Would a PM understand it cold? |
| 2 | Build the evidence file: annual reports, four call transcripts, consensus, sell-side views | Research platform | Every fact has a document behind it |
| 3 | Write the thesis and the anti-thesis, three sentences each | You, with an LLM as sparring partner | A short seller would recognize the anti-thesis |
| 4 | Draft the memo or one-pager | Research platform | No blank cells, no placeholder text |
| 5 | Build the valuation: base, bull, bear with drivers | Your model | Inputs trace back to the evidence file |
| 6 | Write one headline per slide, as a conclusion | LLM | Read only the headlines aloud: is it a pitch? |
| 7 | Generate the slides from the memo | Research platform or slide tool | Charts match the model, not the tool's guess |
| 8 | Footnote every number; re-open the sources behind the valuation drivers | Research platform | A judge could check any figure in under a minute |
| 9 | Rehearse Q&A | LLM as hostile PM, research platform to confirm answers | Each answer has a number and a source |
On the AllMind AI path, step 2 is a chat scoped to one company's filings and transcripts, and the run of cited answers is already an outline: it seeds the memo or primer in your own template at step 4, and the same engine builds the deck at step 7.
On the self-serve path, step 2 is a Fiscal.ai session for fundamentals plus a Koyfin tab for charts, step 4 is written by hand over material you paste in, and step 8 is heavier because the citations were never attached. It works. It takes a weekend instead of an afternoon.
What should each of the 12 slides answer?
Each slide answers one question, the headline states the answer, and the body proves it. The outline below is the artifact: copy it, keep the question column, and change the slide count to fit the format (a PM review often wants 6).
| # | Slide | Question it answers | AI drafts | You decide |
|---|---|---|---|---|
| 1 | Recommendation | Long or short, at what price, for what return, over what horizon? | Nothing useful | Everything |
| 2 | Business in one picture | What does the company sell, to whom, and how does it make money? | Segment table and description from filings | Which segment the thesis lives in |
| 3 | Industry and position | Where does it sit against peers, and where is the industry going? | Share and growth data, peer comps | The one structural point that matters |
| 4 | Why now | What changed recently that the market has not digested? | Timeline of events from transcripts and news | Catalyst or noise |
| 5 | Thesis point one | What is the first thing consensus has wrong? | Supporting numbers with citations | The claim itself |
| 6 | Thesis point two | What is the second? | Supporting numbers with citations | The claim itself |
| 7 | What the market is missing | Why does the mispricing exist, and who is on the other side? | Estimate dispersion, ownership, sell-side ratings | The mechanism |
| 8 | Financials and estimates | What do your numbers look like against consensus and why? | Consensus pull and a variance table | Every variance you own |
| 9 | Valuation | What is it worth in base, bull and bear, and on what? | Comps table, scenario layout | The assumptions |
| 10 | Catalysts | What forces the repricing and when? | Dated calendar of events | Which ones you would bet on |
| 11 | Risks | What breaks the thesis, how big, and what will you watch? | First list from risk factors and call Q&A | The two that keep you up |
| 12 | Appendix | Where did every number come from? | Source list, model snapshots | That it is complete |
Notice where the AI column says nothing. Slides 1, 5, 6 and 7 carry the judgment, and a deck where those four were drafted by a model reads like it. The rest is where AI saves hours: comps tables, dated timelines, consensus pulls, source lists. The one-pager version compresses slides 2 through 11 onto one page.
How do the tools compare for pitch work?
Pitch work splits the field cleanly. Research platforms are judged on sourcing and drafting, slide tools on layout and export fidelity, and the assistants on how well they edit prose you have already checked. The eight below run in that order, research first.
AllMind AI
In pitch work AllMind AI produces the evidence, the memo and the deck.
Where it wins: slides 5 through 8 decide a pitch and are the slowest to build, because each needs a claim that survives a PM opening the source. AllMind AI answers that run of questions with the passage cited, scoped to the sources you pick, across 750M+ documents and 6,800+ commercial datasets: entitled broker research, Expert Insights transcripts, S&P Global, FactSet, LSEG and MSCI data, filings and global IR material, earnings that post within minutes, alternative data, and the mining, healthcare and consumer-staples sets behind most pitched names.
Your own material joins that corpus: the model in your drive, last quarter's pitch on the same name, the dashboard behind the KPI, and warehouse tables in Snowflake, Databricks or S3, queried where they sit through a scoped role. Because the ontology holds peers, suppliers, customers and estimate revisions as relationships, slide 7 can be built from the chain instead of a keyword hunt: the supplier's pricing commentary, the estimate that has not moved since, and the sell-side note still assuming the old cost curve. A build like that runs for hours, sometimes across days, which is the shape of real pitch work at a hedge fund, a bank or a Fortune 500 corporate development team.
When the thread turns into a pitch, Reports drafts the memo, the comps note or a company primer in your committee's template, each figure traceable to its source and re-checked by a verification pass before the draft ships. The deliverable comes out of the same run: a PowerPoint deck on one of 20+ investment-bank templates, a Word memo, or an Excel model with live formulas.
Where it falls short: the deck arrives in a supplied template, so a firm with a bespoke house format still runs a formatting pass. Pricing is by quote, so a student team will be working with the self-serve tools below.
AlphaSense
AlphaSense searches broker research, transcripts, filings and, since the Tegus acquisition, a library publicly reported at 280,000+ expert transcripts.
Where it wins: for a pitch that depends on what the sell side and former employees think, it is the deepest single place to look, with passage-level citations, and the agentic features added since 2025 shorten the summary step.
Where it falls short: the output is search results and summaries. The memo, the comps and the scenarios get built somewhere else, and pricing is quote-only. The split is detailed in AllMind AI vs AlphaSense.
Fiscal.ai
Fiscal.ai is a self-serve fundamentals terminal with an AI copilot, covering 100K+ companies and segment KPIs for roughly the largest 2,300.
Where it wins: a student team or a one-person shop can have clean historical financials, segment data and a copilot over filings within the hour, at a price one person can pay.
Where it falls short: no broker research, no expert content and no internal-data route, so the pitch leans entirely on public documents and step 8 is manual.
Koyfin
Koyfin is a low-cost data terminal with published plans in the roughly $468 to $948 per year range.
Where it wins: the charts on slides 3 through 9. Price and estimate revisions, margin history and peer comparisons export cleanly and look like a deck.
Where it falls short: the AI layer is thin and there is no document intelligence, so it complements a research platform, never replaces one.
ChatGPT and Claude
The general assistants are the narrative layer: they turn a checked memo into headlines, slide text and speaker notes.
Where they win: writing twelve headlines that read as an argument, cutting a 40-word bullet to 12, and playing the hostile PM in rehearsal. Claude can also return a downloadable PowerPoint file from the conversation, which Anthropic's support documentation lists across its plans (checked August 2026), so a competition team can get a rough first deck out of a chat window.
Where they fall short: neither has a filings database, neither cites at the passage level, and both will produce a plausible revenue figure that is wrong. Use them on content you have already sourced. More on this in our guide to AI investment memos.
Gamma
Gamma is a web-first AI presentation generator that builds a deck from a prompt or pasted text in under a minute.
Where it wins: speed and appearance. Paste the memo, get a presentable web deck with sensible visuals in about a minute, and share it as a link. Self-serve pricing is why competition teams reach for it first.
Where it falls short: the deck is web-native first, and the PowerPoint export is the step users complain about most. A layout built for scrolling has to be flattened into slides, so dense comps tables usually need rebuilding on the other side. If the deck has to live in PowerPoint, treat Gamma as a storyboard.
Microsoft PowerPoint Copilot
PowerPoint Copilot is the Microsoft 365 Copilot add-on inside PowerPoint, listed at $30 per user per month paid yearly on top of a qualifying Microsoft 365 license, with a cheaper Business add-on for organizations up to 300 seats (Microsoft's pricing pages, checked August 2026).
Where it wins: it builds slides inside your firm's own template, reads a Word memo as input, and lives where compliance and IT already are. The deck that goes to a PM is almost always a PowerPoint file, so the fit is natural.
Where it falls short: it needs the content written first and is mediocre at financial tables; the comps slide will be rebuilt by hand.
Beautiful.ai
Beautiful.ai is a presentation tool with smart templates that enforce alignment, spacing and hierarchy as you type.
Where it wins: a team of several presenters whose decks otherwise look like they came from different firms. The design constraints are the product. Pro is listed at $14.50 per month on annual billing and Team at $40 per user per month annual, and editable PowerPoint export sits on the Team and Enterprise plans (Beautiful.ai pricing page, checked August 2026).
Where it falls short: the same constraints resist a custom valuation bridge or a three-scenario football field, an individual on Pro cannot hand a colleague an editable .pptx, and the 14-day trial takes a card. It suits the pitch that is mostly narrative, less so the one that is mostly tables.
What gets pitches rejected even with AI?
Pitches fail for the same reasons they failed before AI; the tools have mostly made the failures faster to produce.
- The thesis is a description. Slides 2 through 4 explain what the company does, then slide 5 says it is undervalued. No variant view, no mechanism.
- Numbers without sources. A figure the presenter cannot trace in Q&A. Passage-level citation during research prevents this; a slide tool cannot.
- One DCF, one scenario. A point estimate presented as fact. Judges want the bear case and the assumptions that separate it from the base.
- Catalysts without dates. "Continued execution" is not a catalyst. Name the print, the contract, the decision, and the quarter.
- Generic risks. A list lifted from the 10-K risk factors, not the two things that would change your mind.
- AI filler. Stock imagery, round-number claims, headlines that could be about any company. Reviewers spot it fast.
Frequently Asked Questions
Can AI write a stock pitch deck for me?
AI can draft most of it. AllMind AI runs the research pass and exports the deck itself, from 20+ investment-bank templates. On a split stack, AlphaSense or Fiscal.ai produces the evidence and a slide tool such as PowerPoint Copilot, Gamma or Beautiful.ai lays it out. The thesis, the variant view and the answers in Q&A still have to be yours, because that is what the judge or the PM is testing.
Does AllMind AI generate PowerPoint slides?
Yes. AllMind AI builds PowerPoint decks from 20+ investment-bank templates, Word memos and research notes, and Excel models with live formulas, and it edits existing PPTX, DOCX and XLSX files. The research layer underneath cites every figure to its source passage, so the deck is assembled from checked content. A firm with a bespoke house format still runs a formatting pass.
Can ChatGPT or Claude build a stock pitch deck?
They can write slide headlines, tighten the thesis, draft speaker notes and play a hostile portfolio manager in rehearsal, and Claude can return an editable .pptx file from the conversation. What they cannot do is source the numbers: neither has a filings database or passage-level citations, so every figure they produce has to be checked against the document it supposedly came from before it goes on a slide.
How many slides should a stock pitch deck have?
Twelve is a working default for a full pitch: recommendation, business, industry, why now, two thesis points, what the market is missing, financials, valuation, catalysts, risks and an appendix. Competitions publish their own slide cap and a fixed presentation-plus-Q&A format, so outline against the rules your competition posts. An internal PM review usually wants the six-slide version: recommendation, thesis, what the market is missing, valuation with scenarios, catalysts with dates, and risks.
How do I stop AI from inventing numbers in a pitch deck?
Do the research in a tool that cites at the passage level, keep a footnote on every slide that carries a number, and re-open the source for anything that drives the valuation. Generate the slides from a memo you have already checked instead of letting the slide tool write content from a prompt. Then scan any AI-drafted table for blanks and placeholders before the deck goes out, because a cell left empty looks identical to a cell that was verified.
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.