AI research assistants for strategy and finance: buyer guide (October 2026)

The short answer

Pick by where the evidence lives.

If the evidence your team needs is licensed content, such as broker research, expert transcripts and filings, buy the AI from the company that licenses it: AlphaSense is the example. If the evidence is your own deal files, data rooms and models, plus data you already license from FactSet, S&P or PitchBook, buy a finance AI workspace such as Hebbia or Rogo. If you want one assistant for the whole firm and can live with fewer finance-specific workflows, buy a general assistant with finance data connections: Claude, ChatGPT and Perplexity all sell enterprise versions.

In every case, test citations before features: click each figure through to its source.

Why ai research assistants for strategy and finance matter

Strategy, corporate development and investment teams spend much of their time reading: filings, earnings calls, broker notes, expert transcripts, data room documents, and their own past memos. The decisions that follow are large: which company to buy, which market to enter, which thesis to defend to an investment committee.

AI research assistants read and summarize that material, build comparison tables across hundreds of documents, and draft memos, models and slides. Several now run multi-step tasks as agents and keep monitoring a thesis after the first answer.

The risk is a confident answer built on the wrong document, an out-of-date figure or content the firm is not licensed to use. The category matters because the choice of assistant decides which evidence your team sees, and whether each claim can be checked.

Who buys it

Buyers include investment banks, private equity firms, hedge funds and asset managers. Corporate strategy, corporate development, investor relations and FP&A teams at large companies also buy, sometimes extending a research platform or enterprise AI license they already hold. Triggers include a firm-wide AI program, analyst capacity limits during deal or earnings season, and a review of overlapping research and data subscriptions.

Before you buy: what teams do without one

Without a dedicated assistant, teams use analysts, the search built into their data terminals and research platforms, and a general chatbot that cannot see licensed content or internal files.

That works for occasional questions on public information. A dedicated product pays when analysts repeat the same tasks across many documents, such as comparing credit agreements, summarizing earnings calls or building comps, or when answers must draw on licensed data and internal files with citations a reviewer can check. The decision is less buy or build than which of your existing vendors' AI to standardize on.

What good looks like

Capability	What good looks like	Why it matters
Cited answers	Each figure and claim links to the page, cell or passage it came from, and the link opens the source.	Investment committees and boards ask where a number came from.
Licensed data access	Built-in or connected access to the filings, transcripts, broker research and market data your team already uses.	An assistant can only cite what it can read.
Internal documents	Connections to SharePoint, data rooms and drives that respect existing permissions.	Deal and strategy work depends on files that are not public.
Work across many documents	The same questions run across dozens or hundreds of documents, returned as a table you can audit.	Diligence and comps are tabular work, not single questions.
Office output	Models built or updated in Excel and slides drafted in PowerPoint, with formulas and sources intact.	The work product is a model, a memo or a deck.
Agents and monitoring	Multi-step tasks run on a schedule or trigger, with each step and source visible for review.	A thesis needs to be rechecked when new filings or news arrive.
Security and governance	SSO, audit logs, data retention controls and a contractual commitment not to train on your data.	Deal files and client data are confidential and often regulated.

How ai research assistants for strategy and finance are built, and who sells each kind

Vendors in this category can take a labeled Spotlight slot on this guide. How Spotlight works

Research platforms with AI agents

A research platform that licenses its own content, such as broker research, expert transcripts and filings, and adds AI search and agents on top. AlphaSense works this way.

Fits: Teams whose answers depend on licensed research content they do not already hold.

AlphaSenseStrategy and investment teams that want AI agents over broker research, filings and expert transcripts.

Finance AI workspaces

AI products built for banking and investing workflows that connect to your documents and to data you license from vendors such as FactSet, S&P Capital IQ and PitchBook. Hebbia, Rogo and Samaya work this way.

Fits: Deal and investment teams that want finance-specific workflows over their own files and data.

HebbiaInvestors, bankers and lawyers who run the same questions across thousands of deal and market documents.
RogoBanks and investment firms that want finance agents working across their systems, data and models.
Samaya AIBuy-side and sell-side investment teams combining internal research with filings, transcripts and licensed data.

General AI assistants with finance data

Enterprise versions of general AI assistants, with financial data connectors or included datasets and Office add-ins. Claude, ChatGPT and Perplexity work this way.

Fits: Firms that want one assistant for every team, with finance data added.

Claude for Financial Services (Anthropic)Financial firms that want a general assistant with finance data connectors and Excel and PowerPoint add-ins.
ChatGPT for Financial Services (OpenAI)Banking and equity research teams that want a general assistant with financial datasets included.
Perplexity EnterpriseTeams that want cited web, file and app search for every employee on a published per-seat plan.

How to choose

Whose content it can cite

Licensed by the vendor, licensed by you and connected, or open web only. Ask for the list of sources each answer can draw on in your plan.

Finance-specific workflows

Comps, precedent transactions, credit memos, earnings reviews and pitch materials as built-in workflows, or prompts you write yourself.

Scale of document work

Whether the product runs one question at a time or the same questions across hundreds of documents in a reviewable table.

Model choice

Some vendors route work across several AI models; others use their own. Ask which models are used and who decides when they change.

Deployment and controls

Single sign-on, permissions inherited from source systems, audit logs, retention settings and where data is processed.

Fit with existing licenses

Whether the assistant uses data subscriptions you already pay for, includes its own, or requires new contracts with data vendors.

How pricing works

Pricing is published only at the self-serve end. Perplexity lists per-seat enterprise plans, billed annually, with usage credits for heavier agent work and custom pricing for large deployments. The finance editions of ChatGPT and Claude, the specialist finance workspaces and the research platforms in this guide sell through sales teams without published prices.

The larger cost question is data. Some products include financial datasets in the license. Others connect to data you license separately, so the assistant's price sits on top of your existing terminal, research and data subscriptions.

Before comparing quotes, list seats by role, the data sources each team needs, and expected agent usage.

Model	How it works	Watch for
Published per-seat plan	A listed monthly price per seat, billed annually, with higher tiers adding usage and security features.	Usage credits that run out, and which security controls sit only in custom-priced tiers.
Enterprise contract after a demo	An annual license priced by seats, teams or firm-wide deployment, negotiated with sales.	Price steps when more teams join, and minimum seat counts.
Usage credits	Agent runs, large document jobs or compute charged against a monthly credit allowance.	How credits are consumed by long agent tasks, and the cost of top-ups.
Data included or connected	Financial datasets bundled in the license, or connectors to data you license from other vendors.	Whether a connector requires a separate data contract, and any extra fees for AI use of that data.

Implementation

Pick 5 real tasksChoose recurring tasks, such as an earnings review, a comps table or a diligence question list, and score every vendor on the same ones.
Clear data rightsConfirm with each data vendor and legal which licensed content may be used by an AI assistant, and where it may be processed.
Connect sources with permissionsLink document stores and data feeds using single sign-on so users only see what they already have rights to.
Set review rulesDecide which outputs need a human check of every citation before they reach a client, committee or board.
Measure before renewalTrack active users, tasks completed and hours saved by team, and cut seats that are not used.

Confirm before you sign

The list of data sources the assistant can cite in your plan, licensed and connected.
Written confirmation from each data vendor that AI use of its data is permitted.
Citations that open the exact passage, page or cell.
Permissions inherited from SharePoint, data rooms and drives.
A contractual commitment not to train models on your data.
SSO, SCIM provisioning, audit logs and retention controls in the tier you buy.
Where data is processed and stored, and which AI models are used.
Excel and PowerPoint output that keeps formulas and sources.
Usage limits, credit consumption and overage pricing.
Export of prompts, outputs and agent histories if you leave.
Price for adding teams or seats at renewal, fixed in the contract.

Questions to ask in a demo

Run our earnings review task on 3 companies we cover. Click every figure through to its source.
Which sources in that answer come from your licenses, which from ours, and which from the open web?
Run the same 10 questions across 50 documents from one of our past deals. How long does it take, and can we audit each cell?
What happens when the source documents disagree, or when the answer is not in them?
Build a comps table in Excel. Do the formulas and source links survive when we edit it?
Which AI models does the product use, and how do you tell us when they change?
How do you enforce our existing document permissions, and what do your audit logs record?
What does a heavy user's month cost in credits or overages?

Frequently asked questions

What is an AI research assistant for finance?

Software that reads financial and business documents, such as filings, earnings transcripts, broker research, deal files and internal memos, then answers questions with citations, builds comparison tables and drafts models, memos and slides. Products range from research platforms with their own licensed content, to finance-specific AI workspaces over your files, to enterprise versions of general assistants with finance data connections.

Finance AI workspaces vs research platforms: what is the difference?

A research platform sells its own licensed content, such as broker research and expert transcripts, with AI search and agents on top. A finance AI workspace sells the AI layer and connects it to your documents and to data you license elsewhere, such as FactSet, S&P Capital IQ or PitchBook. AlphaSense is an example of the first; Hebbia and Rogo of the second. Decide whether your gap is content or workflow.

Can ChatGPT or Claude replace AlphaSense?

Not for content they cannot read. Research platforms such as AlphaSense license broker research and expert transcripts. General assistants are adding finance data: ChatGPT for Financial Services, launched in September 2026, includes datasets from Daloopa, PitchBook, LSEG News and Crunchbase, and Claude connects to sources including LSEG, FactSet, S&P Global and Morningstar. Compare the source lists, not the chat interfaces.

How much do AI research assistants for finance cost?

Perplexity publishes per-seat enterprise plans billed annually, with credits for heavier agent use. The finance editions of ChatGPT and Claude, specialist finance workspaces and research platforms do not publish prices and sell through their sales teams. Data can be the larger cost: check whether financial datasets are included or must be licensed separately.

Are AI research assistants safe for confidential deal work?

Enterprise plans from the vendors in this guide offer controls such as single sign-on and admin permissions, and several state that they do not train models on enterprise customer data. Confirm in the contract where data is processed and stored, who at the vendor can access it, how long it is retained, and that document permissions from your own systems carry over.

Sources

Written by Stratrix editors from public sources and checked claim by claim by a second AI model against those sources. Vendors named in the editorial sections did not pay to be included. Spotlight listings are paid and labeled. Methodology and corrections

AI research assistants for strategy and finance: 3 approaches