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Rogo is the most credible finance-native agentic platform we have evaluated in 2026 — its Felix agent runs real investment-banking workflows end-to-end, grounded in the market data and document sources banks already trust. The catch for buyers is opaque, quote-based enterprise pricing and a footprint still centred on deal-side finance.
Our editorial scores are qualitative judgements from the AI Agent Square framework — six dimensions, each justified below. They are not aggregated user ratings, and no vendor can influence them.
Every agent on AI Agent Square is assessed by our editorial team against six dimensions: features and capabilities, pricing transparency, ease of onboarding, support quality, integration breadth, and real-world fit. Because Rogo is a closed, enterprise-only platform with no self-serve tier, this review is built from Rogo's own product, security and news pages, primary funding announcements, and independent market analysis — not from a hands-on trial. Where a claim cannot be independently verified, we say so.
Rogo does not publish a price list. It is sold as an enterprise subscription through multi-year contracts, negotiated per firm. The tiers below describe how Rogo is packaged and bought, not fixed public prices — every number is custom.
| What you're buying | How it's priced | What to confirm in the quote |
|---|---|---|
| Named user seats | Per seat, per year, on a multi-year term | Ramp schedule, seat true-ups, minimum seat count |
| Platform / implementation | One-time and recurring platform fees | Whether pilot spend is credited; onboarding scope |
| Data entitlements | Some feeds require your own third-party licences | Which providers are pass-through vs. bundled |
| Deployment | Multi-tenant vs. single-tenant options | Data residency (US / EU) in writing |
On the actual number: Rogo publishes none. The closest public estimate we found is from market-intelligence firm Sacra, which pegs Rogo at roughly $3,300 per seat per year — useful as a directional anchor, but it is a third-party estimate, not a Rogo-published rate, and real contracts swing with firm size, module selection, and term length. Do not budget off a single figure; scope a tight pilot, instrument the productivity gain, and negotiate the firm-wide rollout with that data in hand.
Rogo is a generative AI platform purpose-built for financial services, with its centre of gravity in investment banking. Where most AI vendors built a horizontal product and then sold it into finance, Rogo went the other way: its reasoning models, workflows, data partnerships, and outputs are all shaped around the work bankers actually do. The company was founded in 2022 by Princeton classmates Gabriel Stengel (CEO), John Willett (COO), and Tumas Rackaitis (CTO), who between them came out of J.P. Morgan and Lazard — a lineage that shows in how closely the product tracks real deal-team workflow rather than generic "chat with your documents" patterns.
The traction is the clearest signal of category fit. At its April 2026 Series D, Rogo reported more than 35,000 financial professionals across 250+ institutions, naming clients including Rothschild & Co, Jefferies, Lazard, Moelis, and Nomura. The $160M round was led by Kleiner Perkins with participation from Sequoia, Thrive Capital, Khosla Ventures, and J.P. Morgan Growth Equity Partners, bringing total funding above $300M at a $2 billion valuation. The company opened a London office in January 2026, led by co-founder John Willett, to push into European banks. That is an unusually strong balance sheet and customer base for a company barely four years old, and it matters for buyers: this is not a startup you have to worry about disappearing mid-contract.
It is worth being precise about what Rogo is and is not. It is not a general-purpose assistant, and it is not a data terminal replacement. It is an agentic layer that sits on top of the data, documents, and productivity tools a bank already runs, automating the analyst-and-associate-heavy grind of assembling, checking, and formatting financial work. The pitch is not "ask the AI anything" — it is "hand the AI the multi-step task you'd give a junior banker, and get back auditable, source-cited output in a fraction of the time."
Felix is Rogo's flagship agent and the reason the platform reads as genuinely agentic rather than a chatbot with finance data bolted on. Per Rogo's own description, Felix "executes complex, multi-step financial processes autonomously, from deal screening and CIM generation to buyer outreach and data room diligence." In practice that means Felix can take a high-level instruction, plan the sub-tasks, pull the right data, run the analysis, and assemble a formatted deliverable — with a banker reviewing and steering rather than doing the assembly by hand.
The distinction between a copilot and an agent is the whole story here. A copilot answers a question or drafts a paragraph. An agent takes a goal — "build me a preliminary target screen for mid-market industrial companies in the DACH region with a sponsor-ownership angle" — decomposes it, and returns a structured longlist with financials, comparable deals, and rationale. Felix is squarely in the second category, and that is what a bank is paying a premium for. Below are the workflows Rogo highlights and that we consider the core of its value.
Felix scans data providers, filings, and news flow for opportunities matching configured screens — sector, geography, revenue band, ownership type, recent triggering events. The output is a banker-quality longlist: structured candidates with strategic rationale, recent financials, comparable transactions, and, where the firm's own systems are connected, relevant prior relationships. This compresses one of the most tedious associate tasks — building and refreshing target lists — from days of manual data-pulling into a reviewable draft.
Felix drafts Confidential Information Memorandums and other deliverables — pitch materials, profiles, summaries — from a structured brief and the underlying deal data. Rogo describes the platform as able to "automatically create presentations, models, and documents." The result is a first draft formatted toward the firm's house style, with comparable-company analysis, historical financials, and a narrative thesis. The banker still reviews and revises, but the agent collapses first-draft time from days to hours, which is where the productivity story is easiest to demonstrate to a skeptical managing director.
Rogo pulls public comps and precedent transactions and calculates trading multiples — EV/EBITDA, EV/Revenue, P/E — with the numbers grounded in real data pulls rather than model arithmetic. This is the single most important reliability distinction from horizontal copilots, which will happily hallucinate a multiple. Because Rogo's outputs are sourced from named finance data providers and filings, the analysis carries citations back to the underlying figures, which is exactly what a banker needs before putting a number in front of a client or an IC.
Felix generates targeted buyer or investor lists and drafts personalised outreach for each contact based on their recent activity and stated strategy, then helps track responses. The deal team owns the actual send and the relationship — the agent does the longlist-to-draft compression that would otherwise eat an analyst's week. This is a lower-stakes, high-volume workflow where automation is easy to trust because a human always reviews before anything goes out.
Rogo indexes the data room and supports what it calls "proprietary document interrogation" — answering banker questions with citations into the source documents and surfacing gaps before they become buyer-side questions. For diligence, this replaces the analyst-hours-spent-reading work that has historically throttled deal velocity, and the citation trail is what makes the answers usable rather than merely plausible.
Two product features do a lot of quiet work. The first is the AI Table interface — a spreadsheet-like surface for organising, sorting, filtering, and refreshing extracted data, so a screen or comp set behaves like a living object rather than a one-shot answer. The second, and arguably the most important for regulated buyers, is transparent, auditable sourcing: Rogo emphasises that every figure traces back to a source. In finance, an answer you cannot audit is an answer you cannot use, and Rogo has clearly built around that constraint rather than treating it as an afterthought.
Rogo's integration footprint is the clearest expression of its finance focus. Its product pages list data partners including S&P Capital IQ, FactSet, LSEG, Dow Jones, PitchBook, Preqin, Quartr, and Daloopa, alongside SEC and international filings, earnings-call transcripts, and real-time web and news. That breadth is the moat: a horizontal copilot can summarise a PDF, but it cannot natively reconcile a company's filed financials against a Capital IQ pull and a broker transcript in one auditable workflow. Rogo can, and that is what makes its comps and screens defensible.
On the model layer, Rogo describes "custom-trained LLMs built for finance, using professionally labeled data." Rather than betting on a single frontier model, Rogo routes across leading models and its own fine-tuned finance models, choosing the right engine for reasoning-heavy versus extraction-heavy tasks — an approach that also insulates the firm from any one model vendor's roadmap. Rogo has been featured as an OpenAI customer scaling finance research and runs on Amazon Bedrock for secure model serving, with additional cloud infrastructure supporting its data pipelines. For a bank's cloud-security team, an existing approved Bedrock path can meaningfully shorten procurement.
Security is where Rogo has to be excellent to sell to banks at all, and its public posture is strong and specific. Rogo's security page lists SOC 2, ISO/IEC 27001, GDPR, and CCPA, and the company has separately announced ISO/IEC 42001 certification — the AI management-system standard — and EU AI Act compliance. It runs a public trust center and has established a security advisory board.
Two data-handling commitments matter most to procurement. First, Rogo states plainly that it "never uses your private data to train or update our models." Second, it stores customer data in "siloed environments, isolated from other customer data," with the largest customers able to take single-tenant deployments. Rogo also describes zero-trust architecture, least-privilege access, end-to-end encryption, continuous monitoring, and third-party audits. None of this removes the need for your own security review — the detailed audit reports are shared under NDA — but as a starting posture it clears the bar most bank InfoSec teams set, and the ISO 42001 and EU AI Act items in particular put Rogo ahead of many finance-AI peers on AI-specific governance. Confirm data residency (US or EU) in writing during procurement rather than assuming it.
Rogo is not a swipe-a-card-and-go product, and buyers should plan accordingly. A realistic rollout runs roughly: a security and legal phase (review, DPA execution, integration discovery); an integration build phase (data entitlements, CRM and document-repository permissions); a pilot phase with one deal team capturing productivity baselines; and then staged expansion to additional groups with compliance and audit review before firm-wide rollout. In practice that is a matter of weeks to a first useful workflow and a few months to broad adoption, depending on how clean the firm's data entitlements and document discipline already are.
The harder surface is people, not technology. Junior bankers worry about displacement, managing directors are skeptical of AI-generated work until they've seen it hold up, IT is wary of new data flows, and compliance wants audit trails. The good news is that Rogo's auditable-sourcing design directly answers the compliance and trust objections, and its white-glove enterprise support is built for exactly this kind of guided rollout. Still, budget for executive sponsorship and an internal champion program — the platforms that fail in banks usually fail on adoption, not capability.
Integrations below are drawn from Rogo's own product pages. Some data feeds may require your firm to hold the underlying third-party licence — confirm which are bundled versus pass-through during procurement.
Coverage and M&A teams use Felix to build and refresh target screens across sector, geography, and ownership filters, returning structured longlists with financials and rationale in place of days of manual data-pulling.
Bankers use Rogo to draft CIMs, profiles, and pitch materials to house style, with comparable-company analysis and financials assembled from live data — compressing first-draft time from days to hours.
Analysts generate trading and transaction comps with multiples grounded in Capital IQ, FactSet, and filings — every number source-cited, which is what makes the output usable in front of a client or committee.
Deal teams point Rogo at the data room to answer diligence questions with citations into source documents and to flag gaps early, replacing hours of analyst reading time and improving deal velocity.
Rogo is the most credible finance-native agentic platform on the market in 2026. Its Felix agent handles real investment-banking workflows end-to-end — deal screening, CIM and material generation, comparable-company analysis, and data-room diligence — grounded in the market data (S&P Capital IQ, FactSet, LSEG, filings) and the auditable sourcing that banks require, and backed by a security posture (SOC 2, ISO 27001, ISO/IEC 42001, EU AI Act) that clears most InfoSec bars. A $2B valuation, $300M+ raised, and named clients from Rothschild & Co to Nomura mean this is a durable vendor, not a bet.
The honest caveats are pricing and scope. Rogo publishes no price list; the most-cited external estimate is roughly $3,300 per seat per year, but the real number is quote-based and firm-specific, and there is no self-serve way to try before you buy. Its strength is deal-side banking — outside that, horizontal copilots or purpose-built tools often win on cost or fit. Our advice for any bank above a modest deal-team headcount: put Rogo on the evaluation shortlist alongside Hebbia, scope a tight pilot, instrument the productivity gain in hours saved, and negotiate the firm-wide rollout with that evidence in hand. On capability, it earns its place. On price transparency, go in with your eyes open.
Rogo has no free tier — evaluation runs through sales. Before you book that call, compare it against the alternatives and pin down the questions that matter for your firm.