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Verdict: 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 generation, comparable company analysis, data-room diligence — with the data integrations (FactSet, Cap IQ) and security posture banks require. If you are an investment bank above 50 deal-team headcount, evaluating Rogo against AlphaSense and Hebbia is a near-mandatory exercise. Outside investment banking, the vertical fit is narrower and the per-seat price is hard to justify against horizontal copilots.
Best-in-category for IB workflows
Felix agent + finance integrations
Enterprise-only, opaque
Natural language, finance-fluent
White-glove for enterprise
FactSet, Cap IQ, CRM, SharePoint native
Evaluating finance AI platforms? Request a Rogo demo or compare alternatives.
Request a demo Compare alternatives Pricing guideWhat is Rogo AI?
Rogo is a generative AI platform purpose-built for financial services, especially investment banking. Where most AI vendors built horizontal products and then sold them into finance, Rogo built finance-specific from day one — its models, workflows, integrations, and data partnerships are all tuned to the work investment bankers actually do.
The platform's flagship agent, Felix, is designed to handle complex multi-step financial tasks without human intervention — spanning deal screening, CIM generation, buyer outreach, and data-room diligence. Beyond Felix, Rogo provides finance-trained search, comparable-company analysis, sector benchmarking, and structured-output document generation that drops into Excel, Word, and PowerPoint with the formatting bankers actually use.
The traction tells the story of category fit. By Series D in April 2026, Rogo served 35,000+ bankers at 250 firms including Truist Securities, Nomura, and Baird, with $300M+ in total capital raised and a $2 billion post-money valuation led by Kleiner Perkins. The company opened a London office in January 2026 led by co-founder John Willett to expand into European banks.
Pricing and packaging
Rogo does not publish a price list. It is sold via enterprise-grade subscriptions through multi-year contracts directly to financial institutions. Multiple market reports place per-seat pricing in the same band as other enterprise finance AI tools — several thousand dollars per seat per year — with platform fees and one-time implementation costs on top. Negotiated price varies substantially with firm size, modules selected, and contract length.
| Tier | What's included | Pricing | Best for |
|---|---|---|---|
| Pilot | Limited seats, single deal team, time-boxed evaluation | Custom; often credited against subsequent contract | Boutique advisory firms, evaluation |
| Team | Single sector or product group, core Felix workflows, FactSet/Cap IQ data | Custom enterprise pricing | Sector coverage groups, M&A teams |
| Firm-wide | All bankers, all modules, dedicated CSM, custom integrations | Multi-million-dollar annual contracts at scale | Bulge-bracket and large mid-market banks |
The opaque pricing model is the single biggest friction in evaluating Rogo. For procurement teams, the practical advice: scope the pilot tightly to one sector team, instrument the productivity metrics carefully, and negotiate the firm-wide rollover with that data in hand. Rogo's negotiating posture historically has been firm on per-seat price but flexible on platform fees and implementation credits.
What Rogo and Felix actually do
Deal screening
Felix scans deal databases, news flow, and Cap IQ/FactSet for opportunities matching configured screens (sector, geography, revenue band, ownership type, recent events). Banker-quality output: structured longlist with strategic rationale, recent financials, comparable deals, and relevant prior banker relationships pulled from the firm's CRM.
CIM generation
Felix drafts Confidential Information Memorandums from a structured prompt and the underlying deal data. The result is a CIM draft formatted to the firm's house template, with comparable-company analysis, historical financials, and an investment thesis. The banker reviews and revises — the agent compresses the first-draft time from days to hours.
Comparable-company analysis
Pulls public comps and recent precedent transactions, calculates trading multiples (EV/EBITDA, EV/Revenue, P/E), and produces the structured output with cited sources. Math is grounded in Cap IQ / FactSet pulls, not LLM arithmetic — a critical distinction from horizontal copilots that hallucinate multiples.
Buyer outreach
Generates targeted buyer lists, drafts personalised outreach for each contact based on the buyer's recent investment history and stated thesis, and tracks responses. The deal team owns the actual send; the agent does the longlist-to-draft compression.
Data-room diligence
Indexes the data room (SharePoint, iManage, or vendor VDR), answers banker questions with citations into the source documents, and flags structural gaps in the data room before they become buyer-side questions. Replaces the analyst-hours-spent-reading work that has historically blocked deal velocity.
Pitch and CIM polish
Beyond first-draft generation, Felix offers structural editing on existing decks and CIMs — checking consistency across slides, validating financial figures against source data, and applying house-style formatting. This is where bankers spend a surprising amount of time and where the productivity gains are easiest to demonstrate.
Data integrations and infrastructure
Rogo's integration footprint reflects its finance focus. The firm runs deal pipelines on Google Cloud and uses Amazon Bedrock for secure model serving. Custom-trained LLMs are built for finance using professionally labeled data tailored to investment-banking precision standards. The combination of OpenAI o-series reasoning models (via direct partnership) and Rogo's own fine-tuned models delivers the accuracy bankers will accept.
| Integration | Status | Use case |
|---|---|---|
| S&P Capital IQ | Native | Comparable companies, precedent transactions, financials |
| FactSet | Native | Real-time market data, ownership, transactions |
| Salesforce / Microsoft Dynamics | Native | Deal CRM, prior relationships, contact intelligence |
| SharePoint / iManage | Native | Document repository for data-room diligence |
| Microsoft 365 (Word, Excel, PowerPoint) | Native | Output to bank-standard document formats |
| Snowflake / Databricks | Available | Custom firm data integration |
| OpenAI o1 / o3 reasoning models | Direct partnership | Complex multi-step reasoning |
Pros and cons
Strengths
- Finance-native product, not a horizontal copilot retrofitted
- Felix agent genuinely handles end-to-end IB workflows
- Deep Cap IQ / FactSet integration grounds numbers in real data
- Strong customer roster gives reference checks
- Bank-grade security and infrastructure on AWS Bedrock
- Recent $160M raise funds aggressive product and partnership investment
- UK / EU expansion via London office accelerates European procurement
Limitations
- Opaque pricing; no self-serve evaluation
- Enterprise-only — not viable for sub-50 banker boutiques on the cheap
- Narrowly focused on IB; limited fit for asset management, hedge funds, lending
- Newer entrant relative to FactSet/Bloomberg incumbents on workflow trust
- Implementation lift is non-trivial — plan 4-8 weeks to first useful flow
- Compliance and audit-trail tooling is still maturing for some regulators
Who Rogo is for — and who should look elsewhere
Strong fit: Investment banks above 50 deal-team headcount, M&A advisory firms, sector coverage teams, equity capital markets desks, and large boutiques with FactSet/Cap IQ subscriptions and SharePoint/iManage document discipline.
Weak fit: Hedge funds and asset managers (different workflow shape — look at Hebbia or AlphaSense), private equity firms outside their banker workflows (consider Cassidy or general-purpose copilots), lending teams (different data needs), and very small boutiques where the implementation cost outweighs the productivity gain.
Alternatives to evaluate
Hebbia. Generalist agentic research platform with strong finance traction. More flexible across asset classes; less IB-workflow-specific than Rogo.
AlphaSense Generative Search. Document research powerhouse; strong for company research and competitive intelligence. Less workflow-orchestration than Rogo's Felix.
FactSet Mercury AI. Native to FactSet, growing fast. Compelling if your firm is already a heavy FactSet shop and wants vendor consolidation.
Numeric AI. Finance close and accounting automation. Different category — see our Numeric AI review for accounting-team buyers.
ChatGPT Enterprise / Microsoft Copilot. Horizontal copilots that work across the firm. Cheaper per seat; less IB-workflow-specific. Often deployed alongside Rogo rather than instead of.
Implementation and change management
Rogo's implementation is more involved than a typical SaaS deployment. Expect:
- Weeks 1-2: Security review, BAA / DPA execution, integration discovery with the firm's data and document stack.
- Weeks 3-4: Integration build — Cap IQ / FactSet entitlements, CRM connections, document-repository ingestion permissions.
- Weeks 5-8: Pilot deal team onboarding, first Felix workflows shipped, productivity baselines captured.
- Weeks 9-12: Expansion to additional sector teams, governance and audit-trail review with compliance, success-metric review.
- Months 4-6: Firm-wide rollout planning, training program for analysts and associates, integration with M&A pipeline reporting.
The change-management surface is significant: junior bankers worried about role displacement, managing directors skeptical of AI-generated work, IT skeptical of new data flows, compliance demanding audit trails. Plan a structured rollout with executive sponsorship and an internal champion program.
Security, compliance, and data handling
Rogo runs on AWS infrastructure with Amazon Bedrock for model serving. The company sells exclusively to financial institutions and has been built around the security and compliance requirements those buyers impose: SOC 2 Type II, ISO 27001, GDPR DPA, single-tenant deployment options for largest customers. ZDR and BYO-key options are available at enterprise tiers under negotiation.
For US bank buyers, the integration with Amazon Bedrock is itself a procurement asset — many banks already have approved Bedrock paths through their cloud-security program. For EU buyers, EU-resident deployment is available; verify residency in writing during procurement.
User reviews and reference posture
Rogo customers in public reference (Truist Securities, Nomura, Baird) have been positive on specific dimensions — speed of CIM generation, accuracy of comparable-company analysis grounded in Cap IQ/FactSet data, and the Felix agent's ability to handle multi-step workflows without constant supervision. The most common critique surfaces around the implementation lift and the absence of a self-serve tier for evaluation.
Reference-call protocol: ask for a banker in a similar sector and deal-size band to your firm's. Discuss specific workflows (CIM generation, comp analysis, data-room) rather than general satisfaction. Get specifics on productivity uplift in hours and confidence in output accuracy.
Frequently asked questions
What is Rogo AI?
Rogo is a generative AI platform purpose-built for financial services, especially investment banking. Its agent Felix handles complex multi-step financial workflows including deal screening, CIM generation, buyer outreach, and data-room diligence. Rogo serves 35,000+ bankers at 250 firms including Truist Securities, Nomura, and Baird, and was valued at $2 billion after its April 2026 Series D.
How much does Rogo AI cost?
Rogo is sold via enterprise-grade subscriptions through multi-year contracts directly to financial institutions. Pricing is not publicly listed; market reports place per-seat pricing in the same band as other enterprise finance AI tools — typically several thousand dollars per seat per year, with platform fees and implementation costs on top. Contact sales for a quote tailored to firm size and modules.
What data sources does Rogo integrate with?
Rogo is deeply integrated with finance-specific data providers including FactSet and S&P Capital IQ, plus CRMs like Salesforce and Microsoft Dynamics, document repositories like SharePoint and iManage, and Microsoft 365/Excel for output. The product is designed to live inside an investment bank's existing data and document stack rather than displace it.
Is Rogo AI secure for banking use?
Rogo runs on AWS infrastructure using Amazon Bedrock for model serving and is built around financial-services security and compliance requirements. The company sells exclusively to financial institutions through multi-year contracts that include compliance certifications and bank-grade security commitments. Specific certifications and audit reports are available under NDA from Rogo's sales team.
Who are Rogo's main competitors?
In the finance-AI category, Rogo competes most directly with Hebbia (general agentic research), AlphaSense Generative Search, FactSet's Mercury AI, and verticalised competitors like Numeric (accounting close) and Data Snipper (audit). General-purpose copilots (Microsoft Copilot, ChatGPT Enterprise) compete on horizontal capabilities but lack the finance-specific data integrations and workflows.
Sources & further reading
- Rogo official site — rogo.ai/product
- Rogo $160M Series D announcement — prnewswire.com
- FinTech Global — Rogo Series D coverage — fintech.global
- OpenAI customer story — Rogo scales with o1 — openai.com/index/rogo
- AWS Startups — Rogo on Bedrock — aws.amazon.com
- Google Cloud customer story — Rogo — cloud.google.com/customers/rogo