Rogo vs Hebbia vs Shortcut in 2026: Choosing an AI Analyst Platform for Private Equity

1. Three Different Bets on the Same Analyst

Ask five PE firms which AI analyst platform to buy and you will hear three names: Rogo, Hebbia, and Shortcut. Ask which one wins and you will get five different answers, because the three are running different bets on what an AI analyst should be. Rogo bets on a finance-native platform that works the deal team's whole task list. Hebbia bets the real bottleneck is reading, and built a matrix workspace that answers questions across hundreds of documents in parallel. Shortcut bets the entire game is Excel, and put the analyst inside the spreadsheet.

WorkWise is an AI consulting firm, not a reseller of any of these platforms, so no commission rides on this comparison. It describes each product at the level the public record supports as of mid-2026, flags exactly where the claims need a live test, and ends with the two-week bake-off worth running before anyone signs an annual contract. It is written for the people who actually carry this decision: the partner sponsoring the purchase, the associate who will live inside the tool, and the CFO who signs the invoice.

Two more options belong in the frame before the end, because the right answer for many firms is neither of the three names: horizontal AI configured for finance, and a custom build for the workflows that repeat. Both get their own sections.

2. The Comparison at a Glance

The map first, the detail after.

Read the table as a starting hypothesis, nothing more. Feature sets in this market converge every quarter, which is why the bake-off in section 10 matters more than any grid, including this one.

3. Rogo: The Finance-Native Analyst Platform

Rogo is the finance-native bet: an AI analyst platform built for financial professionals rather than adapted from a general assistant. As of mid-2026 its established ground is the deal team task list. It reads CIMs and management presentations, extracts structured financials, builds comp tables, populates Excel models, drafts memo paragraphs, and pulls from market data sources such as Bloomberg. Several mid-market PE firms use it as their primary deal team tool, and its output moves cleanly into Excel and PowerPoint, which is where deal work actually lives.

The heritage cuts both ways. Rogo grew up around investment banking workflows: pitch decks, comps, deal execution analysis, serving a broad financial audience. The domain knowledge runs deep and the tool speaks analyst. But PE runs workflows that banking does not: thesis-specific screening, portfolio monitoring, LP reporting. Rogo assists a person on a task; it does not run a standing workflow on a schedule. An analyst still opens it, asks, reviews, and moves the work forward.

Pricing is enterprise, so the right cost comparison is against analyst hours saved, never against a chat subscription. For a fuller treatment of where a copilot stops and a workflow system starts, see our Rogo alternative comparison.

4. Hebbia: The Document-Intelligence Matrix

Hebbia made a different bet: the analyst's scarcest hour goes to reading. Its Matrix workspace runs parallel queries across data rooms, CIMs, earnings transcripts, and SEC filings, pulling answers from hundreds of documents at once into a structured grid. The signature move is 50 CIMs by 30 questions, every cell answered, cited, and exportable. Larger PE firms and several investment banks use it, and for research-heavy screening at volume it compresses days of reading into hours.

Three things to weigh. First, scale economics: as of mid-2026 Hebbia carries enterprise pricing and a learning curve, so it tends to earn its keep at heavy document volume, on the order of hundreds of CIMs a quarter, and tends to be more platform than a smaller fund needs. Second, breadth: it serves finance, legal, and government buyers, so the PE-specific judgment lives in the questions your team writes, not in the tool. Third, the division of labor: your analysts formulate the queries and interpret the results. Document intelligence is the product; the model build, the thesis call, and the memo remain your work.

Credit teams weighing it for borrower documents face the same trade in a different wrapper. Our Hebbia alternative comparison goes deeper on the line between a query tool and a workflow system.

5. Shortcut: The AI Analyst Inside Excel

Shortcut is the newest bet of the three: put the AI analyst inside Excel itself. The category logic is easy to state. PE work product ships as a spreadsheet, so an AI that produces real workbooks, with live formulas a person can audit cell by cell, removes the copy-paste step that every chat-window tool leaves behind.

The honest caveat. As of mid-2026, public detail on Shortcut is thinner than on Rogo or Hebbia, and this guide will not pad the gap with claims it cannot ground. Treat the thin record as neither endorsement nor warning; young tools sometimes outrun documented ones. It simply shifts the weight of the decision onto the live test.

What the live test should prove for any Excel-native AI analyst: it builds in your house format rather than a generic template; every formula is inspectable, with no answers hiding in opaque cells; ambiguity surfaces as a flag rather than a silent guess; and the workbook it returns opens, calculates, and audits like one your team built. Hand it a raw trial balance and your LBO template, then check every cell that matters. If a vendor's demo cannot run on your files, that refusal is itself your finding.

6. The Excel Test All Three Face

Buyer searches tell the story: people evaluate Rogo, Hebbia, and Shortcut as AI for Excel, because in private equity the analysis only counts once it lands in a model someone can audit. A brilliant answer in a chat window still has to become a number in a cell with a formula behind it.

As of mid-2026 the three meet Excel differently. Rogo works partly inside and partly outside the spreadsheet, with output that lands cleanly in Excel and PowerPoint. Hebbia's matrix output is structured and exportable, which makes it a feeder of spreadsheets more than a builder of them. Shortcut's entire pitch is the grid itself. So run the same Excel test on all three: your model template, your naming conventions, your error checks, then ask the question every demo avoids. When the AI gets a formula wrong, how does your analyst find it? A number without a traceable formula behind it does not survive an investment committee, and it should not. The Excel test also has a quiet second half: time how long an analyst takes to verify each tool's output, because verification, not generation, is the real cost of AI-assisted modeling.

Excel AI is also a wider market than these three platforms: Microsoft Copilot inside your own tenant, cheap formula add-ins, extraction specialists like Daloopa. The full landscape, tier by tier, is mapped in our AI for Excel buyer's guide.

7. The Fourth Option: Claude and Copilot Configured for Finance

The fourth option rarely appears in vendor bake-offs because no vendor benefits from it: horizontal AI, meaning Claude or Microsoft Copilot, configured for finance work with good instruction sets and a prompt library.

The case is cost and coverage. A configured horizontal model drafts memo sections, summarizes a CIM, stress-tests a thesis, and answers questions against uploaded documents for a fraction of an enterprise platform seat. The security posture is knowable: Copilot runs inside your Microsoft tenant and does not train the foundation models on your content, and Claude's commercial plans (Team, Enterprise, and the API) do not train on your data. Consumer accounts can train on your data unless you opt out, which is why deal documents never belong in one.

The honest limits. A horizontal model is jagged: extraordinary at drafting and synthesis, weaker at producing identical structured output across 50 CIMs, and it arrives knowing nothing about your screening template until you teach it. Teams that invest in configuration close more of that gap than most vendors like to admit.

Our ChatGPT vs Copilot vs Claude comparison covers which horizontal model fits which firm. The practical sequence for many smaller firms: exhaust this layer first, then pay enterprise pricing only for the gap that remains.

8. The Fifth Option: A Custom Build

The fifth option is to stop shopping and automate the workflow. It fits one situation precisely: the work is repeatable, high volume, and specific to your firm. Every inbound CIM screened against your thesis the same way. Every Monday, the same portfolio check. Every quarter, the same reporting assembly. A platform assists those tasks. A custom agent on the Anthropic or OpenAI API simply runs them, in your environment, with your prompts, your templates, and your data path owned by you.

The build is smaller than it sounds and larger than a demo suggests. The AI capability arrives from the model provider; the real work is orchestration, testing, and maintenance, measured in weeks rather than months. When the models update, someone retests. That someone is you, or a partner you trust with it.

Our Custom Build engagements start at $75,000 and are scoped as fixed projects against a named workflow. The decision rule we give clients weighing a build against a Rogo or Hebbia subscription: buy what is common to every firm, build only what is yours. Document reading is common. Your screening criteria and your IC memo format are not common, and they are where the edge lives.

9. Rogo vs Accenture, Deloitte, and PwC Document Chat

One comparison shows up in real buyer queries that no vendor page answers: Rogo versus the AI document chat offered through Accenture, Deloitte, or PwC. It reads like a product comparison, but it is really a choice between two purchasing models.

The platforms sell software: a subscription your own team operates, on your deals, every week, with the learning accruing to your analysts. The large consultancies, as of mid-2026, generally bring AI document analysis inside a services engagement: their people operate the tooling, the deliverable is the product, and when the engagement ends, much of the capability leaves with the team that ran it. Data control follows the same split: on a platform, your firm sets and audits the controls; inside an engagement, your documents move under the consultancy's arrangements, which your counsel should read before kickoff.

When services logic wins. A mega-deal where a brand-name workstream matters to LPs or co-investors, a one-off surge beyond your bench, or a diligence scope wider than any software covers. When platform logic wins. A standing capability you want on every deal, priced per seat rather than per engagement, run by people who stay.

Three questions settle it: who operates the AI day to day, where does your data sit and under whose controls, and what remains at your firm after the invoice. A firm that wants the capability should buy or build software. A firm that wants a defended deliverable for one process can rent the consultancy, eyes open.

10. How to Run the Evaluation: A Two-Week Bake-Off

Every platform in this guide demos beautifully, and demos are the worst available evidence, because generative AI is jagged: extraordinary on some tasks and weak on adjacent ones, in ways a scripted demo is built to hide. The Harvard field study quoted below found that the gap between people who get little from AI and people who get a lot comes down to knowing where to apply it. A financial software company evaluation, run properly, is how a firm buys that knowledge before the contract instead of after.

Week one, your work. Same inputs to every finalist: three CIMs from last quarter including your messiest one, one recent data room, and your actual model template. Same prompts for each tool. Outputs scored blind against the analyst-built baseline, because the review that matters is your own private equity analysts' review, on your documents, not a reference call. Put the associates on the scoring panel alongside a partner, since they will live in the tool daily, and track time-to-first-useful-output for each platform as its own metric.

Week two, the audit. Security first: does the vendor train on your inputs, what is retained and for how long, where is the data processed, who are the sub-processors, and is there SOC 2. Then seat economics: quoted price times the seats that will genuinely use it, against hours saved at loaded cost, on your real deal count, over three years.

The decision rule at the end: buy the platform that won on your documents, at your volume, past your security bar. If none clears all three, the answer this cycle is horizontal AI plus discipline, and the market will look different next year. The full scoring framework, with weightings, is in our AI vendor evaluation guide.

11. Where to Start

Start with the job, because the platforms are answers to different questions. If your team drowns in reading, the document-matrix bet fits. If the work product is models and memos, the finance-native and Excel-native bets fit. If the same workflow repeats every week, stop buying assistance and automate the workflow instead.

Deciding which question your firm is actually asking is the useful work, and it is exactly what an AI Readiness Sprint does: $12,500 flat for firms up to 20 people, a baseline of where your hours actually go, and a shortlist of the two or three tools worth testing at your size, so the bake-off runs on fit rather than on marketing. Firms of 20 or more take the $30,000 Comprehensive version.

And when the platform question surfaced mid-deal, because a target's data room is drowning the team right now, our AI Diligence engagements put the machinery to work immediately: $15,000 for a screen, $25,000 for a comprehensive review. Either way, make the vendors compete on your documents. The demo is theirs. The decision should be yours.