Try Rogo
Overview
Rogo understood something most vertical AI products miss: in investment banking, the format is the deliverable. An answer in a chat window is worthless to an associate who needs a model in the firm's Excel template, a deck in the firm's PowerPoint style, and a memo in the firm's Word format. Rogo produces those directly, which is why it reached 35,000 finance professionals across more than 250 firms and a $2 billion valuation.
The workflows are calibrated to investment banking conventions rather than generalised. Comparable company analysis, precedent transactions, market research, document review across a data room — the actual task list of a deal team, executed the way that team expects. An April 2026 partnership with Daloopa strengthened the underlying financial data, which matters because in finance a confidently wrong number is worse than no answer at all.
The obvious question is what happens to junior analyst work, and the honest answer is that a meaningful portion of it — the formatting, the comp table assembly, the first-pass document review — is exactly what this automates. Firms adopting it are not eliminating analysts so much as changing what the first two years look like. Whether that produces better senior bankers or hollows out the apprenticeship is a genuinely open question.
Key Features
Firm-Template Native Output
Excel models, PowerPoint decks and Word memos produced in your firm's own templates, which is what makes output usable rather than a starting point.
Comparable and Precedent Analysis
Builds comp tables and precedent transaction analyses following investment banking convention rather than generic financial summarisation.
Verified Financial Data
Underpinned by financial data partnerships, including Daloopa from April 2026, because accuracy is non-negotiable in this domain.
Data Room Document Review
Works across large document sets in diligence, which is where the most punishing hours go.
Firm-Grade Security
Built for an industry where a data leak is an existential rather than reputational event.
Deal Team Workflows
Structured around how deal teams actually work rather than around a general chat interface.
Pros & Cons
Advantages
- Output arrives in usable firm formats, not as chat text to reformat
- Workflows follow banking convention rather than generic finance
- Very strong adoption — 35,000+ professionals at 250+ firms
- Financial data partnerships address the accuracy problem directly
- $2B valuation reflects real revenue rather than promise
Disadvantages
- Extremely narrow — useless outside financial services
- Enterprise pricing aimed at firms, not individuals
- Output still requires expert review before it reaches a client
- Raises real questions about how junior analysts learn the trade
Pricing Plans
| Plan | Price | Key Features |
|---|---|---|
| Enterprise | Custom | Firm-level licensing priced on seats and deployment scope |
Best Use Cases
Rogo Excels At:
- Investment banking deal teams under time pressure
- Private equity diligence across large document sets
- Comparable company and precedent transaction analysis
- Any finance workflow where template compliance is mandatory
May Not Be Ideal For:
- Anyone outside financial services
- Individual analysts without firm licensing
- Work where the reasoning, not the assembly, is the hard part
How It Compares
Rogo vs Hebbia
Hebbia bets the bottleneck is reading — its Matrix product answers questions across hundreds of documents in parallel. Rogo bets it is the whole task list and produces finished deliverables. Diligence-heavy work leans Hebbia; deal execution leans Rogo.
Rogo vs a general AI assistant
A general assistant can discuss a DCF; it cannot produce one in your firm's template with your firm's conventions. In an industry where the format is the deliverable, that gap is the entire product.
Final Verdict
Our Recommendation
Rogo is the clearest example of vertical AI done properly. It succeeded by recognising that the last mile in finance is formatting and convention, not reasoning, and by building for that rather than around it — which is why 250 firms adopted it while general assistants sat unused. Output still needs expert review before it reaches a client, and it is useless outside financial services. Firms should also think seriously about what happens to the apprenticeship model when the work juniors learned from is the work being automated.